oEPA
United States
Environmental Protection
Agency
EPA600/R-20/274
August, 2020
Development of a Cumulative Resilience
Screening Index (CRSI) for Natural
Hazards: An Assessment of Resilience to
Acute Meteorological Events and Selected
Natural Hazards
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EPA600/R-20/274
August, 2020
Development of a Cumulative Resilience
Screening Index (CRSI) for Natural
Hazards: An Assessment of Resilience to
Acute Meteorological Events and Selected
Natural Hazards
by
J. Kevin Summers, Linda C. Harwell, Kyle D. Buck, Lisa M. Smith
Deborah N. Vivian, Justin J. Bousquin, James E. Harvey, Stephen F.
Hafner, Michelle D. McLaughlin and Courtney A. McMillion
U.S. Environmental Protection Agency
Office of Research and Development
Center for Environmental Measurement and Modeling (CEMM)
Gulf Ecosystem Measurement and Modeling Division (GEMMD)
1 Sabine Island Drive, Gulf Breeze, FL 32561
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Notice/Disclaimer Statement
This revised document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. The information in this article has been funded
wholly (or in part) by the U.S. Environmental Protection Agency. It has been subjected to
review by the Center for Environmental Measurements and Models and approved for
publication. Approval does not signify that the contents reflect the views of the Agency, nor
does mention of trade names or commercial products constitute endorsement or
recommendation for use. All images and copyrights are the property of the U.S. Environmental
Protection Agency.
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Table of Contents
Notice/Disclaimer Statement 2
Table of Figures 5
Table of Tables 10
Acronyms and Abbreviations 11
Acknowledgments 12
Highlights of Results 17
1. Introduction and Background 21
2. Approach 24
2.1. Overview of Indicator/Indices Development 24
2.2. A Review of Existing Resilience Indicators and Indices 26
2.3. Determination of Natural Hazard Event Factors to be Included in CRSI 29
2.4. The CRSI Conceptual Framework 32
2.4.1. Risk Domain 35
2.4.2. Governance Domain 37
2.4.3. Society Domain 38
2.4.4. Built Environment Domain 43
2.4.5. Natural Environment Domain 45
2.5. Metric Selection and Data Sources 47
2.6. Data Handling and Standardization 47
2.7. Calculations 50
2.7.1. Built Environment, Governance, Natural Environment and Society Domains 50
2.7.2. Risk Domain 51
2.8. The Final Steps to CRSI 52
3. How to Use CRSI - Its Utility and Potential Applications 56
3.1. Introduction 56
3.2. General Broad Use 56
3.3. Use by EPA Regions 57
3.4. Use by EPA Program Offices 58
3.5. Use by States, Counties, Metropolitan Areas and Communities 59
3.6. Examples 60
4. Results and Discussion - National and EPA Regions 63
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4.1. Organization of Results 63
4.2. General Broad Analyses and Results of Basic Resilience (Governance/Risk) 64
4.3 Presentation of Results 69
4.3.1. CRSI and Domain Score Bar Graphs 70
4.3.2. Six Panel Maps 71
4.3.3. Top County CRSI Values 72
4.3.4. Breakdown of the Risk Domain 73
4.3.5. Polar Plots for Nation and EPA Regions 74
4.3.6. National Results 74
4.3.7 Regional Results 81
7. Future Directions for Community Resilience to Extreme Weather Events 142
8. References 144
9. Appendices 155
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Table of Figures
Figure E-l. Conceptual representation of the Cumulative Resilience Screening Index (CRSI)
Approach 13
Figure E-2. Map showing distribution of final CRSI Scores across the U.S. (2000-2015). Darker
colors indicate higher resilience scores; lighter colors indicate lower resilience scores 14
Figure E-3 The distribution of CSRI values and domain scores (Risk, Governance, Society, Built
Environmental, and Natural Environmental 15
Figure E-4. Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 5. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 16
Figure 1.1 Conceptual representation of the Cumulative Resilience Screening Index (CRSI)
Approach 24
Figure 2.1 Number of applied resilience indices found using multi-factor composite index
measures 27
Figure 2.3 Final CRSI conceptual framework. Arrows projected from boxes to the left and right
represent hypothetical increases and decreases in ranges for indicators (black arrows) and
domains (colored arrows) 34
Figure 2.4 Representation of the Metric, Indicator and Domain scores for Governance, Society,
Built Environment and Natural Environment Domains of CRSI. For this report, aggregations
were made at the EPA regional scales and national scale. Similar aggregations could be
accomplished at any appropriate scale (e.g., western regions, intermountain regions, coastal
regions) 51
Figure 2.5 Representation of the Metric, Indicator and Domain scores for Risk Domain of CRSI.. 52
Figure 4.1 Linear assessment of risk versus governance based on domain scores 65
Figure 4.2 Distribution of number of counties in quartiles for risk and governance domains based
on the domain scores 66
Figure 4.3 Map of the distribution of county scores for basic resilience 67
Figure 4.4. Distribution of number of counties in quartiles for risk and governance domains
based on number of samples (redistributing the basic resilience scores 68
Figure 4.6 Example summary of CRSI and domain available for the nation and each EPA region. 70
Figure 4.7 Example of six-panel maps showing the distribution of county-level CRSI and domain
scores available for the nation and for the EPA Regions 71
Figure 4.8 Example Table of highest ranking CRSI values for all U.S. counties and counties
within EPA Regions. All state and county CRSI scores can be found in Appendices B and C 72
Figure 4.9 Example summary of Risk domain presented for the nation and the EPA Regions 73
Figure 4.10 Example polar plot describing the contributions of the 20 indicators to the domain
scores 74
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Figure 4.11 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for the U.S, along with domain median adjusted scores showing influence of each domain on
final CRSI score (dark colored bars) 75
Figure 4.12 The distributions of CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment) 76
Figure 4.13 U.S. map depicting scored natural hazard risk exposure by county. Bar charts
showing the percentage of counties with > 0.01% of total land area: exposed to natural hazards
by event type; at risk for secondary technological hazard exposures; and cumulative losses
incurred as a result of natural hazard events. The counties exhibiting the highest risk and lowest
risk along with National risk score average (several counties have 0.01 and 0.99 adjusted risk
domain scores but Kodiak Island, AK has the lowest unadjusted calculated risk score and Shelby,
TN has the highest unadjusted risk score) 79
Figure 4.14 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the nation. The length of the bars corresponds to the indicator score. Within a domain,
the higher indicator scores show a greater contribution to the domain 80
Figure 4.15 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 1 along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 83
Figure 4.16 The distributions of EPA Region 1 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 84
Figure 4.17 Map of Risk Domain scores by county for Region 1; proportion of natural exposures
by climate event type, technological exposures, losses and exposure type nationwide; and the
range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region (If a category was represented by <0.1%, it was not
included) 86
Figure 4.18 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 1. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 87
Figure 4.19 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 2, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 89
Figure 4.20 The distributions of EPA Region 2 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 90
Figure 4.21 Map of Risk Domain scores by county for Region 2; proportion of natural exposures
by natural hazard event type, technological exposures, losses and exposure type nationwide; and
the range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 92
Figure 4.22 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 2. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 93
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Figure 4.23 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 3, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 95
Figure 4.24 The distributions of EPA Region 3 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 96
Figure 4.25 Map of Risk Domain scores by county for Region 3; proportion of natural exposures
by climate event type, technological exposures, losses and exposure type nationwide; and the
range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 98
Figure 4.26 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 3. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 99
Figure 4.27 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 4, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 101
Figure 4.28 The distributions of EPA Region 4 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 102
Figure 4.29 Map of Risk Domain scores by county for Region 4; proportion of natural exposures
by climate event type, technological exposures, losses and exposure type nationwide; and the
range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 104
Figure 4.30 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 4. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 105
Figure 4.31 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 5, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 107
Figure 4.32 The distributions of EPA Region 5 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 108
Figure 4.33 Map of Risk Domain scores by county for Region 5; proportion of natural exposures
by natural hazard event type, technological exposures, losses and exposure type nationwide; and
the range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 110
Figure 4.34 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 5. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) Ill
Figure 4.35 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for the U.S, along with domain median adjusted scores showing influence of each domain on
final CRSI score (dark colored bars) 113
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Figure 4.36 The distributions of EPA Region 6 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 114
Figure 4.37 Map of Risk Domain scores by county for Region 6; proportion of natural exposures
by climate event type, technological exposures, losses and exposure type nationwide; and the
range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 116
Figure 4.38 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 6. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 117
Figure 4.39 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 7, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 119
Figure 4.40 The distributions of EPA Region 7 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 120
Figure 4.41 Map of Risk Domain scores by county for Region 7; proportion of natural exposures
by natural hazard event type, technological exposures, losses and exposure type nationwide; and
the range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 122
Figure 4.42 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 7. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 123
Figure 4.43 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 8, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 125
Figure 4.44 The distributions of EPA Region 8 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 126
Figure 4.45 Map of Risk Domain scores by county for Region 8; proportion of natural exposures
by natural hazard event type, technological exposures, losses and exposure type nationwide; and
the range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 128
Figure 4.46 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 8. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 129
Figure 4.47 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 9, along with domain median adjusted scores showing influence of each domain
on final CRSI score (dark colored bars) 131
Figure 4.48 The distributions of EPA Region 9 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 132
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Figure 4.49 Map of Risk Domain scores by county for Region 9; proportion of natural exposures
by natural hazard event type, technological exposures, losses and exposure type nationwide; and
the range of risk with the highest risk and lowest risk counties identified; as well as, the three
primary exposure types in the region 134
Figure 4.50 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 9. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 135
Figure 4.51 Summary of CRSI (upper right hand value) and domain scores (light colored bars)
for EPA Region 10, along with domain median adjusted scores showing influence of each
domain on final CRSI score (dark colored bars) 137
Figure 4.52 The distributions of EPA Region 10 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 138
Figure 4.53 Map of Risk Domain scores by county for Region 10; proportion of natural
exposures by natural hazard event type, technological exposures, losses and exposure type
nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well
as, the three primary exposure types in the region 140
Figure 4.54 Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 10. The length of the bars corresponds to the indicator score. Within a
domain, the higher indicator scores show a greater contribution to the domain score (sum of
indicator scores) 141
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Table of Tables
Table E-l. CRSI and domain scores for EPA Regions with National Average scores (including
Alaska); (Bold denotes significantly below national average for CRSI and above national
average for domains) 19
Table 2.1 Existing measures of climate resilience included in this review, the number of
domains/indicators and metrics used in each measure 28
Table 2.3 Summarized climate/natural hazard impacts and resilience issues for selected cities of
the U.S. from 100 Resilient Cities and ICLEI/RC4A (Local Governments for Sustainability
(previously the International Council for Local Environmental Initiatives)/Resilient Communities
for America) 31
Table 2.4 Summary of literature reviewed index by topical areas of interest for development of
CRSI 33
Table 2.5 List of CRSI domains, indicators, scope and number of metrics. Numbers in
parentheses for domains show the total number of indicators/total metrics in the domain 47
Table 3.1. CRSI and domain scores for select counties along the Texas Gulf Coast and National
Average scores (excluding Alaska); (Bold denotes significantly below national average for CRSI
and above national average for domains) 61
Table 3.2. CRSI and domain scores for EPA Regions with National Average scores (including
Alaska); (Bold denotes significantly below national average for CRSI, significantly above
national average for risk domain and simply below national average for remaining domains
which results in negative adjustment factors) 63
Table 4.1 Top 150 counties according to CRSI values (i.e., potentially higher resilience to natural
hazard events) 78
Table 4.2 Top 25 counties according to CRSI values in EPA Region 1 (i.e., higher resilience to
natural hazard events) 85
Table 4.3 Highest 25 CRSI values in EPA Region 2 by county 91
Table 4.4 Counties in EPA Region 3 with the highest CRSI values 97
Table 4.5 Twenty-five counties in EPA Region 4 with the highest CRSI values 103
Table 4.6 Twenty-five counties in EPA Region 5 with the highest CRSI values 109
Table 4.7 Twenty-five counties in EPA Region 6 with the highest CRSI values 115
Table 4.8 Twenty-five highest CRSI values in the counties of EPA Region 7 121
Table 4.9 Twenty-five counties in EPA Region 8 with the highest CRSI values 127
Table 4.10 Twenty-five counties in EPA Region 9 with the highest CRSI values 133
Table 4.11 Twenty-five counties in EPA Region 10 with the highest CRSI values 139
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Acronyms and Abbreviations
BRIC Baseline Resilience Indicators for Communities
CBNRM Community-Based Natural Resource Management
CDRI1 Climate Disaster Resilience Index 1
CDRI2 Climate Disaster Resilience Index 2
CEQ Council on Environmental Quality
CRS Community Rating System
CRSI Cumulative Resilience Screening Index
CWPPRA Coastal, Wetlands Planning, Protection and Restoration Act
DOC Department of Commerce
DOI Department of the Interior
EPA/USEPA U.S. Environmental Protection Agency
FEMA Federal Emergency Management Agency
GAO Government Accounting Office
ICLEI Local Governments for Sustainability (previously the International Council for Local
Environmental Initiatives)
LUST Leaking Underground Storage Tanks
M-CRD Metrics for Community Resilience to Disaster
MERLIN-RC Model for External Reliance of Localities In Regional Contexts
M-RD Metrics for Resilience to Disaster
NCA National Climate Assessment
NHEERL National Health and Environmental Effect Research Laboratory
NFIP National Flood Insurance Program
NGO Non-Government Organization
NRC National Research Council
NRCS National Resource Conservation Service
OECD Organization for Economic Co-Operation and Development
OMB Office of Management and Budget
PCA Principal Components Analysis
PRISM Patterns of Risk using an Integrated Spatial Multi-Hazard Model
RC4A Resilient Communities for America
RCRA Resource Conservation and Recovery Act
SBA Small Business Administration
SHC Sustainable and Healthy Communities Research Program
SHELDUS Spatial Hazard Events and Losses Database
SOVI Social Vulnerability Index
TRI Toxic Resource Inventory
USFS United States Forest Service
USGS United States Geological Survey
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Acknowledgments
The authors would like to thank the peer reviewers of this report:
Courtney G. Flint, Ph.D., Professor of Natural Resource Sociology, Dept. of Sociology, Social
Work and Anthropology, Utah State University
Peter B. Meyer, Ph.D., Professor Emeritus of Urban Policy and Economics, University of
Louisville, President and Chief Economist, The E.P. Systems Group, Inc.
Matthew Nicholson, EPA Region 3
Meaghan Bresnahan, EPA Region 6, Unable to Complete due to Hurricane Harvey
Adele Cardenas Malott, EPA Region 6
Suzanna Perea, EPA Region 6
Joyce Stubblefield, EPA Region 6
Laura J. Farris, Climate Change Coordinator and International Coordinator, EPA Region 8
Bruce Duncan, Regional Science Liaison to Office of Research and Development, EPA Region
10
Jeff Peterson, Senior Policy Advisor, EPA Office of Water
Megan Susman, EPA Office of Sustainable Communities
Dana Krishland, EPA Office of Air and Radiation
Emma Zinsmeister, EPA Office of Air and Radiation
Additionally, the authors would like to thank Ms. Virginia Houk of the National Health and
Environmental Effects Research Laboratory for organizing and conducting the peer review.
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Executive Summary
This 2019 revision research report, Development of a
Cumulative Resilience Screening Index (CRSI) for Natural
Hazards: An Assessment of Resilience to Acute
Meteorological Events and Selected Natural Hazards is a
revision of the original 2017 report: Development of a
Climate Resilience Screening Index (CRSI): An
Assessment of Resilience to Acute Meteorological Events
and Selected Natural Hazards (Summers et al. 2017). This
revised report is a synthesis report detailing research in the
development and demonstration of the CRSI approach at
both the national and regional scales using county data
Natural Environment
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The Cumulative Resilience Screening Index (CRSI) has been developed as an endpoint for
characterizing county and community resilience outcomes that are based on risk profiles and
responsive to changes in governance, societal, built and natural system characteristics. The
Cumulative Resilience Screening Index (CRSI) framework (Figure E-l) serves as a conceptual
roadmap showing how acute natural hazard events impact resilience after factoring in the county
and community characteristics. By evaluating the factors that influence vulnerability and
recoverability, an estimation of resilience can quantify how changes in these characteristics will
impact resilience given specific hazard profiles. Ultimately, this knowledge will help
communities identify potential areas to target for increasing resilience to acute natural hazard
events (Figure E-2).
!&, . 'W'-
Ik . MI'S at
Score
Lower
Figure E-2. Map showing distribution of final CRSI Scores across the U.S. (2000-2015).
Darker colors indicate higher resilience scores; lighter colors indicate lower resilience scores.
The index is a composite measure comprised of five domains (Risk, Governance, Society, Built
Environment, and Natural Environment), represented by 20 indicators, calculated from 117 metrics.
CRSI scores have been calculated at the county level (or parish or borough) and community
resilience, and additional break out assessments are presented for individual domains of the index
as well as regional level as a composite for the years 2000-2015 (Figure E-3). In addition, to a
national assessment of resilience, EPA regional and county measured are calculated and mapped.
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Figure E-3 The distribution of CSRI values and domain scores (Risk, Governance, Society,
Built Environmental, and Natural Environmental)
Score
—~ Higher No Data
Governance
Lower
Society
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Regional analyses characterize risk components, evaluate relative domain contributions to
resilience, and delineate indicator contributions within the geography. Polar plots are utilized as
a method to easily discern indicator influence (Figure E-4).
Ecosystem
Extent
Exposure
Condition
Vacant
Structures
' Community
/ Preparedness
latural \ •?,
¦puree \ *
Servation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
economics
Health
Characteristics
Social
Services
Safety
Social &
Cohesion Security
Figure E-4. Polar plot showing the contribution of the 20 indicators associated with the domain scores
for the EPA Region 5. The length of the bars corresponds to the indicator score. Within a domain, the
higher indicator scores show a greater contribution to the domain score (sum of indicator scores).
CRSI was developed with input from EPA Regional Climate Coordinators and ORD Regional
Science Liaisons. The demonstration results by county and by EPA region can be used by the
Regions to engage communities in resilience discussions, be vetted with local knowledge and
potentially be used to target resources for improving resilience. CRSI results data, like EQI and
HWBI results can be made available through the Geoplatform for use in SITC tools. Overall CRSI
values, and domain scores at the county-level can inform sustainability assessments research
(4.61) and could complement vulnerability assessments for developing resilience strategies (e.g.,
developing water resilience strategy for Merrimack River, Lawrence, MA (SHC 2.62)).
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"Your effort is "laudable and important" I very much
appreciate the focus on a multi-dimensional
approach to assessing climate related resilience. The
report cites important literature defining resiliency
and vulnerability. The graphics are visually appealing
and, as long as the data are accurate behind them,
likely to be helpful for various users. But I have some
concerns about the data being used in ways that
might not be appropriate given the aggregation and
operationalization issues that relate to data
availability, I appreciate that such an index endeavor
is limited to existing available data. But these
limitations need to be much more clearly
acknowledged.'
Dr. Courtney Flint, Utah State University
Great care lias teen tolcen to ensure that the
aggregations used in CRSI are correct and the
authors have attempted to provide examples of how
to use the index. Might elements of CRSI be
misused? Of course, this is the case with any index or
aggregation of data; however, the authors have
taken great pains to ensure the accuracy and
limitations of the available data, - Dr. Kevin
Highlights of Results
In the section above, the maps and analytic results of the national application are shown. The
highlights of the national analysis show moderate to strong resilience to natural hazard events
throughout many of the counties in the U.S. Areas with weaker overall resilience include the
Appalachians, many counties in the southeast and the western Mid-West and some counties in
southwestern Texas. Strong contributors to the final CRSI scores are natural resource
conservation, local demographics, and information pertaining to vacant structures. Weak
contributors include infrastructure associated with utilities and communications and safety and
security issues as well as the local mix of labor skills. Increases in these weak contributors could
substantially enhance resilience to acute natural hazard events on a national scale.
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Regional analyses (Table E-l) and mapping show that EPA Region 10 (14.8) and EPA Region 1
(10.7) have the strongest overall resilience scores with EPA Region 4 (0.6) and EPA Region 6
(2.8) having weaker scores. The remaining six EPA Regions cluster together with moderate
scores (3.4-6.1). Disassembly of the CRSI scores shows that Region 10 strengths lie in its low risk
score which result in a high basic resilience score even though its governance low is less than the
national average. Although lower, its governance domain score is more than three times the
Region's risk domain score. Region 1 strengths lie in the highest governance score in the Nation
with moderate risk, and above average domain scores for social, built environment and natural
environment. On the other hand, Regions 4 and 6 have above average risk domain scores and
below average governance related to natural hazard events scores. Driving down these lower
basic resilience scores, both regions have below average society domain scores suggesting a
poorer population, increased ethnicity (making communication for emergency response more
difficult), lower levels of social services, poorer access to health facilities, and higher level of
undocumented skilled trade laborers (making an assessment of the abundance of trade labor
difficult). Region 4 also has a below average score for its built environment suggesting less
stringent building codes, higher levels of vacant structures and weaker levels of public
infrastructure especially in Georgia and Alabama.
The utility of the index is addressed in Section 3 although the greatest level of confidence in
utility can be found in the quotes listed below by reviewers from EPA Regions in response to the
questions, "In your opinion, does the index have utility for EPA (e.g., Regions and Program
Offices)?" and "Does this utility extend to community decision makers, community planners,
and other potential stakeholders?".
"Fes - Using the data in work we do in each of our
programs relative to pollution control implications
and sustainability"
-Joyce Stuhhlefield, Region 6
'utely! I like the discussion of ORD research
1 to natural disaster and other climate
event resiliency topics ..." -Laura Farris, Region 3
"I look forward to seeing the final report and using it
in my own work," -Matt Nicholson, Region 3
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Table E-l. CRSI and domain scores for EPA Regions with National Average scores (including
Alaska); (Bold denotes significantly below national average for CRSI and above national
average for domains).
Built Natural
EPA Region Risk Governance Environment Environment Society
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
Region 7
Region 8
Region 9
Region 10
National
Average
0.240
0.308
0.272
0.255
0.222
0.239
0.209
0.162
0.235
0.137
0.229
0.660
0.658
0.571
0.443
0.696
0.584
0.683
0.685
0.551
0.660
0.597
0.492
0.469
0.382
0.342
0.407
0.394
0.358
0.398
0.620
0.478
0.393
0.445
0.385
0.378
0.403
0.434
0.422
0.380
0.395
0.469
0.531
0.4 J 3
0.599
0.520
0.512
0.414
0.572
0.474
0.609
0.617
0.480
0.492
0.516
"Yes, there is poten tial use. Regions and programs
are being asked that same question for other indices
based on similar structure (national databases;
selecting domains; comparison at a county scale)
developed by ORD (e.g., HWBI). ... Certainly, this will
have utility at the county level and for others who
can use it as is to aggregate above counties (such as
coastal states or coastal counties), ... Again, a
community of practice across index developers could
help quickly identify many issues that stakeholders
have raised,"
-Bruce Duncan, Region 10
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To be fair, not all reviewers were as enthusiastic. Several reviewers not associated with EPA
Regions found greater difficulty with the utility of the index. These reviewers thought it would be
very helpful to indicate how the index could be used and how it should not be used. However, the
target audience of CRSI is the EPA Regional staff working on resilience and sustainability issues and
the index and its utility appears to resonant with the Regional reviewers. Overall, the U.S. shows
good levels of resilience to acute climatic events. However, analyses demonstrate that selected
counties (hundreds of them) with higher levels of risk and low levels of governance can improve their
resilience by specifically addressing issues associated with the governance, built environment, natural
environment, and society domains. CRSI, which is meant to be a screening tool, provides those
directions investment, assistance and action by the EPA Regions and Program Offices.
20
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1. Introduction and Background
Natural hazard events often impose significant and long-lasting stress
on financial, social and ecological systems. From Atlantic
hurricanes to midwest tornadoes to western wildfires, no corner
of the U.S. is immune from the threat of a devastating climate-
event. Statistics from the Office of Management and Budget
show the federal government has incurred more than $357 billion
in direct costs due to extreme weather and fire events alone over
the last ten years (OMB and CEQ 2016). Starting in 2013, the
U.S. Government Accountability Office (GAO) began
monitoring the high-risk fiscal exposure that the federal
government faces because of natural hazard-related events, both
acute and chronic. The GAO recognized the sweeping impacts of
these events across multiple sectors including defense,
infrastructure, health, agriculture and local economies. In the
most recent GAO report (2017), steps to better manage this fiscal
risk had only been partially implemented. Further, the U.S.
National Security Strategy (2015) highlights efforts in
strengthening county and community resilience, suggesting that
impacts from adverse natural hazard events represent an area of
credible national security concern.
SUSTAINABILITY
AND RESILIENCE
In general terms, resilience is a characteristic in human and
natural systems exhibiting a capacity to withstand and recover
from an adverse shock or event. In towns and cities, resilience is
promoted through planning while in nature, this trait is assumed
inherent (NRC 2012; Meadows 2008). Over the last decade, there
has been a notable increase in communities seeking sustainable
economic, social and ecological solutions for local planning
concerns. However, more county and community decision
makers are recognizing that recurring and anomalous natural
hazard events may impede achieving their sustainability goals
without appropriate and actionable preparation. Therefore, it is
not surprising that interest in the subject of resilience related to
natural hazard events, both cyclic and evolving, is growing.
"...RESILIENCE THEN BECOMES
A THEORETICAL CONSTRUCT
FOR SUSTAINABILITY THAT: A)
GUIDES AGAINST BREACHING
UNKNOWN SYSTEMS
BOUNDARIES; B) SUGGESTS
THAT CONTINUOUS CHANGES
IN CERTAIN DRIVING
VARIABLES ARE INHERENTLY
DANGEROUS (E.G.,
CONTINUOUSLY INCREASING
FISHING PRESSURE,
ESCALATING GREENHOUSE
GAS EMISSIONS, OR
CONSTANT MATERIAL
GROWTH) AND; C) WARNS
THAT SURVIVING THE BREACH
OF A MAJOR TIPPING POINT,
WHETHER HUMAN INDUCED
OR NATURAL, WILL REQUIRE
UNPRECEDENTED LEVELS OF
INVESTMENT, COOPERATION
AND OTHER FORMS OF
INSTITUTIONAL AND
SOCIETAL ADAPTATION.
HUMAN-INDUCED CLIMATE
CHANGE WILL ALMOST
CERTAINLY VALIDATE ALL
THESE ASSERTIONS."
SUSTAINABILITY VS. RESILIENCE
PUBLISHED BY RESILIENCE.ORG ON
2014-07-16
BY WILLIAM E. REES
SOURCE URL:
http://www.resilience.org/stori
ES/2014-07-16/SUSTAINABILITY-
VSRESILIENCE
Across the nation, there is a recognition that the benefits of
creating environments resilient to adverse natural hazard events
helps promote and sustain county and community success over
time. The challenge for communities is in finding ways to
balance the need to preserve the socio-ecological systems on which they depend in the face of
constantly changing natural hazard threats. Resilience applies to both human and natural
21
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systems, yet the examination of resilience is often described without appreciation of one another
or in the context of opposing roles (Handmer et al. 2012)—with one system making the other
more vulnerable. Previous research suggests that positive aspects of county and community
quality of life are linked to not only built environments, but natural ones as well (Smith et al.
2012; Summers et al. 2012). Any discussion of county and community resilience would be
incomplete without considering the role of natural ecosystems, as they have the ability to
influence many of a county's and community's vulnerability and recoverability characteristics
(Summers et al. 2012, 2015).
In the context of this research, vulnerability describes the propensity or predisposition to be
adversely affected, while resilience describes the ability of a system and its component parts to
anticipate, absorb, accommodate, or recover from the effects of a hazardous event in a timely
and efficient manner (IPCC 2012). Much of the existing resilience literature focuses on either
vulnerability or recovery (e.g., Cutter et al. 2003; Frazier et al. 2014) as independent constructs
of resilience. Summers et al. (2016) suggests a more holistic relationship exists, where an
intersection of vulnerability and recoverability sits along a spectrum of resilience. The position
along this gradient where human and natural systems rest depends on their ability or capacity for
resilience. In terms of natural hazard events, for example, both people and nature can absorb,
recover from and adapt to adverse events (Gunderson 2010; Berkes and Ross 2013). However,
the degree of resilience is reflected in the mechanisms for recovery. Natural ecosystems have
innate internal structures and functions to facilitate recovery from an adverse event (such as
diversity and redundancy) (Holling 1986; National Fish, Wildlife and Plants Climate Adaptation
Partnership 2012; Melillo et al. 2014). Human systems rely on planning and preparation to
mitigate against known natural hazard exposures and reduce vulnerabilities (Tobin 1999; Magus
2010). In both systems, the success of the recovery process is dependent on the robustness of the
mechanism. This robustness refers to the system's ability to resist or tolerate change without
adapting its initial stable configuration. In the case of nature, ecological conditions may be the
determining factor while the depth and breadth in resilience planning or governance is a pillar for
resilience in built environments. Clearly, resilience is a disputed and heavily debated subject
with regard to anthropogenic and natural systems (Patel et al. 2017). Community resilience
remains an amorphous concept that is understood and applied differently by different groups.
Yet despite the differences in conception and application, there are well-understood elements
that are widely proposed as important for a resilient community. All seem to agree that
community resilience (non-individual) relates to the sustained ability of a community (or other
entity) to utilize available resources to respond to, withstand, and recover (hopefully quickly)
from adverse difficulties or perturbations (FEMA 2011, 2012, 2017; RAND 2017).
22
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Operationally, in this report, a broad definition of community has been taken.
Using a community definition of a social group of any size whose members
reside in a specific locality, share government, and often have a common
cultural and historical hertitagc,"community" could be synonymous with
"county". Thus, the term "community," when used in (his report means the
grouping is a county unless specified otherwise. Resilience clearly can apply
to a smaller community unit or neighborhood. That is not the case in this
report. However, in many situations smaller communities resilience can be
directly related to or driven by governance and activities at the county scale.
Many counties and communities are seeking assistance from the U.S. Environmental Protection
Agency (EPA) to help fill resilience information gaps for disaster resilience planning. To better
assist counties and communities, EPA's Office of Research and Development (ORD) has invested
in research related to natural hazard event resiliency topics including:
• National Homeland Security Research Center's investigation of community resilience to
acute disaster events (USEPA 2015b)
National Center for Environmental Assessment research on resilience to climate change
(USEPA 2016b)
• National Exposure Research Laboratory's (NERL) work with counties and communities to
assess resilience to natural hazard events, particularly flooding (Lawrence, MA) (Zartarian
2016)
• National Risk Management Research Laboratory's (NRMRL) research focusing on linking
resilience measures to adaptive management and governance to help frame sustainability
assessments (Garmestani and Benson 2013; Garmestani and Allen 2014; Eason et al.
2016).
Of particular interest to EPA are the development of approaches to assess county and community
resilience readiness in the face of adverse natural hazard events. As part of EPA's Sustainable
and Healthy Communities (SHC) Research Program, a suite of indicators was developed to form
the basis of a composite index—the Cumulative Resilience Screening Index (CRSI). CRSI
characterizes county and communi ty resilience based on a suite of indicators that are grouped
into broad categories or domains of county and community resiliency traits in the context of
natural hazard events. CRSI is intended to be used by EPA Regions and others who work closely
with counties and communities to gauge resilience of built and natural systems to acute natural
hazard events (e.g., hurricanes, wildfires, tornadoes, flooding). The CRSI approach focuses on
characterizing county and community resilience to these natural hazards through an
understanding of the existing conditions in socio-ecological systems - the baseline against which
resilience is quantified. The index and constituent components serve to characterize baseline
conditions for targeting resources and assessing the effectiveness of programs, policies and
23
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interventions specifically designed to improve natural hazard resilience. Five broad areas of
common county and community characteristics or domains are the basis for formulating the
screening tool. CRSI represents a synthesis of vulnerability and recoverability of a county's and
community's built, natural and social environments in relation to the governance of these
systems and context of the risk of natural hazard exposure (Figure 1.1).
Acute Climate Events
Figure 1.1 Conceptual representation of the Cumulative Resilience Screening Index (CRSI)
Approach.
2. Approach
2.1. Overview of Indicator/Indices Development
The methodological challenge in deriving an index of resilience to acute natural hazard events
lies in constructing domains and indicators that are accurate representations of environmental or
societal states and trends but are easily understood by their target audiences. Methodological
challenges involve two broad sets of questions: those concerned with the design and
development of the index/indicators and those concerned with the purpose and use of the
index/indicators. Basic concerns over data availability, data quality, and the adequacy of the
algorithms used can be resolved largely through technical, scientific agreement. However, the
24
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central issue of adjusting methods to index relevance and use has to be addressed through
tradeoffs between form and function in specific societal and political settings.
The general technical approach is based on a familiar and common one, in use for several decades
to develop indices and compare components in a way to describe the current condition and help
stakeholders identify areas to investigate for potential management actions/decisions (Stanners et
al. 2007).
WHAT IS AN INDEX?
An index is made up of many components and indicator research
has a language all its own. Here are few key definitions:
INDEX - An interpretable and synergistic value or category
describing the nature, condition or trend of a multidimensional
concept An index can be an endpoint or final value as well as
one of several values used to create what is called a composite
index. CRSI is a composite index.
DOMAIN - Summary grouping of characteristics that is based on
one or more indicators and represents a major component of a
composite index. A domain and sub-index generally refer to the
same level of information.
INDICATOR: An interpretable value describing a trend or status a
specific feature or characteristic. An indicator may be comprised
of one or more metrics.
METRIC: A measurable or observable value — typically referred to
as "the data".
The relationship among domains, indicators and metrics is shown here as a nested box using the
example of the CRSI index, the risk domain, the exposure indicator and a specific metric of
exposure.
25
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2.2. A Review of Existing Resilience Indicators and Indices
A review of existing community resilience characterization methods and approaches was
conducted. The intent was to identify mainstream resilience indicators and indices and determine
the applicability of each within the scope of CRSI. A Google Scholar search was analyzed
through Publish or Perish® software (7/28/15) using the following keywords: "resilience index",
ecosystems, social, economic, human resilience, natural hazard, and climate change. The time
period of interest was 2000-2015. The initial search produced 369 print and web publications.
Material was considered for in-depth review if described index or framework met the following
criteria:
• Provided quantified or demonstration results
Comprised of a suite of indicators or sub-indices
• Exhibited spatially scalable characteristics
Integrated some combination of economic, ecological and social factors
• Focused on natural hazards.
Fifty-seven candidate indicators were described in the materials reviewed. This representative
group of existing resilience indices favored integrated socio-economic and ecological
development approaches, but to varying degrees. Similarly, review results showed a notable
trend toward the use of composite indices to characterize community resilience over the 2000-
2015 time period (Figure 2.1).
26
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14
2000 2001 2004 2005 2008 2009 2010 2011 2012 2013 2014 2015
Year of Publication
¦ Economic ¦ Ecological ¦ Economic+Social Ecological+Economic+Social ¦ Ecological+Economic
Figure 2.1 Number of applied resilience indices found using multi-factor composite index measures.
A pool of 27 publi shed indices met all the criteria. This final set of existing index development
approaches were used to further develop CRSI research efforts. Figure 2.2 briefly describes the
literature review and culling process. Collectively, the remaining selected literature offered 297
indicators, topical categories or domain groups with 624 related metrics (Table 2.1).
Figure 2.2 Publication elimination summary based on existing climate and natural hazard index
development literature (2000-2015) used to inform CRSI research efforts.
27
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Table 2.1 Existing measures of climate resilience included in this review, the number of
domains/indicators and metrics used in each measure.
Index
Domains or
Indicators
Agriculture Resilience Index (Ciani
2012)
Arctic Water Resource
Vulnerability Index (Alessa et al.
2008)
Baseline Resilience Indicators for
Communities (Cutter et al. 2014)
11
49
Metrics
27
22
49
Index
Composite Measure of Ecological
Integrity (Vickerman and Kagan
2014)
Displacement Risk Index
(Esnard et al. 2011)
EJ Screen Index
(U.S. EPA 2015a)
Domains
or
Indicators
22
15
12
Metrics
22
51
12
City Resilience Index (ARUP 2014)
12
12
Environmental Performance Index
(Hsu et al. 2016)
20
20
City Resilience Index to Sea Level
Rise (Baraboo and Hassan 2014)
13
Environmental Sustainability Index
(Esty et al. 2005)
21
76
Climate Disaster Resilience Index
(Joerin and Shaw 2011; Peacock et
al. 2010)
Community Resilience Index
(Kafle 2012; Renschler et al.
2010)
Community Resilience Index for
the Gulf of Mexico
(Baker 2009)
Community Risk Index
(Daniell et al. 2010)
Composite Measure of Coastal
Community Resilience
(Li 2011)
Composite Measure of Community
Resilience
(Meheretal. 2011)
Composite Measure of Regional
Resilience
(Martini 2014)
Composite Measure of Resilience
to Disasters
(Kusumastuti et al. 2014)
25
38
6
30
27
52
22
120
82
29
30
46
27
130
27
63
Environmental Vulnerability Index
(Pratt et al. 2004)
Flood Resilience Index
(Batica 2015)
Flood Vulnerability Index
(Balica 2012)
Household Resilience Index
(Cassidy and Barnes 2012)
Metrics for Community Resilience
to Disaster
(Burton 2015)
Resilience Factor Index
(Ainuddin and Routray 2012)
Resilience Inference Measurement
Model
(Li 2013; Lam et al. 2016)
Sustainable Society Index
(van de Kerkand Manual 2014)
50
43
19
16
22
16
10
21
50
91
19
16
75
17
33
21
A review of indicator categories and related measures presented in the literature showed that
vulnerability concerns stood out as a major recurring theme. This is not surprising since identifying
vulnerability is typically the first step toward defining resilience i.e., recognizing hazard exposure
weaknesses (e.g., Balica 2012; Batica 2015). However, vulnerability alone is not sufficient to
28
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characterize natural hazard resilience. In several cases, existing indices offered well-rounded
considerations for exposure vulnerability but often lacked similarly extensive measures of
recoverability from these same exposures, (e.g., Alessa et al. 2008; Joerin and Shaw 2011).
There were examples of resilience indices that included both recovery and vulnerability indicators,
but these tended to compartmentalize the constructs into two distinct considerations (e.g., Cutter et
al. 2014) rather than in a synthesized fashion. While several existing indices (ARUP 2014; Cutter et
al. 1996, 2003, 2014) provided a more balanced suite of vulnerability and recoverability resilience
measures, scale or scope limited the generalizability of these indices to fully generate suites of
nationally comparable measures.
2.3. Determination of Natural Hazard Event Factors to be Included in
CRSI
The National Climate Assessment summarizes the current and future impacts of climate change in
the United States (http://nca2014.globalchange.gov/report). In this report, the likely changes in
climate and natural hazard events associated with geographic regions throughout the United
States were assessed, as well as the infrastructure challenges these changes would likely create
(Table 2.2). Extended heat waves (with associated drought), more frequent heavy downpours
(with associated flooding), sea level rise, enhanced insect outbreaks, increased wildfires, altered
timing of streamflow, increased and faster sea ice and glacial loss, and increased major storm
events (including hurricanes, tornadoes and superstorms) are all resultant natural hazard changes
that will likely be seen in the coming decade. Communities (human and natural) will need to
"adapt" to meet the challenges presented by these changes. In human communities, that
adaptation can take the form of enhanced governance to increase recoverability to these events.
In natural communities, the "adaptation" likely will take the form of enhanced structural and
functional redundancy to recover from stress. This combination of modified exposure and
increased recoverability through governance and natural ecosystem processes is the basis of
resilience.
In initial CRSI development discussions, climate/natural hazard experts in each of the ten EPA
regions were interviewed to understand their views on the greatest natural hazard challenges in
their regions. These reported challenges matched well with those identified in the National
Climate Assessment and the 100 Resilient Cities report (Rockefeller Foundation and ARUP
2014), as depicted in Table 2.3. Rockefeller's 100 Resilient Cities helps cities around the world
become more resilient to the physical, social and economic challenges of the 21st century. The
EPA Regional interviews, the 100 Resilient Cities findings and the National Climate Assessment
were combined to determine the eleven (11) natural hazard events that would be tracked in CRSI.
These eleven natural hazard event types are:
Hurricanes • Drought
Tornadoes • High Winds
Inland Floods • Hail
Coastal Flooding • Landslides
Earthquakes • Temperature Extremes (high and low
Wildfires deviations of temperature).
29
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Table 2.2 Summarized climate impacts for regions of the U.S. from the 2014
National Climate Assessment Report. EPA regions within the regional assessment
are identified in parentheses.
National Climate Assessment 2014
http://nca2014.globalchange.gov/report
Regional Assessments
Northeast (EPA Region 1, Region 2 and Region
3 (excluding VA)
Heat waves, heavy downpours, and sea level rise
pose growing challenges to many aspects of life in
the Northeast. Infrastructure, agriculture, fisheries,
and ecosystems will be increasingly compromised.
Many states and cities are beginning to
incorporate climate change into their planning.
Southwest (EPA Region 9 and Region 8 (UT
and CO) Region 6 (NM))
Increased heat, drought, and insect outbreaks, all
linked to climate change, have increased
wildfires. Declining water supplies, reduced
agricultural yields, health impacts in cities due to
heat, and flooding and erosion in coastal areas
are additional concerns.
Southeast and Caribbean (EPA Region 3 (VA),
Region 4, Region 6 (AR and LA))
Sea level rise poses widespread and continuing
threats to the region's economy and environment.
Extreme heat will affect health, energy, agriculture,
and more. Decreased water availability will have
economic and environmental impacts.
Northwest (EPA Region 10 excluding Alaska)
Changes in the timing of streamflow reduce
water supplies for competing demands. Sea level
rise, erosion, inundation, risks to infrastructure,
and increasing ocean acidity pose major threats.
Increasing wildfire, insect outbreaks, and tree
diseases are causing widespread tree die-off.
Midwest (EPA Region 5 and Region 7 (IA and
MO))
Extreme heat, heavy downpours, and flooding will
affect infrastructure, health, agriculture, forestry,
transportation, air and water quality, and more.
Climate change will also exacerbate a range of risks
to the Great Lakes.
Alaska (EPA Region 10)
Alaska has warmed twice as fast as the rest of
the nation, bringing widespread impacts. Sea ice
is rapidly receding and glaciers are shrinking.
Thawing permafrost is leading to more wildfire,
and affecting infrastructure and wildlife habitat.
Rising ocean temperatures and acidification will
alter valuable marine fisheries.
Great Plains (EPA Region 6 (TX and OK), Region
7 (KS and NE) and Region 8 (excluding UT and
CO))
Rising temperatures are leading to increased
demand for water and energy. In parts of the
region, this will constrain development, stress
natural resources, and increase competition for
water. New agricultural practices will be needed to
cope with changing conditions.
Hawaii (EPA Region 9)
Warmer oceans are leading to increased coral
bleaching and disease outbreaks and changing
distribution of tuna fisheries. Freshwater supplies
will become more limited on many islands.
Coastal flooding and erosion will increase.
Mounting threats to food and water security,
infrastructure, health, and safety are expected to
lead to increasing human migration.
30
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Table 2.2 Summarized climate/natural hazard impacts and resilience issues for selected cities of the U.S. from 100 Resilient
Cities and ICLEI/RC4A (Local Governments for Sustainability (previously the International Council for Local Environmental
Initiatives)/Resilient Communities for America)
EPA Region
City/Climate Impacts
Extreme Heat;
Warming
Severe Drought
Extensive
Wildfire
Air Qua lity
Extreme
Rainfall;
Flooding
Storms; Sea-
level rise;
Erosion
Water Quality/
Quantity
infrastructure
Damage
Other Resilience Issues
1
* Boston. MA
-
-
-
affordable housing, social inequity
1
Cambridge. MA
x
x
2
** New York. NY
X
X
poor transportation system
3
Washington, DC
X
X
-
X
transportation and evacuation bottlenecks
3
Norfolk. VA
X
X
X
3
Lewes, DE
X
3
* Pittsburgh. PA
-
-
-
environmental degradation, infra structure failure
4
Atlanta. GA
X
4
6 reward County, FL
X
X
4
Miami Dade County, FL
X
X
X
5
Minneapolis, MN
X
X
5
Milwaukee. Wl
X
X
X
5
Grand Rapids. Ml
X
X
X
X
5
Ann Arbor, Ml
-
5
** Chicago, IL
X
X
X
X
endemic crime, infra structure failure, public health
6
* New Orleans. LA
X
X
X
infra structure fail ure
5
Houston, TX
X
X
X
X
X
6
* Dallas, TX
X
X
energy shortages, infra structure fail ure
6
" El Paso. IX
X
X
X
X
social inequty, epdemsc drue & alcohol abuse, poor
5
•Tulsa, OK
-
-
.
social inequity
6
Tucson, A2
X
-
X
Dubuoue. IA
X
X
X
X
crop failures
7
* St. Louis, MO
¦
.
-
-
social inequity, endemic crime, civil unrest
S
* Boulder. CO
X
X
X
X
X
invasive species, disease, affordable houang
s
Colorado Serines. CO
X
X
x
X
5
Denver, CO
X
x
8
Salt Lake City. UT
-
-
-
9
5an 0*eEQ Bav Region. CA
-
-
-
-
9
* Los Angeles, CA
X
X
X
earthquake, tsunami
9
* Oakland, CA
-
social inequity, earthquake, affordable hcustr®
9
* San Francisco. CA
-
*
-
earthquake
9
* Berkeley, CA
X
X
earthquake
10
Eugene. OR
X
X
cold water species diminishing, invasive species
10
Beaverton, OR
-
-
-
-
-
10
King County. WA
X
X
X
X
(*) 100 Resilient Gties {**) ICLEI/RC4A & 100 Resilient Ctics (x) Im poets Experienced (~) Projected Imports
31
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2.4. The CRSI Conceptual Framework
No singular approach among existing composite measures of natural hazard resilience met all
the expected needs for developing CRSI. Collectively, however, the reviewed literature
provided many of the building blocks (e.g., suites of indicators, indicator groupings, domains).
A "heat map" table (Table 2.4) depicts the metric distribution of the final 27 existing indices
across resilience topics of interest to CRSI. To varying degrees, all the existing indices offered
patterns of indicator groupings supporting the broad areas of interest for CRSI which formed the
basis of five sub-indices or "domains" to describe overall resilience:
• Natural Environment
Society
• Built Environment
Governance
• Risk
While none of the indices reviewed provided all possible indicators of interest to CRSI, 10 of
the 27 publications included information relevant for describing all five CRSI domains. The
Natural Environment, Governance and Risk domains were most frequently excluded from
existing measures. Five indices (BRIC, CDRI1, CDRI2, M-RD and M-CRD) offered fairly
comprehensive descriptions of indicators relevant for quantifying CRSI domains. The Climate
Disaster Resilience Index 2011 (CRDI1) contributed the most to the proposed CRSI structure;
addressing all domains based on a suite of 18 indicators.
Indicators and metrics from the selected literature were paired with one of the five CRSI
domains. Twenty-one domain-specific indicators were derived from 117 unique metrics. Figure
2.3 depicts the final CRSI conceptual framework. Constituents of CRSI: Domains and
Indicators of Community Resilience to Acute Natural Hazard Events. In this section, a summary
description of each CRSI domain and related indicators is provided. The summaries highlight
the importance of the domains in natural hazard related resilience and the indicators used to
characterize the five domains. For each indicator, example measures (metrics) are listed. For
more detailed information about the individual metrics for each indicator, refer to Appendix A.
32
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Table 2.4 Summary of literature reviewed index by topical areas of interest for development of CRSI.
CRSI Review Summary
Selected Index/Framework
Domains of
Resilience
Topic of
Interest
Candidate
Measurement
Categories
ARI
J
BRIC
CRI
CRIS
CDRI
CDRI
CResI
CRIG
CRisk
MCC
MCR
MRR
M-RD
M-EI
DRI
EJSI
EPI
ESI
EVI
FRI
hH
£
HRI
1
s
RFI
RIM
SSI
Natural
Environment
Extent of
Natural Areas
Managed Lands
Ecosystem Type
Integrity
Condition
Society
Economy
Economic Diversity
Employment
Insurance
Critical Services
Safety & Security
Social
Labor/Trade
-
Characteristics
Demographics
Health
Built
Environment
Infrastructure
Integrity/
Continuity
Communication
Transportation
Utilities
Structure/
Housing
Characteristics
Non-Residential
Residential
Shelter
Governance
Preparedness
Planning
Investment
¦
¦
Response
Expenditure
Time
Risk
Losses
Property
Human
Hazard
Exposure
Geophysical
Technology Hazards
# Existing Measures Related to Topic of Interest
id
20 +
List of index abbreviations: (ARI -Agricultural Resilience Index AWRVI -Arctic Water Resource Vulnerability Index BRIC -Baseline Resilience Indicators for Communities CRI-City Resilience Index CRISLR City
Resilience Indexto Sea Level Rise CDRIl-Climate Disaster Resilience Index 2011 CDRI2-Community Disaster Resilience Index 2010 CResl-Community Resilience Index CRIG -Community Resilience Indexfor
the Gulf of Mexico CRiskl-Community Risk Index MCCR -Composite measure of coastal community resilience MCR-Composite measure of community resilience MRR -Composite measure of regional
resilience M-RD -Composite measure of resilience to disasters M-EI -Composite measures of ecological integrity DRI-Displacement Risk Index EJSI-EJ SCREEN Index EPI -Environmental Performance Index
ESI-Environmental Sustainability Index EVI-Environmental Vulnerability Index FRI-Flood Resilience Index FVI -Flood Vulnerability Index HRI -Household Resilience Index M-CRDMetrics for community
resilience to disasters RFI-Resilience Factor Index RIMM -Resilience Inference Measurement model SSI-Sustainable Society Index).
33
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Natural Environment
Extent of Ecosystem Types
Condition
Built Environment
Utility Infrastructure <
Transportation Infrastructure
Communication
Infrastructure
Housing
Characteristics
Vacant Structures
Society
Social Services
Labor/Trade
Safety and Security
Social Cohesion
Socio-Economics
Health Characteristics
Economic Diversity
Demographics
Governance
Community
Preparedness
Personal Preparedness
• Natural Resource
Conservation
Climate & Natural Hazard Stressors
Legend
CRSI Composite Index
Gradient
Italicized Text External Influence
Domain
• Bullet
Indicator
Domain
Resilience
Range
Indicator
| Resilience
Range
Figure 2.3 Final CRSI conceptual framework. Arrows projected from boxes to the left and right
represent hypothetical increases and decreases in ranges for indicators (black arrows) and domains
(colored arrows).
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2.4.1. Risk Domain
/ - ' m
The risk domain of CRSI represents the characteristics of a place that contribute
to a level of exposure or loss resulting from specific hazards (climatic events, e.g.,
sea level rise, hurricane, tornado, wildfire, drought, etc.). Risk, as a construct,
typically represents the likelihood that an interaction with a hazard will result in
an adverse outcome. Within the CRSI
framework, hazard exposure is dealt with wholly within the risk domain. This contrasts with
vulnerabilities, handled as both losses in the risk domain, and socioeconomic characteristics, dealt
with across multiple domains. Socioeconomic characteristics are typically the focus of interventions
taken to increase resilience. Most geologic and atmospheric hazards cannot be controlled or
predicted, and only the likelihood of an event occurring in a specific timeframe can be calculated. In
the natural hazard resilience arena, this is the likelihood that a storm with specific severity will occur,
that sea level will rise by a certain amount, that a wildfire will occur, or extreme total rainfall will
occur. Potential for exposure results when there is more than zero likelihood of a threat occurring in
the same location as human and natural populations or the built environment.
Risk is assessed as a product of exposure probability and vulnerabilities, or the consequences
associated with that exposure. For example, assets (e.g., a county, community or built environment)
constructed in a river's floodplain have enhanced potential exposure to flooding; or an oil rig located
near a natural ecosystem (e.g., forest), enhances the potential exposure of the ecosystem to oil.
Similarly, managed ecosystems (e.g., managed forests, agriculture) constructed in drought prone
areas, have enhanced potential exposure to drought. In each of these scenarios, risk is the result of
exposures and vulnerabilities in a system that could yield a loss. If the goal of a county or community
is to minimize negative impacts, there are two options: reduce the exposure or reduce the
vulnerability. Depending on the structure of the county or community and the nature of the
vulnerability, one option may be easier to achieve. In a flood prone county and community, for
example, risk can be reduced by either reducing exposure potential, e.g., using residential zoning to
eliminate building in flood prone areas, or by reducing vulnerability, e.g., by raising houses in a flood
zone. In either case, identification of exposure and resulting impacts is necessary to inform the
decision, and this is the intent of the risk domain. In the CRSI model, risk is characterized by two
indicators - exposure and loss. The specific natural hazard events and technological hazard types are
listed in Table 2.5. A more in-depth discussion of the risk domain can be found in Buck et al. (2017).
Indicator: Exposure
The exposure indicator addresses the probability of hazard occurrence across a
full spectrum of geologic and atmospheric events as well as additional
technological hazards that may coincide with, or be exacerbated by, the events.
The geophysical category of metrics represents the likelihood of occurrence of a
geologic or atmospheric hazard based on
location of populations (human and non-human) and built environment. This category of metrics is
represented by metrics that characterize both historic and proximity-based likelihood of hazard
occurrence. The technological hazards category of metrics represents the probability of exposure to
hazards resulting from built technologies (e.g., nuclear power plants, oil pipelines, chemical
manufacturing). The exposure indicator includes measures of:
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• Earthquake probability
• Extreme high temperature incidents
• Extreme low temperature incidents
• Flood probability
• Hailstorm probability
• Hurricane probability
• Landslide probability
• Damaging wind incidents
Tornado probability
• Wildfire probability
CRSI calculates risk of exposure to acute natural hazard events and selected natural geological
hazards (e.g., earthquakes and tectonic landslides). The index does not address long-term climate
change and its secondary effects. The one exception is sea level rise; however, CRSI uses sea level
rise as part of coastal flooding based on historic rise and not as a future measure of predicted sea rise
level from climate change. Similarly, CSRI does not directly address secondary effects of some acute
natural hazard events (e.g., pest abundance, hydrologic shifts) but rather addresses these through the
direct acute natural hazard events associated with them (e.g., drought, high temperatures). Similarly,
CRSI does not include standard climatic events (e.g., rainfall, snowfall).
In addition, exposure for each county, parish and borough is modified by the proximity of
technological or anthropogenic hazards including the presence of:
• Nuclear sites
Toxic release sites
Superfund sites
• Resource Conservation and Recovery Act (RCRA) sites
This exposure modification is the result of the probability of exposure to a natural hazard event in a
pixel multiplied by one plus the probability of a technological hazard being located with a 5mile
radius for Superfund sites and a 10-mile radius of the pixel for other technological hazards; thus,
enhancing the overall exposure.
The loss indicator addresses an aspect of a place's vulnerability represented through
historical loss of life and property (including crops) associated with specific
hazards. The property loss indicators describe estimated and actual costs associated
with property and crop losses as a direct result of a hazard. Many of the potential
metrics for this indicator would come from the Spatial Hazard Events and Losses
Database (SHELDUS). Similarly, the human losses indicator represents the loss of human life
directly resulting from a hazard with metrics largely coming from the SHELDUS database. The loss
indicator includes human loss (i.e., fatalities and injuries), property loss (i.e., property damage) and
natural area loss (i.e., increase in impervious surface).
Indicator: Loss
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2.4.2. Governance Domain
"Governance" describes the collaboration of government agencies and Non-
Governmental Organization (NGOs) or private actors (e.g., companies,
citizens, etc.) towards joint objectives within a system of rules and regulations
(e.g., hierarchies, markets, networks, counties and communities, etc.) (Benz
2001; Liesbet and Marks 2003; Bache and Flinders 2004a, b). Consequently,
governance includes both formal and informal coordination processes among,
across and beyond different
sectors of public administration. It has been increasingly recognized that resilience problems related
to natural hazard events can only be sufficiently handled in an integrative way to include diverse
policy fields from all scales (Benz 2001) and actors from different fields (Huiteman et al. 2009; Pahl-
Wostl et al. 2012; ARUP 2014). However, the administrative systems of many U.S. federal, state,
county, city and community agencies are predominantly organized by sector. This organization
makes coordination a major challenge in the wake of a severe natural hazard event; such as, flooding
and sea level rise (Adger 2001; Adger et al. 2005b; Pahl-Wostl 2007; Unwin and Jordan 2007;
Knieling and Filho 2012), storm readiness (Wachinger et al. 2013; Adger
2001), water/river basin management (Cosens and Williams 2012), and fire protection readiness
(Abrams et al. 2015). In light of these challenges, governance requirements for improving
collaboration between sector-administrations, governmental, and non-governmental actors and new
forms of governance must be introduced (e.g., integrated coastal zone management for storm events,
oil spills, etc.) to bolster the ability of each state, county, parish and borough to recover from natural
hazard-related severe events (Crowder et al. 2006; Ramseur 2010; Colten et al. 2012). In CRSI, we
have included three indicators in the governance domain to represent the importance of governance in
resilience to natural hazard events. These are community preparedness, natural resource conservation
and personal preparedness.
Indicator: Community Preparedness
The community preparedness indicator addresses county and community
resilience strengthening and structure hazard mitigation. While there is general
consensus that community resilience is defined as the ability of communities to
withstand and mitigate the stress of a disaster, there is less clarity on the precise
resilience-building process (Chandra et al. 2011). In
other words, we have limited understanding regarding the specific components
that counties and communities can change or the "levers" for action that enable counties and
communities to recover more quickly (although as a screening tool a selection of actions can be
determined). Clearly, community preparedness and planning for such events helps to foster
continuity and stability, defining roles and functions, and how rebuilding of lives, homes, livelihood,
kinship and community will occur (Adger et al. 2005b, Walsh 2007, Linnenluecke et al. 2012).
Structural hazard mitigation is another form of community preparedness. Structural measures are any
physical construction designed to reduce or avoid the possible impacts of hazards. Common
structural preparedness measures could include dams, flood levees, ocean wave barriers, earthquake-
resistant construction and evacuation shelters. The community preparedness indicator in CRSI
includes measures of both county and community resilience strengthening, from Community Rating
System (CRS) information, and structural hazard mitigation, from Small Business Administration
(SBA) recovery mitigation information.
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Indicator: Personal Preparedness
The personal preparedness indicator addresses individual or household
activities that help protect personal property from acute natural hazard events.
Personal preparedness plans run the gamut, including developing a written
plan identifying risks, access to facilities and functional needs, protection of
children and the elderly, shelter plans and caring for pets (Paton and Johnston
2001). While ideal measures, CRSI does not include measures to address all
these issues because nationally consistent data are lacking. Instead, CRSI
targets two major personal preparedness actions that protect property; namely, availability and
coverage of homeowner's insurance and participation in the National Flood Insurance Program
(NFIP). Flooding is the most common natural hazard, but many home insurance policies do not cover
natural or climatic event flooding. In 1968, Congress created the NFIP to fill this void by providing
flood insurance protection to property owners. Insurance or insurability relates to numbers of
structures/property that are insured (which can initiate recovery through an infusion of cash to start
rebuilding) (Cutter et al. 2009).
ft
Indicator: Natural Resource Conservation
The natural resource conservation indicator addresses the protection of natural
resources from anthropogenic activities. Protected natural ecosystems are usually
better able to recover from acute natural hazard events (Tompkins and Adger 2004,
Strickland-Munro et al. 2010). Natural resource conservation management refers to
the management of natural resources (i.e., ecosystems)
with particular focus on how management affects the quality of life for both present
and future generations as well as the sustainability of the ecosystem itself. The Community-Based
Natural Resource Management (CBNRM) approach combines conservation objectives with the
generation of economic benefits for counties and communities (Kellert et al. 2000). A limitation of
using the CBNRM relates to the difficulty of reconciling and harmonizing the objectives of
socioeconomic development, diversity protection, and sustainable resource utilization. The issue of
biodiversity conservation is regarded as an important element in natural resource management as well
as in recovery potential from acute natural hazard events. The CRSI's use of natural resource
conservation indicator related to biodiversity land protection (Land Protection Priority Index for
preserving biodiversity) targets the use of conservation protection by states/counties/communities.
2.4.3. Society Domain
The concept of society, as used in CRSI, includes all human aspects of a
community except the built environment. These are the constructs that
represent the economic, demographic, and social interactions common to all
urban and rural populations. Society is a group of people involved in persistent
social interaction or a large grouping of people sharing the same geographical
or social territory. These groups typically are subject to some political authority
and often similar dominant cultural expectations. More broadly, a society may
be characterized as an economic, social, industrial
or cultural infrastructure made up of, yet distinct from, a collection of individuals. Thus, society can
include the objective and subjective relationships people can have with the material world and other
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people. Proposed society indicators in CRSI include demographics, economic diversity, health
characteristics, labor and trade services, safety and security, social cohesion, social services and
socio-economics Balbus and Malina 2009).
Indicator: Demographics
The demographics indicator includes aspects of vulnerable populations. Demographics
of a county or community reflect attributes of the county's or community's general
population; namely age structure, ethnicity, and socioeconomic levels. All these
factors can influence the ability of a county or community to recover from a disaster
(Lugo 2000; Vasques-Leon et al. 2003; Heltberg et al. 2009; Ibarraran et al. 2009;
Steinbruner et al. 2013). Vulnerable
populations represent those fractions of the population that may be particularly susceptible to impacts
resulting from acute natural hazard events. The vulnerable populations include:
proportion of the population that is 65 years or older and living alone
enclaves isolated by language (Non-English-speaking populations)
groups of persistent homeless persons/families
proportion of the population under the age of 5 years
Indicator: Economic Diversity
The economic diversity indicator represents factors associated with economic
stability and recoverability. Economic diversity addresses issues associated with
a society's ability to monetarily respond and recover from a natural hazard event
(Klein et al. 2003, Linnenluecke et al. 2012). Economic diversity relates to the
array of business sectors a county or community might have and the equitable
distribution of economy. Lack of business
sector diversity can suggest a more difficult path for economic recovery (Adger
et al. 2005a; Reusch et al. 2005; Adger 2010). Employment and employment conditions can be
important for a county's or community's recoverability.
The economic diversity indicator is represented by two indices - the Gini Index (Gastwirth 1972) and
the Hachman Index (Hachman 1994). The Gini Index is a measurement of the income distribution of
a county's residents. This number, which ranges from 0 to 1 and is based on residents' net income,
helps define the gap between the rich and the poor, with 0 representing perfect equality and 1
representing perfect inequality. It is typically expressed as a percentage and is often referred to as the
Gini coefficient. The Hachman Index incorporates location quotients, which measure relative
industrial concentration in one area compared to that in another area. Location quotient (LQ) is a
valuable way of quantifying how the concentration of a particular industry, cluster, occupation, or
demographic group in a spatial unit (e.g., region, state, county) compared to the nation. It can reveal
what makes a particular area unique compared to the national average. The Hachman Index is a
measure of economic diversity that compares the industry composition of a state to the industry
composition of the nation by taking the total employment of an industry in a state divided by total
state employment and comparing it to the nation's equivalent.
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Indicator: Health Characteristics
The health characteristics indicator addresses factors associated with healthcare
access, special health vulnerability populations, and specific health problems
related to or exacerbated by acute natural hazard events. The general health
characteristics of a population emphasize conditions associated with greater
vulnerability to natural hazard events such as respiratory or cardiac condition
changes during periods of intense heat; hospitalization conditions requiring
electronic equipment during times of loss
of power during floods, hurricanes or tornadoes; or, injuries or premature death related to extreme
weather events (Greenough et al. 2001; McMichael et al. 2003; McMichael et al. 2006; Melillo et al.
2014). Access to healthcare means the timely use of personal health services to achieve the best
health outcomes; such as, gaining entry into the health care system, accessing a health care location
where needed services are provided and finding a health care provider with whom the patient can
communicate and trust (Ebi 2011, Oven et al. 2012). Healthcare access is represented by a single
measure of the proportion of the county's population with health insurance. Special health-care needs
vulnerabilities represent any individual, group or community whose circumstances create a barrier to
accessing emergency services because of pre-existing health conditions or vulnerabilities. Of concern
are the more than 23 million U.S. residents (roughly 12% of the total population aged 16 to 64 years)
with special health-care needs due to disability (U.S. Census Bureau 2016). This population is
diverse and broadly distributed and deserves special attention because there is an 80% chance that
any person will experience a temporary or permanent disability at some point in their lives (Kailes
and Enders 2007). Specific health problems represent the proportion of a county's population with
special health issues that can be exacerbated by acute natural hazard events. These health conditions
include:
asthma
cancer
diabetes
heart disease • obesity
stroke.
Indicator: Labor and Trade Services
The labor and trade services indicator addresses factors related to recoverability
from an acute natural hazard event associated with construction (Kirrane et al.
2013). In short, does a county or community have the appropriate construction
skills to provide for accelerated recovery and represent a resilient construction
workforce? Skilled construction labor is a segment of the workforce with a high
skill level that creates significant economic value or, in this case, recoverability
through the work performed by human capital. Labor and trade services represent the availability of
skilled labor and tradecraft that can be utilized in the aftermath of a natural hazard event (e.g.,
carpenters, bricklayers, engineers, roofers, construction workers, civil servants). This indicator
includes construction skills (represented by adjusted numbers) relating to:
concrete construction
• framing
highway construction
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masonry
power construction
• roofing
steel construction • water construction.
Indicator: Safety and Security
The safety and security indicator addresses the provisioning of emergency
and civil services. The primary definition of safety is "the condition of being
4 free from harm or risk", which is essentially the same as the primary
¦ . j definition of security, which is "the quality or state of being free from
^ 01 danger." The hierarchy considers safety needs secondary only to basic
physiological needs like food and water. The need for safety has to do with
our natural desire for a predictable, orderly world that is somewhat within our control. In relation to
the development of the CSRI, safety targets the provisioning of the types of emergency services that
would be necessary for a reasonable and rapid recovery from an acute natural hazard event. Safety
and security services encompass the availability of emergency first responders, medical personnel,
civil order, and legal services. Measurements related to these services demonstrate a county's or
community's ability to respond and the timing of that response to the results of a natural hazard event
(e.g., flood, hurricane, tornado, wildfire). The specific emergency and civil services included in the
safety and security indicator include adjusted numbers of personnel associated with emergency
services, law enforcement personnel, law enforcement support personnel and public safety personnel
Christopher and Peck 2004, Keim 2008).
Indicator: Social Cohesion
The social cohesion indicator represents the willingness of members of a
society to cooperate with each other in order to survive and prosper. We
define social cohesion as a society that works toward the resilience of all its
members, fights exclusion and marginalization, creates a sense of
belonging, promotes trust, and offers its members the opportunity of upward
mobility. Social cohesion can be an important element of
recoverability after a natural hazard disaster (Baldwin and King 2018,
Meitzen et al. 2018, Sanchez et al. 2017). It represents community and family-centric networks and
value structures with an emphasis on the characteristics that increase the likelihood of vulnerability
(e.g., sense of place) and/or recoverability (e.g., family and social networks) (Schwartz and Randall
2003; Adger et al. 2005b; Baussan 2015). Social cohesion plays a significant role in the planning for
resilience to acute natural hazard events and in the execution of that planning after an event. The
constituent elements of social cohesion (OECD 2011), include social inclusion, social capital, and
social mobility. Social capital, the resources that result from people cooperating toward a common
end, can play an important role in event. In the CRSI framework, social cohesion addresses access to
social support. The measures of social cohesion include volunteering and volunteer organizations,
ethnic diversity and the proportion of population native to a county or community.
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Indicator: Social Services
The social services indicator for CRSI is represented by a range of public services
provided by government, private, and non-profit organizations. Access to these
services is critical for recovery from an acute natural hazard event and include the
availability of services unrelated to infrastructure, labor/trade, emergency
services and civil control important for a county's or community's response to a
mr natural hazard event (Dominelli 2013). These services would relate to laws,
* childcare, education, healthcare, and faith-
based organizations. In the CRSI framework, this indicator is represented by:
index depicting the average medically underserved population
number of blood and organ banks in a county relative to the county's population
access and availability of childcare facilities
number of emergency shelter and goods providers in a county relative to the county's
population
number of food service providers in a county relative to the county's population
number of hospitals in a county relative to the county's population
number of insurance claims in a county relative to the county' s population
number of educational facilities in a county relative to the county's population and support for
those facilities
mental health services
percent of the county population living in a health professional shortage area (HPSA)
number of physician services in a county relative to the county's population
number of rehabilitative services in a county relative to the county's population
number of religious organizations in a county relative to the county's population
number of social advocacy facilities in a county relative to the county's population
number of special needs transportation facilities in a county relative to the county's
population.
Indicator: Socio-Economics
The socio-economic indicator for the CRSI society domain relates to
= employment opportunity and issues associated with personal economics, primarily
level of income. Employment opportunity is represented by overall county-level
unemployment rate. Employment and employment conditions can be important for
a county's or community's recoverability. This indicator would include metrics
like unemployment rates, underemployment rates and the formation of human
capital (Marston 1985; Cohen 2011; Peiro et al. 2015). Personal economics relate
to personal finances and involves all financial decisions and activities of an individual or household.
The most basic of these activities is income, both actual income and relative income. For the socio-
economic indicator, personal economics is represented by three measures: the proportion of a
county's population that earns less than 150% of the poverty guidelines for a specific household size,
county unemployment rate and the median income for the county.
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2.4.4. Built Environment Domain
The concept of a built environment is relatively recent, and it was initially
coined by social scientists (Rapoport 1976). The "built environment"
describes the man-made surroundings that provide the setting for human
activity, ranging in scale from buildings and
greenspaces to neighborhoods and cities. The scope of the built
environment typically includes supporting infrastructure such as water
supply, energy networks and transportation corridors. The built environment
is a material, spatial and cultural product of human labor that combines
physical elements and energy in forms for living, working and playing (Roof and Oleru 2008). In
recent years, public health research has expanded the definition of "built environment" to include
healthy food access, community gardens, "walkability" and "bikeability" (Lee et al. 2012). The urban
fabric is a complex socio-technical system that encompasses different scales - buildings, building
stocks, neighborhoods, cities and regions - each with different time constants, actors and institutional
regimes. The term "built environment" has also been adapted to address the relation between the built
and the "unbuilt" part of the environment. This corresponds to the definition of a socioecological
system where the "built environment" can be considered an artifact in an overlapping zone between
culture and nature, with causation occurring in both directions. The sustainability debate and the
growing awareness of risks to the built environment due to natural hazard change and natural hazard
events have all helped to focus attention on the fragilities and the need to create resilience in the built
environment (Hassler and Kohler 2014). In CRSI, we have included five indicators in the built
environment domain to represent the importance of built environment in resilience to natural hazard
events; communications infrastructure, housing characteristics, transportation infrastructure, utility
infrastructure, and vacant structures.
Indicator: Communications Infrastructure
Continuity of communications is the ability of a county, community or organization
to execute its essential functions at its continuity facilities. This continuity depends on
the identification, availability and redundancy of critical communications and
information technology systems to support connectivity among key government
leadership personnel, internal elements, agencies, critical customers and the public
during crisis and/or disaster conditions Martins et al. 2017, Wang and Wang 2017,
Zimmerman 2017). The communications infrastructure indicator primarily addresses a county's or
community's communications continuity in the aftermath of an acute natural hazard event. This
indicator encompasses the number and distribution of:
cell phone towers
land mobile towers
microwave towers
paging towers
radio broadcast towers
TV transmission towers • areas of no internet coverage.
it
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Indicator: Housing Characteristics
Housing characteristics relate to the types of households distributed throughout a
county and their structural vulnerability. Structural vulnerability is a distinct
likelihood of encountering major difficulties within the county or community
atmosphere or the threat to the county or community itself because of deficient
housing or building conditions. While this concept applies to engineered
structures and the meeting of building codes and requirements in order to sustain
acute natural hazard events, the primary issue in the indicator is physical structure (e.g., buildings),
the construction of which usually has not been through the formal building permit process. Such
buildings are obviously prevalent in the rural or non-urban areas along the periphery of
municipalities. These types of constructions also include old historic buildings. Structural
vulnerability generally pertains to the structural elements of building, e.g., load bearing walls,
columns, beams, floor and roof. The structure vulnerability indicator in CRSI addresses issues of
home overcrowding, age of home, housing unit density, major home construction and functional
problems and number of mobile homes in a county or community (Cutter et al. 2008, Dominelli
2013, Henstra2012, Smoyer 1998).
Indicator: Transportation Infrastructure
Transportation infrastructure refers to the framework that supports our transport
system. This includes roads, railways, ports and airports. National and local
governments are responsible for the development and maintenance of our transport
infrastructure. Transportation infrastructure is the fixed installations
that allow vehicular traffic to operate. Transport is often a natural monopoly and a
necessity for the public and a critical element of community infrastructure in the event of an acute
natural hazard event or the recovery from such an event Linnenluecke et al. 2012, Wedawetta et al.
2010). In the CRSI index, the transportation infrastructure indicator is represented by transportation
flow continuity including:
access to highway entrances and exits
number of and access to airports
number of and miles of arterial roads in a county
collector road lengths
freight railroads
heliports
miles of local roads in a county
roadway bridge access
roadway bridge structures in a county
seaplane bases.
One reviewer questioned the absence of public transit as a metric in this indicator. We agree with the
potential importance of public transit to resiliency from an acute natural hazard event. However,
public transit was not included in the Transportation Infrastructure indicator for two reasons; one
technical and one practical. The technical reason is that public transit is no more part of
transportation infrastructure than automobiles. Transportation infrastructure consists of the fixed
installations supporting transportation (e.g., roads, railways, terminals). The practical reason is that
we considered the inclusion of public transit as a separate indicator but, while there was adequate
A
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data for the topic in metropolitan areas, data was sparse or non-existent for nonmetropolitan areas
which make up the bulk of the U.S.
Indicator: Utilities Infrastructure
Public utilities are organizations that produce, deliver and maintain the infrastructure
for supporting public access to critical public health services and power. Robust utility
networks are essential for promoting quality of life during the disaster recovery process
4 (Ma et al. 2018, Panteli and Mancarella 2017, Zimmerman 2017). Utilities networks
are one of the most protected resources within any county or community, but areas that
are sparsely populated may lack any redundancy or rerouting options should the main utility
service(s) be compromised as a result of an adverse natural hazard event. Within CRSI, the utilities
infrastructure indicator describes the relative availability of drinking water, sewer and power services
based on number and location.
Indicator: Vacant Structures
Vacant structures (residential and non-residential) are generally at greater risk to an
acute natural hazard event than occupied structures. This vulnerability is often due to
a lack of maintenance, general deterioration and/or owner disinterest. Although not
related to acute natural hazard events, these structures are also a matter of increasing
concern for fires. For example, Cleveland is
plagued by over 12,000 vacant structures including houses, blighted buildings,
schools, former manufacturing plants and forgotten warehouses. The issue is of such concern to
Detroit (with over 78,000 vacant structures), that the city has demolished nearly 12,000 structures
since 2014 resulting in a 25% reduction in vacant structure fires over the past two years (Helms
2016). By removing dangerous vacant buildings and empty houses, safety and quality of life in
Detroit is improved. These types of buildings are particularly vulnerable to acute natural hazard
events. The CRSI vacant structures indicator includes the number of vacant business structures in a
county, the number of vacant residences in the county and the number of other vacant buildings in the
county (e.g., hospitals, schools, government buildings).
2.4.5. Natural Environment Domain
The natural environment is a domain that encompasses all living and nonliving
things, occurring naturally in the United States. The concept of natural
environment can be distinguished by two primary components: 1) complete
ecological units that function as natural systems without extensive human
inventions (often called ecosystems) and 2) universal natural resources and
physical phenomena that lack clear-cut boundaries (e.g., air, water, climate,
radiation, magnetism) not originating from anthropogenic activities. In this
domain the natural environment is represented by two indicators - the extent of ecosystem type and
condition of natural ecosystems and managed lands. Open space and green space are included in
appropriate
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Indicator: Extent of Ecosystem Types
CRSI addresses the resilience of natural ecosystems as well as the resilience of
developed lands and dual-purpose lands. The extent domain is necessary to
gauge resilience on the proportion of land that is undeveloped and includes the
spatial extent or acreage of each ecosystem type that occurs naturally without
any significant human intervention (Adger et al. 2005, Foley et al. 2005, Smit et
al. 2000). Some of these measures include:
wetlands
forested areas
deserts
aquatic areas or "blue space"
grasslands
tundra.
Indicator: Condition
CRSI addresses the resilience of natural ecosystems as well as the
resilience of developed lands and dual-purpose lands. The condition
domain is necessary to gauge the original condition of the proportion of
land types or ecosystems that is undeveloped and includes an assessment
of the ecological condition of each ecosystem type that occurs naturally
without any significant human intervention (Foley et al. 2005, Stenseth
et al. 2002, Walther et al. 2002). This condition estimate is based on surveys completed by EPA's
Office of Water (USEPA 2017) and Office of Air and Radiation (USEPA
2016a), USDA's Forest Service (USFS 2017) and Natural Resources Conservation Service (NRCS
2017a, b). The condition indicator is related to metrics that describe the following ecological
conditions in natural communities and resources:
• biodiversity
Conditions of aquatic ecosystems
condition forests condition
air condition • soils condition.
Comparison of Differing Versions of CRSI
An earlier version of the CRSI framework was published as a conceptual model (Summers et
al. 2017). The earlier conceptual model included five sub-models (risk, governance, social,
built environment and natural environment), eleven domains and 25 indicators. The authors
believed after further investigation that the domains and the indicators were largely
duplicative. In order to maintain the structural integrity of the earlier index framework, the
five sub-models were renamed domains. The original domains and indicators where
combined to create a single set of well-rounded indicators. These changes did not
significantly alter the structure of CRSI but rather introduced a different nomenclature to
simplify the CRSI structure.
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2.5. Metric Selection and Data Sources
A candidate list of potential metrics was identified based on existing literature and expert opinion.
The inventory of metrics was largely driven by the relevancy for measuring natural hazard events and
natural hazard impacts, ecological connections of natural systems to built and natural environments
and how well the sets of metrics fit as "proxies" for respective indicators. Metric redundancies across
the literature were encountered. Over 600 metrics were described in the literature, many of which
were duplicative. Based on the data acceptance criteria and other approaches such as autocorrelation
analysis, duplicate measures review, etc. the candidate list of metrics was distilled through group
consensus and expert counsel. Only the most robust metrics were retained for quantification. Data
acceptance criteria are described as follows:
To the extent possible, data sources were selected based on the following criteria:
Availability and access: The data are publicly available and easy to understand, access and
extract.
• Reliability and data credibility: The data owners collected data in a manner that is vetted by
the professional community and have metadata available for review.
Spatial preference: County-level data is preferred spatial unit for population-based
information and acres, meters, hydrologic units or similar for geospatial units.
Coverage: Nationally consistent in scope.
Chronological history and the likelihood that the data will continue to be collected: Data
exhibit a consistent collection history from 2000-2015.
Types of Data: Subjective and/or objective data specifically relevant for development of
CRSI.
Table 2.5 offers a brief overview regarding the indicators and general description for interpretation.
Detailed metric information is located in Appendix A.
2.6. Data Handling and Standardization
Acquired raw data used to populate CRSI metrics are maintained as an archive in their original
format to help ensure data transparency. Metric data are derived from raw data, are stored in plain
text format (e.g., ASCII) and are organized in hierarchical or nested structures that match the CRSI
conceptual framework. This data structure allows each level of CRSI data, from raw data to final
scores, to be examined either individually or as a whole. The plain text format makes the data not
only more available to a variety of softwares (e.g., ESRI ArcGIS®, SAS®, R, JavaScript), but also
makes the data more readable.
Table 2.5 List of CRSI domains, indicators, scope and number of metrics. Numbers in
parentheses for domains show the total number of indicators/total metrics in the domain.
Domain
Indicators(s)
Metric(s)
Built
Communication Infrastructure
Communication continuity (7)
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Domain
Indicators(s)
Metric(s)
Environment
(5/24)
Housing Characteristics
Structure vulnerability (5)
Transportation Infrastructure
Transportation flow continuity (6)
Utility Infrastructure
Utility Continuity (3)
Vacant Structures
Structure vulnerability (3)
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Domain Indicators(s) Metric(s)
Community Preparedness
Community resilience strengthening (2)
Governance XT + t d r +
Natural Resource Conservation
(ys,\
Natural resource recovery (1)
\yU) h
Personal Preparedness
Personal property hazard protection (2)
Condition
Natural
Biodiversity, using birds as a proxy (1)
Coastal condition (1)
Forest condition (1)
Inland lake condition (1)
Percentage of clean air days (1)
Rivers and streams condition (1)
Soil growth suitability (1)
Soil productivity (1)
Wetlands condition (1)
Environment
(2/18)
Extent of Ecosystem Types
Agriculture area (1)
Forested area (1)
Grassland area (1)
Inland surface water area (1)
Marine/Estuarine area (1)
Perennial ice/Snow area (1)
Protected areas (1)
Tundra area (1)
Wetland area (1)
Risk
(2/20) Exp0Sure
Earthquake probability (1)
Extreme high temperature incidents (1)
Extreme low temperature incidents (1)
Flood probability (2)
Hailstorm probability (1)
Tornado probability (2)
Hurricane probability (2)
Landslide probability (1)
Major toxics presence (1)
Non-storm damaging wind incidents (1)
Nuclear presence (1)
RCRA sites (1)
Superfund sites (1)
Toxic release presence (1)
Wildfire probability (1)
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Domain Indicators(s) Metric(s)
Loss
Developed area loss (includes human and
property measures) (1)
Natural area loss (1)
Dual-benefit area loss (includes cropland and
managed area measures) (1)
Demographics
Vulnerable population (5)
Economic Diversity
Economic stability/recovery (2)
Health Characteristics
Health problems that may impact personal
resilience (9)
Society Labor and Trade Services
Construction recovery (8)
(8/50) Safety and Security
Provisioning of emergency and civil services
(4)
Social Cohesion
Access to social support (4)
Social Services
Access provisioning to critical services (15)
Socio-Economics
Employment opportunity (1)
Personal economics (2)
A team consensus approach was used to rate every candidate metric as to whether it was or was not a
valid measure for a specific indicator. A final comprehensive review of the pool of indicator metrics
was performed to identify potential data sources. If data for a metric could be obtained from two or
more data sources, then a single source for the metric data was chosen based on the data acceptance
criteria. Metric data were averaged across all years of available data. Any remaining data gaps were
not imputed for count data, as a rule. Where missing data existed and were not expected (e.g.,
wetlands condition, scored indicator) then missing value was set to null. If missing data represented a
metric where a zero was meaningful, the missing value was set to zero. For geospatial data
interpolation methods were used to fill in missing data. The interpolation method varied by metric
depending on measurement—aerially-weighted, modeled, etc. Box-and-Whisker analyses were
completed for each fully enumerated CRSI metric. Extreme lower and upper outlier measures were
set to minimum and maximum values, respectively. The maximum values were calculated to be three
times the 75% percentile for each metric and the minimum values were calculated as minus three
times the 25% percentile. Any outliers of this three times maximization technique were set to the
metric value closest to the fence (Baum et al. 1970). Except for measures presented in percent or
proportion, data were standardized on a scale from 0.01 to 0.99 using a min-max normalization
process as follows: (p )"= ((x - xmin)/(xmax-xmin)). The resulting CRSI metric data set included
measured, modeled and filled standardized data for the 3,135 counties of the U.S. Approximately 1.3
million metric data points were extracted and synthesized to quantify CRSI indicators.
2.7. Calculations
2.7.1. Built Environment, Governance, Natural Environment and Society Domains
Four basic steps were used to summarize metrics to domains (Figure 2.4), except for the Risk domain
which will be discussed separately. Indicators and domains were derived using the following
approach:
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• Metric data were adjusted for age, population or spatial area, as appropriate, prior to
standardization (e.g., number of hospitals in a county adjusted by the population of the
county). Count information contributing to continuity measures were not weighted.
Average of related standardized metric values served as the basis for indicator scores •
Domain scores were obtained from the sum of appropriate standardized indicator values.
Domains for built environment, natural environment, society and governance were
standardized in preparation for the final CRSI calculation.
Multiple Scale Metric, Indicator and Domain Score Calculations
(Governance, Society, Built Environment, Natural Environment Domains Only)
County
Unnct •Jrcuvry
twreslnU-S.
ndceo'icaiB nU 5
UtaoofBiwiKy
dDnunxnrai nUS
Figure 2.4 Representation of the Metric, Indicator and Domain scores for Governance, Society, Built
Environment and Natural Environment Domains of CRSI. For this report, aggregations were made at
the EPA regional scales and national scale. Similar aggregations could be accomplished at any
appropriate scale (e.g., western regions, intermountain regions, coastal regions).
2.7.2. Risk Domain
The Risk domain is a probabilistic calculation based on geophysical and technological exposure
and loss described in Buck et al. 2017. The components include historical exposure, basic
likelihood of exposure factor, anthropogenic exposure, and human, property and natural
ecosystem losses. All metrics were min-max standardized. A sum of metric values representing
incidents of past natural hazard events and exposure likelihood for each county, parish and
borough was used as the basis for calculating metric scores for the Exposure Indicator. The Loss
indicator was derived from the sum of loss metric scores identified as three land type categories-
natural, developed and dual use. The domain measures were calculated as the standardized
product of total exposure divided by total loss. The approach used to calculate the Risk domain
scores is presented in Figure 2.5.
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U.S.
Risk Domain
Score
Metric, Indicatorand Domain Score
Calculations for Risk Domain
Figure 2.5 Representation of the Metric, Indicator and Domain scores for Risk Domain of
CRSI.
2.8. The Final Steps to CRSI
All domains for each county, parish and borough (all referred to as county below) were min-max
standardized on a scale from 0.01 to 0.99. The final CRSI calculation begins as a scaled value for
recoverability/ vulnerability derived from Governance and Risk (basic CRSI) with the Governance
value being adjusted by the remaining domain scores for social, built environment and natural
environment to complete the calculation of CRSI as shown below:
CRSI(B), = R/y = Gm)
Risk
where CRSI(B)i = value of basic resilience (Recovery/Vulnerability or Rj/YY) and Ri/V.: Governance
in county i/Risk in county i. The overall CRSI score is calculated as:
CRSL = (Go\'i + Soc(a)iGovi + BE(a)iGovi + NE(a)iGovi) / Risk,
where CRSIi = the value of CRSI or adjusted resilience for county i and Soc(a)i, BE(a)i, and NE(a)i
are the adjustment multipliers for Society, Built Environment, and Natural Environment in each
county i, and Riski is the Risk score for county i.
The adjustment factors are calculated as follows:
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5oc(a> = ^i_&Cm^oC)
¦m
where Soc(a)i is the adjustment multiplier for society in county i, Soci is the social domain score for
county I and Socm is the median social domain score for all counties;
where BE(a)i is the adjustment multiplier for built environment in county i, BEi is the built
environment domain score for county I and BEmis the median built environment domain score for all
counties;
and where NE(a)i is the adjustment multiplier for natural environment in county i, NEi is the natural
environment domain score for county I and NEmis the median natural environment domain score for
all counties.
The calculation process is depicted pictorially in Figure 2.5. The domains are weighted equally in the
calculation of CRSI in this report. By no means do the domains have to be weighted equally. If
communities or counties have specific data to inform CRSI then weights can be added to the final
CRSI calculation based on local priorities regarding the domain issues. Even if, no new data is added,
domains can be weighted based on local knowledge of priorities.
2.9. Uncertainty Analysis
Uncertainty analyses is recognized as an important step in the presentation of new index frameworks.
For CRSI, this analysis will be completed as part of Next Steps for further development of the index.
2.10 Technical Soundness of Approach
The approach used for CRSI is based on a basic method of creating an index to describe current
condition and to be used as a screening tool to determine locations in need of improvement to
increase resilience to acute natural hazard events. The use of metrics to develop indicators and
indicators to develop domains is a standardized approach. The selection of indicators and metrics
based on scientific and social literature adds to the technical soundness of the approach. However,
there are also limitations. Literature and team technical evaluation suggested several metrics that
would be useful in representing the indicators but the decision to develop CRSI at the county level
made the use of these metrics impossible as the data representing the metrics do not exist at the
county level. Would CRSI likely be improved by their inclusion? Probably. Limitations of data
always reduce the power of indices but using the available data certainly provides a screening level of
accuracy for CRSI. Reviewers were asked specifically, "Is the approach for the index technically and
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conceptually sound?" Their responses provide a better measure of the soundness of approach than
anything the authors could add:
"Fes, the general technical approach is based on a
familiar and common one, in use for several decades
to develop indices and compare components in a way
to describe the current condition and help
stakeholders identify areas to investigate for
potential management actions/decisions (i.e.,
conceptually sound). Polar plots, scatter] jnked
lists, maps, and examples by county and >
present a useful array of ways to engage
stakeholders, A key area in my opinion is Figure 3,1
(now Figure 4.1) where the scatterplot is compared
to a 45-degree line -1 think the discussion and the
figure can be made more impactful by explicitly
drawing ellipses/circles on the plot itself and
indicating potential management decisions/actions, I
do think this document is very clear about distilling
and clarifying new thinking around climate
resilience, I did not identify any flaws,..."
—Bruce Duncan Region 10
"The literature review/synthesis is a good approach
that takes advantage of existing work," —Megan
Sussman, Office of Sustainable
Communities
"Yes, Well written and succinct, I • graphic on
pg, 3," (Figure 1-1) —Laura Farris, :i 8
One reviewer felt the report suffers from conceptual under explication as well as a lack of
transparency in the operationalization of concepts in the form of measures (Dr. Courtney Flint, Utah
State University). While some reviewers felt selected indicators needed further explanation, most
appreciated the level of explanation.
The reviewers were also asked: "Do the methods, results and discussion sections adequately describe
the index development approach?" and "Are tables and graphics helpful?". Again, their responses
provide more information than the authors can provide through additional explanation.
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In addition, the responses suggest that the level of detail and explanation is sufficient for EPA
Regional staff.
"Fes, from my point of view,"
'"Resilience Graphics and Tables clear,"
-Joyce Stubblefield, Region 6
"Overall, yes, it is s forward to follow the
developmental apf. and to see how additional
information could he incorporated as new nationally
accessible knowledge is developed, I do like the
discussion here for a region - the overall comparison
and then differences within the region by location
and by domain is a good approach. However, as
much as I appreciate the evaluation by region, I think
there may be some other constructs, but maybe not
for this effort. States and Tribal lands come to mind
as being particularly useful unless you think county
governance tends to outweigh state governance,
which it probably does when it comes to land use
decisions, I think your discussion around page 35 or
so is very helpful example of how the index can be
used,"
-Bruce Duncan, Region 10
"Yes, I really like the graphic on pg, 12." (now Figure
E-2)
-Laura Farris, Region 3
"As for adequately describing the index development
approach, my biggest concern is that the
operationalization is still a bit of < box."
—Courtney Flint, Utah State Unive
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3. How to Use CRSI - Its Utility and Potential
Applications
3.1. Introduction
The potential for use of the Cumulative Resilience Screening Index (CRSI) is very broad and with
additional localized data additions, even broader. Below, the potential uses are outlined by spatial
unit (nation, region, county, community) and a few relevant specific examples (e.g., specific risks,
specific county comparisons with similar circumstances but different CRSI scores, and comparisons
of EPA Regions) are discussed.
Categories of purposes/uses of CRSI include:
Describing the state of the condition of resilience at the county level and aggregated levels above the
county (i.e., the minimal intended use of the index)
Providing a framework that might be useful for communities to expound upon the county information
using county- or community-specific data to create county- or community level resilience scores
Identifying areas for management/action decisions
Tracking changes over time at the county, state, region, and national levels (potential use that would
really need more research on which elements respond on what time scales to management decisions)
Improving/further developing/vetting the index (i.e., ORD furthering the research in response to
stakeholder identified uses)
The multiple application options discussed below address the utility of CRSI's extension to
community decision makers, planners and other potential stakeholders.
3.2. General Broad Use
CRSI is not intended to be "run" based on the information in this report. CRSI has been run and its
results provided for all counties in the United States (except for a few boroughs in Alaska and no
counties in U.S. Territories) in this report. Users at this point will simply apply the results that are or
can be easily provided (e.g., CRSI scores, domain scores, listings, plots of contributions to CRSI
score, maps). For any other reasonable information at the county scale and higher, readers can
contact the authors of this report and get most information. These available results can provide broad
scale comparisons of large areas across the United States. For example, at the national level, the
Appalachians, deep South and much of the West Coast states show relatively low governance
associated with natural hazard events and higher than average levels of risk to those natural hazard
events. The western states show higher CRSI scores than most of the U.S. (i.e., higher resilience)
even though its governance levels associated with acute natural hazard events is lower than much of
U.S. However, the scores associated with built environment and natural environment are higher than
much of the U.S. offsetting the minimal levels of natural hazard event governance. This increases a
low to moderate base resilience score
(governance/risk) to a moderate to high CRSI score due to strong building codes, lower level of
vacant structure, large areas of preserved and conserved lands, and higher levels of insured
homeowners.
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On a broad scale, EPA regions can be compared to assess which regions (based on the mean of
county CRSI scores) have higher levels of resilience and which regions have lower levels. EPA
Region 3, 4 and 6 have lower overall levels of natural hazard event resilience based on CRSI scores.
This does not mean that all counties in these regions have low scores. In fact, in Region
3, areas in northern Pennsylvania and in Maryland and Virginia on the lower shores of Chesapeake
Bay have among the highest CRSI scores in the U.S. and can serve as models with valuable
"lessoned learned" for areas of West Virginia with considerably lower CRSI scores. By
disassembling the county CRSI scores, counties with low CRSI domain scores can learn from
counties with higher scores. Similarly, in Region 4, counties with low governance scores related to
natural hazard events often show moderate to high risk to natural hazard events scores. The Region
can determine which counties need particular assistance in becoming more resilient to natural hazard
events. In Region 6, a region that lists enhancing resilience to natural hazard events as a major goal,
lower than average CRSI scores are seen along the Gulf Coast with very high-risk scores in Harris.
Brazoria, Jefferson and Chambers counties and low governance scores in all of these counties except
Harris County. All of these counties have been major flood victims of Hurricane Harvey. While
Harris County a reasonable level of governance associated with natural hazard events, it's natural
environment score is very low resulting in a diminution of the governance score (i.e., Harris County
has been developing much of its natural acreage leaving small amounts of natural ecosystems to help
ameliorate flooding conditions). Aransas County, second landfall of Hurricane Harvey, has a lower
risk score with reasonable governance; however, that governance is diminished by a very low built
environment score suggesting large numbers of vacant structure, on the whole, older buildings and
poorer overall infrastructure for utilities, communications and transportation. These low built
environment domain scores suggest that, if the area experienced a major natural hazard event, the
county would be a risk to broad scale destruction (as was evidenced in Rockport, TX).
Finally, individual counties can use CRSI scores on a broad scaled to determine nearby or similar
counties with better domain scores - finding counties which can be consulted for "lessons learned".
Counties may even be able to use CRSI scores and domain scores to pursue federal or state funding
for improvement. Most utility benefits for counties and communities are shown below in Section
3.5.
3.3. Use by EPA Regions
This report provides CRSI and domain score information for all counties within an EPA region
specifically to allow the regions to assess natural hazard event resilience at the spatial scales of use to
them rather than at the national level. The results by Region are shown as composites of the national
scores (i.e., average county scores from within the region but based within a national context. This
means that counties have the same CRSI and domain scores in the regional analysis as they do in the
national analysis. This permits direct comparison of EPA Regions and counties within the regions.
While direct regional comparisons may have limited value from some regional perspectives. It does
allow Program Offices (see below) to assess comparative regional trends and allows Regions to
locate other Regions with higher scores for CRSI or the domains to be assessed as models for
improvement. Regional analysis does permit comparisons of the specific counties in their Region and
allows the delineation of county CRSI and domain scores to ascertain which counties are in the most
need of assistance in selected domains or overall resilience to natural hazard events. For example,
EPA Region 4 has a low overall CRSI score due to a number of counties in Alabama and Mississippi
with relatively high risk and low governance for natural hazard events. Similarly, these counties also
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have low society and built environment domain scores further reducing the impact of natural hazard-
related governance. In short, the counties have minimal governance related to natural hazard events
and, if an event were to strike, these counties do not have the composite skill mixes and demographic
characteristics to ensure recoverability. To exacerbate the situation, these counties often have large
numbers of vacant structures, less stringent building codes, and older public infrastructure.
Examining these attributes of the CRSI and domain scores permits Region 4 decision makers to
determine those counties most in need of assistance in developing their resilience to natural hazard
events. EPA Regions can ascertain which counties in their jurisdictions are most at risk to natural
hazard events overall as well as to individual natural hazard event types. The Regions can also
determine which high-exposure or moderate-exposure counties have their risk levels elevated due to
the proximity of technological hazards. Harris County, Texas's low risk domain score is the product
of the combination of natural hazard exposures and multiple technological hazards (e.g., Superfund
sites, RCRA sites, petro-chemical plants). Unfortunately, this exacerbation of risk has proven true in
the aftermath of Hurricane Harvey in the Harris County metropolitan and suburb area of Houston
with multiple explosions and fires at these types of technological hazards. Similarly, Regions can
ascertain the major contributors to CRSI scores at the Regional level as well as the county level.
3.4. Use by EPA Program Offices
EPA Program Offices are most concerned with the establishment of policies and programs across the
nation and, as such, are less interested in individual county information. However, Program Offices
are interested if whole regions of the United States show relatively poor resilience to natural hazard
events and if certain areas of the country demonstrate high exposure to natural hazard events in
conjunction with high exposure to technological hazards addressed by EPA. EPA's Office of Land
and Emergency Management (OLEM) has a special interest in this union of natural hazard event
exposure and technological hazards (e.g., Superfund, RCRA, active waste sites) in its development of
guidance and technical assistance to establish safe waste management practices. Knowing the
juxtaposition of counties at risk and placement of technology hazards is useful to OLEM for both
guidance and organization of clean-up activities resulting from a major waste event.
EPA's Office of Water (OW) ensures that drinking water is safe and restores and maintains watershed
and ecosystems to protect human health, support economic and recreational activities, and provide
healthy habitats. Drinking water issues were a major problem in the aftermath of Hurricane Katrina
and is a major continuing issue in several major Texas cities as a result of
Hurricane Harvey. Wildfires can be a major source of watershed devastation, particularly in the
West as evidenced by the magnitude and spatial spread of fires during late summer 2017 in
California, Arizona, Oregon and Washington. Earthquakes, prominent in the West, can also be source
of modified drinking water as well as infrastructure destruction. Through interactions with the ten
EPA Regions, state and local governments and American Indian tribes, OW helps to build capacity
and resilience for water resources.
EPA's Office of Air and Radiation (OAR) is concerned with air pollution prevention, radiation
protection and natural hazard change issues among many other issues. Knowing the juxtaposition of
counties with high natural hazard event risk exposure with radiation producing facilities (e.g., nuclear
power plants) and chemical producing facilities could be important data for the Office of
Atmospheric Programs (OAP). The interaction of natural hazard change indicators and natural hazard
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event exposure rates as well as recovery rates for regions of the United States could also be of
importance.
EPA's Office Community Revitalization (OSR) supports locally led, community-driven efforts to
revitalize local economies and attain better environmental and human health outcomes contributing to
community sustainability and resilience. Knowing which counties (and communities) display lower
resilience to acute natural hazard events could be an important factor in evaluating which resources
are placed to contribute to clean air, clean water, and other important resilience goals of communities
and counties. OSC is also interested in the development of tools, research and case studies that
promote understandings of resilience and sustainability. Finally, the use of shared examples among
counties and communities (learning from each other) in order to provide models of behavior and
action is one cornerstone for OSC.
3.5. Use by States, Counties, Metropolitan Areas and Communities
The use of CRSI results or CRSI modification is important at the state, county, metropolitan area, and
community level. While one could argue that every community is different with regard to its likely
exposure to acute natural hazard events, governance associate with natural hazard events and its
resilience to natural hazard events, it is clear the counties in much of the United States can set the
tone, guidance and often specifics for emergency operations plans, and emergency response to
disaster recovery and hazard mitigation (FEMA 2011) even those developed at smaller spatial scales.
Emergency and disaster planning involve a coordinated, cooperative process of preparing to match
urgent needs with available resources (Alexander 2016). For successful responses to acute natural
hazard events, there must be high levels of coordination and continuing cooperation among, federal,
state, county and community infrastructures (Plough et al. 2013).
Many states develop basic disaster management plans and require counties to develop comprehensive
emergency management plans and county emergency management programs that must comply with
the basic plan. Counties often engage with larger communities in the same manner. However, in
many cases smaller communities (without significant resources) simply adopt the county plan and
jointly administer the plan in their jurisdictions. For example, Florida (a state with significant acute
natural hazard event risks) has established a Comprehensive
Emergency Management Plan (CEMP) (FDEM 2016) as the master operations document for the State
of Florida establishing a framework through which the state handles emergencies and disasters. The
CEMP consists of a basic plan which describes the process for preparedness, response, recovery and
mitigation and provides local CEMP compliance criteria (CEMP-001). In the vast majority of
counties, the County CEMP drives these activities in all communities within the county. The
exceptions are in counties with large metropolitan areas (e.g., Miami, Tampa, Orlando, Jacksonville)
which will have their own CEMPs that are required to meet the county criteria. Thus, the county
governmental unit becomes a major actor in the resilience of counties and communities to acute
natural hazard events. As a result of this necessary cooperation at all levels of government for
satisfactory resilience to acute natural hazard events, counties are often the central focus of specific
disaster planning and preparedness for all towns, communities and jurisdictions within the specific
county. This is the case in Pensacola, FL where responsibility for this type of preparedness and
planning and the execution of emergency management falls to Escambia County. Similarly, in
Rockport, TX (the site of the second landfall of Hurricane Harvey), one of the primary actors in
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emergency planning and response in Aransas County, TX. Therefore, in the majority of cases
throughout the U.S., collection of data at the county-level in appropriate and can be augmented by
specifics associated with the individual community affected by the event.
CRSI and domain scores at the county-level permit state, county and community planners to
ascertain, risks to natural hazard events in their jurisdictions, likelihood of recovery from such an
event (resilience), the likely causes of low levels of recovery, and the identification of counties in
similar circumstances (similar risk) that have strong resilience scores.
3.6. Examples
Hurricane Harvey
In August 2017, Hurricane Harvey had two landfalls in Texas - Rockport, TX in Aransas County and
Port Aransas, TX in Nueces County. In addition, rainfall from Hurricane Harvey resulted in massive
flooding in Houston and surrounding areas (Harris and Brazoria Counties) and Beaumont and
surrounding areas (Jefferson and Chambers Counties). Some of the worst damage appeared to be in
Rockport, a coastal city of about 10,000 that was directly in the storm's path. Many structures were
destroyed, and Rockport's roads were littered with toppled power poles. Extensive damage was also
registered in Port Aransas, TX (site of the second Texas landfall). It is estimated that it will be a long
time before the storm's catastrophic damage is repaired. Flooding in the Houston/ Beaumont areas
was the worst in history, displacing millions of people and with flood waters expected to recede over
the course of weeks to months. As an exercise, CRSI results were examined (after the fact) to
determine the magnitude and likely locations of extensive damage and low resilience along the Texas
Gulf Coast (Table 3.1). Of these counties, CRSI scores for Chambers, Harris and Jefferson Counties
are significantly below the national average for CRSI suggesting significantly lesser resilience to
natural hazard events. In addition to these counties, Aransas and Refugio Counties (first Texas
landfall) display low risk domain scores suggesting little recent history of major natural hazard events
(until Hurricane Harvey) but both counties have significantly reduced built environment domain
scores suggesting that, if an event were to strike these counties, both would suffer significant
structural damage due to reduced public infrastructure and large proportions of vacant buildings.
Both counties also showed lower than national average levels for the society domain score suggesting
that neither county has the skills diversity to easily rebuild and neither have strong security and
security infrastructures. Hurricane Harvey also devastated Port Aransas, TX in Nueces County.
Nueces County has a significantly higher risk domain score than the national average associated
primarily with historical hurricane paths. The county is dominated by Corpus Christi, TX which
avoided much of the devastation associated with the hurricane; however, Port Aransas suffered
extensive structural damage. Port Aransas is likely much more similar to Rockport, TX in Aransas
County which demonstrates a significantly lower than average CRSI score.
The other counties with lower CRSI scores - Harris (1.35), Chambers (1.57) and Jefferson (2.01) -
all show high risk domain scores well above the national average. The Harris County risk score is
exacerbated by significant technological risks located there (e.g., chemical and oil refinery facilities,
Superfund sites). Brazoria County, located southwest of Harris county, has an average CRSI score
but a significantly higher than average risk domain score. All three of these counties are at significant
risk to flooding and all four counties significantly flooded due to the intense rainfall associated with
Hurricane Harvey. Houston (in Harris County) is reported to have had historic flooding than likely
did not recede for weeks and, in some cases, months.
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Resilience from the flooding in these counties appears to be driven by differing factors based on the
CRSI and domain scores. Brazoria County has an average resilience score that appears to be the
result simply of a high risk with all the remaining factors reducing the risk and increasing the overall
resilience score to 2.70 (about the national average). Harris County, on the other hand, has among the
highest risk scores in Texas (0.758) again associated with flooding and several exacerbating factors.
The CRSI score for this county is significantly below the national average at 1.35 suggesting
recovery from a major event could be a very long process. This lower resilience seems to be driven
by a very low natural environment score (0.192) suggesting that increasing development in the last
decade and loss of natural lands is significant (particularly to the north and west of Houston). Natural,
open lands and wetlands often provide a buffering impact to acute natural hazard events (Alongi
2008, Cai et al. 2011, Kuenzer and Renaud 2012). They are usually damaged but tend to recover
quickly while reducing the impact of the event on surrounding populated areas. This low level of
natural ecosystems in the Houston area (often replaced by impervious surfaces) would enhance the
impact of flooding. Chambers and Jefferson Counties also have high risks levels associated with
flooding with both counties displaying significantly lower than average resilience scores (Chambers
County - 1.57 and Jefferson - 2.01). However, the remaining domain scores in both counties suggest
more rapid recovery than Harris County with Chambers County recovering at a slower rate than
Jefferson County.
Table 3.1. CRSI and domain scores for select counties along the Texas Gulf Coast and National
Average scores (excluding Alaska); (Bold denotes significantly below national average for CRSI
and above national average for domains).
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Aransas
0.180
0.573
0.334
0.522
0.404
3.070
Brazoria
0.602
0.662
0.776
0.549
0.524
2.694
Calhoun
0.217
0.505
0.435
0.490
0.429
2.808
Chambers
0.571
0.615
0.511
0.500
0.440
1.567
Fort Bend
0.411
0.644
0.785
0.420
0.580
3.545
Galveston
0.610
0.753
0.608
0.472
0.408
1.257
Harris
0.758
0.611
0.837
0.192
0.491
1.345
Jackson
0.121
0.586
0.337
0.481
0.538
5.510
Jefferson
0.530
0.534
0.698
0.449
0.521
2.005
Matagorda
0.256
0.545
0.440
0.503
0.431
2.677
61
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County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Nueces
0.465
0.639
0.699
0.419
0.477
2.518
Refugio
0.116
0.631
0.266
0.468
0.443
3.961
San Patricio
0.189
0.615
0.489
0.444
0.402
3.860
Victoria
0.141
0.533
0.512
0.510
0.541
6.348
National
Average
0.229
0.597
0.393
0.413
0.516
4.321
County Comparisons
Direct comparisons of counties can be made with CRSI. These comparisons might reflect
comparisons of counties with "perceived" similarities. Appendix B provides the information
necessary to compare counties (i.e., CRSI and Domain Scores). As was done in the above examples,
a reader can determine differences between or among counties from the CRSI scores and then
determine which domains drive the observed differences. These comparisons would permit the
viewer to compare counties with "perceived" similar risks and governance and, if the CRSI scores are
different, to determine what drives the differences (e.g., low domain scores in particular areas.
EPA Regional Screening Comparisons
Regional analyses (Table 3.2) and mapping show that EPA Region 10 (15.395) and EPA Region
1 (7.53) have the strongest overall resilience scores with EPA Region 4 (1.443) and EPA Region 3
(2.934) having weaker scores. The remaining six EPA Regions cluster together with moderate scores
(3.06 - 6.477). Disassembly of the CRSI scores shows that Region 10 strengths lie in its low risk
score which result in a high basic resilience score even though its governance domain score is about
the national average. Although average, its governance domain score is more than five times the
Region's risk domain score. Region 1 strengths lie in an average governance score in the Nation with
below average risk, and above average domain scores for social, built environment and natural
environment. On the other hand, Regions 3 and 4 have above average risk domain scores and below
average governance related to natural hazard events scores. Driving down these lower basic resilience
scores, both regions have below average society domain scores suggesting a poorer population,
increased ethnicity (making communication for emergency response more difficult), lower levels of
social services, poorer access to health facilities, and higher level of undocumented skilled trade
laborers (making an assessment of the abundance of trade labor difficult). Region 4 also has a below
average score for its built environment suggesting less stringent building codes, higher levels of
vacant structures and weaker levels of public infrastructure especially in Georgia and Alabama.
Overall, the U.S. shows good levels of resilience to acute climatic events. However, analyses
demonstrate that selected counties (hundreds of them) with higher levels of risk and low levels of
62
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governance can improve their resilience by specifically addressing issues associated with the
governance, built environment, natural environment, and society domains. CRSI, which is meant to
be a screening tool, provides those directions investment, assistance and action by the EPA Regions
and Program Offices.
Table 3.2. CRSI and domain scores for EPA Regions with National Average scores (including
Alaska); (Bold denotes significantly below national average for CRSI, significantly above
national average for risk domain and simply below national average for remaining domains
which results in negative adjustment factors).
EPA Region
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Region 1
0.240
0.660
0.492
0.445
0.599
7.530
Region 2
0.308
0.658
0.469
0.385
0.520
3.839
Region 3
0.272
0.571
0.382
0.378
0.512
2.934
Region 4
0.255
0.443
0.342
0.403
0.414
1.443
Region 5
0.222
0.696
0.407
0.434
0.572
5.476
Region 6
0.239
0.584
0.394
0.422
0.474
3.060
Region 7
0.209
0.683
0.358
0.380
0.609
4.469
Region 8
0.162
0.685
0.398
0.395
0.617
6.477
Region 9
0.235
0.551
0.620
0.469
0.480
5.524
Region 10
0.137
0.660
0.478
0.531
0.492
15.395
National
Average
0.229
0.597
0.393
0.413
0.516
4.321
4. Results and Discussion - National and EPA
Regions
4.1. Organization of Results
The results of the CRSI application are shown in this section as a series of maps and graphics that
delineate CRSI scores, first across the national and then across all ten EPA Regional scales. Each
series for each of the scales consists of the same six maps and two graphics: one map for overall
63
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CRSI county results; five maps depicting county results for each of the CRSI domains (Overall Risk,
Governance, Built Environment, Natural Environment and Society); and two graphics that break
down the index to demonstrate the contributions of the five domains and the 20 indicators to the
overall CRSI score. This disassembly of the index within the EPA Regions allows each region to
assess the most significant contributors to strong and/or weak resilience to natural hazard events.
Results from the national scale CRSI scores are further examined to explore how basic resilience
(governance/risk) relates to governance. This is accomplished by analyzing the number of counties,
represented in a 5x5 matrix depicting the quintiles of governance and overall risk domain scores. This
matrix ranges from low-low (lowest 20% risk and governance) to high-high (highest 20% risk and
governance). This analysis examines whether the distribution of basic resilience in the U.S. is
characterized by greater risk scores being matched by greater governance scores. Similarly, the
analysis assesses the number of counties with high levels of governance but low levels of risk as well
as counties with low levels of governance but high levels of risk. Counties in either of these
categories would be of interest to EPA Regions as areas of potential investment (low governance and
high risk) or areas to understand the level of governance investment given the low level of risk (high
governance and low risk).
4.2. General Broad Analyses and Results of Basic Resilience
(Governance/Risk)
An initial analysis was performed to assess whether the CRSI results associated with basic resilience
(governance and risk) varied in a predictable way. Plotting the domain values of risk vs. governance
would, from a policy standpoint, be expected to have a positive relationship - greater risk should be
accompanied by greater governance. This was tested in three ways: (1) assessment analysis of risk
domain versus governance domain scores, (2) examination of the number of counties in the quintiles
of risk versus governance (i.e., the number of counties in each quintile combination and testing for
expectation using a chi-squared test) and (3) mapping the 25 quintile combinations to examine
potential patterns.
An assessment of risk domain versus governance domain is the governance/risk ratio. The expected
result of the assessment is a 45 degree angle from low risk-low governance to high risk high
governance. This finding would demonstrate that governance is developed in proportion to likely risk
(i.e., if you experience high risk there are governance activities/structures in place to counter act that
risk). Significant deviation from this finding could reflect an under- or over-
reaction to likely risk in terms of governance activities. Placing results into quantiles allows
characterization of clusters of counties as over- or under-reacting to risk in terms of governance. In
this categorical relationship, generally any combination of risk and governance along the 45 degree
angle (slope=1.0) plus or minus one category would be in the expected range. A combination of high
risk and low governance would suggest under-reacting, where as or low risk and high governance
would suggest over-reacting (new figure showing categories). Mapping these risk-governance ratio
categories by county demonstrates any clustering throughout the U.S. to detect spatial trends.
The assessment results based on normalized risk and governance domain are shown in Figure 4.1.
These results indicate that for the U.S. the governance score is generally higher than the risk score;
64
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however, 206 counties (6.6% of U.S. counties) have risk scores greater than their governance scores.
This suggests that governance activities in the majority of counties outweigh the risk of exposure to
extreme natural hazard events.
0.0 0.2 0.4 0.6 0.8 1.0
Hazard Exposure Risk Potential
Figure 4.1 Linear assessment of risk versus governance based on domain scores. Ellipses
represent differing management implications with A: Low Risk-High Governance (little increased
governance necessary other than improvements for selected below-average indicators; B: High
Risk-High Governance (likely appropriate governance but any improvement in below-average
indicators a likely improvement to resilience); C: Low Risk-Low Governance (likely appropriate
governance for level of potential risk; D: High Risk-Low Governance (improvements to governance
and indicator of the CRSI domains necessary)
Figure 4.2 shows the county data from the assessment analysis distributed across the categories
of risk-governance. For the majority of counties, risk is clustered in the second and third
quintiles, while governance clustered between the third and fourth quintile. While this result is
positive for the U.S., it can be misleading. The result may occur because the risk of exposure to
extreme natural hazard events clusters largely in the second quintile demonstrating relatively low
risk while governance clusters in the fourth quintile giving the appearance of "excessive"
governance. To account for this, the distribution of basic risk among the counties was examined
using a min-max of risk-governance based on the distribution of the county scores to determine
the roughly 500-1000 counties with the largest risk to governance disparities (Figure 4.3). These
disparities focus on those counties with lower risk and higher governance ratios and higher risk
and lower governance allowing the identification of counties where increased governance might
be beneficial.
65
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Basic Resilience Matrix Using True Domain Scores
High-
Mod-High-
o
CD
Low-Mod -
Number of
Counties
1000
750
500
250
0
Low-
Low Low-Mod Moderate Mod-High
Hazard Exposure Risk Potential
High
Figure
4.2 Distribution of number of counties in quartiles for risk and governance domains based on the
domain scores.
These county min-max scores were mapped to explore the spatial distribution of the quintiles for
any potential trends (Figure 4.3). Areas with the highest governance to risk ratio tend to be in the
northeast and scattered through midwest and Wisconsin. Areas with the lowest governance to
risk ratios appear along the west coast, and in northern and eastern North Dakota. Twenty-two
counties (<1% of total) showed very high risk scores coupled with very low governance scores.
These counties are in EPA Region 4 (8 - Alabama, Florida, Georgia and Tennessee),
EPA Region 6 (8 - Arkansas, Louisiana, Oklahoma and Texas), EPA Region 7 (2 - Missouri
and Nebraska) and EPA Region 9 (4 - California). This clustering of the min-max scores needs
to be investigated to see if spreading out the clusters creates a better understanding of the risk
versus governance interactions.
66
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; v.
. te: •;<: •/ -, n ¦<- *
sah^r -£-. '••
"¦ ¦¦S i¦ A-«^i « i : •*
m ! .ak.l «m" « ''
•Trf I -¦T i- "
- % » - !'•
*"Vj|
• W
'mm
tlla
^ j
•«u
Higher
Governance,
Lower Risk
Lower
Governance,
Higher Risk
O No Data
Figure 4.3 Map of the distribution of county scores for basic resilience.
Figure 4.4 redistributes the scores in Figure 4.2 to "spread out" the variability of both the risk
and governance scores. This helps to identify the counties where the greatest return can be
expected for the governance investment dollar. This redistribution identifies 487 counties where
low governance investment will show a modest increases in resilience; the 373 counties where
moderate investment in governance should result in moderate increases in resilience; and the 355
counties (including the original 206 counties described above) where greater investment in
governance should result in the highest increases in resilience. Approximately 1204 counties
would benefit in a small way from further governance investment while 728 counties would not
really benefit from increased resilience from further investment in governance activities.
67
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Basic Resilience Matrix Using Percentile-Ranked Domain Scores
High-
Mod-High
a>
o
c
re
£ Moderate-
v
>
o
O
Number of
Counties
¦ 175
150
125
100
75
50
Low-Mod ¦
Low-
I 120 128 153 136
Low Low-Mod Moderate Mod-High High
Hazard Exposure Risk Potential
Figure 4.2. Distribution of number of counties in quartiles for risk and governance domains based on
number of samples (redistributing the basic resilience scores
The spatial distribution of these counties is shown in Figure 4.5. The areas with the highest and
lowest governance to risk ratios remain consistent with Figure 4.3 (as expected). Throughout the
eastern seaboard the ratio of scores is moderate governance to higher risk, as are the Ohio Valley area
and Great Lakes region. Lower governance to moderate risk ratios are seen through much of
the midwest and the northwest (east of the Cascades). In addition to the west coast, the lowest ratios
are seen in much of California, Indian country, Arizona, Nevada, and Utah.
Basic resilience can be modified by social activities and structures, the built environment and the
natural environment to represent overall resilience (the CRSI score). If these attributes are strong then
resilience (mainly through recoverability) can be enhanced. If these attributes are weak then resil ience
68
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for a area can be deterred. The next sections will examine basic resilience as modified by these
factors for the nation and each EPA region.
Higher
Governance,
Lower Risk
Lower
Governance,
Higher Risk
No Data
Figure 4.3 Map of the re-distribution of counties to demonstrate the likelihood of increased
resilience with increased governance.
4.3 Presentation of Results
Results in the following sections are organized by spatial sub-division (nation and EPA regions).
Figures 4.6-4.10 provide information to interpret the results in the graphics presented for the sub-
divisions. Each national and regional section includes:
• Figure 4.6: CRSI and Domain score bar graph depicting the scores and the adjustment values
for the Society, Built Environment and Natural Environment domains.
• Figure 4.7: Six-panel maps showing the distribution of the CRSI and domain scores by
county.
• Figure 4.8: Table of the highest -5% of CRSI values.
• Figure 4.9: Characterization of the Risk Domain with breakdowns of the exposure and loss
indicator scores.
• Figure 4.10: Polar bar plots describing the contributions of the indicators to the domain
scores. These plots show the scores for each indicator with the five domains. Longer bars
represent higher scores; shorter scores represent lower scores. The polar plots show the
contribution of indicator scores to the domain score.
Discussion of results follow the graphic results in each section. All CRSI and domain scores for each
region by state and each region by state and county are listed in Appendices B and C.
69
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4.3.1. CRSI and Domain Score Bar Graphs
The spatial scale for the domain and CRSI scores is
either U.S. or EPA region. CRSI and domain scores for
the U.S. are the mean of all county scores. EPA Region
CRSI and domain scores are calculated as the mean of
all county scores in the Region.
CRSI is an index value characterizing the
resilience of the U.S or EPA region with the
range of county values. Higher scores reflect
more resilience to acute climate events.
Built Environment
The lighter shade bars represent the
domain scores. For risk, the higher
scores reflect greater risk. Higher scores
for the other 4 domains reflect better
governance, and greater capacity for
recoverability (less vulnerability).
Natural Environment
Domain Score (lighter shat
nrJ/Median Adjusted Score(darker shade bar)
The darker shade bars represent the domain median adjusted scores
used in the calculation of CRSI (Society, Built Environment and Natural
Environment only). For example, the value represented by the dark green
bar in the Natural Environment domain shows the degree of positive or
negative influence in the CRSI value based on the deviation above or
below the domain median score
Figure 4.6 Example summary of CRSI and domain available for the nation and each EPA region.
70
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4.3.2. Six Panel Maps
U.S. PANEL
EPA REGION PANEL
CRSI
r-?.Sr^"
" >
Built Environment
fv t>t .« v.
>
Risk
r
(5
Vr " f ""''
* - < •>
/ v*'
Society
i
#Mffc
»* >: ~-v
,* *
Natural Environment
- J?
•<':<£, ¦ 4 f
>., -• /
r*.7 ••/'• J
v'"- # •
Risk
4 ^
' V* * '-<*
;
4 *
4LJHi i' *
4
Society
Natural Environment
*
Higher No Data
Figure 4.7 Example of six-panel maps showing the distribution of county-level CRSI and domain scores available for the nation and for the
EPA Regions.
71
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4.3.3. Top County CRSI Values
Rank
County
|EPA Region
Rank
County
| EPA Region
Rank
County
EPA Region
1-
Kodiak Island Borough, Alaska
Region 10
51.
Skagway Municipality, Alaska
Region 10
101-
St- Louis County, Minnesota
Region 5
i
Iuomu City end Borough, Alaska
Region 10
52.
Oneida County, Wisconsin
Region 5
102.
Washington County, Vermont
Region 1
B.
Kttchihin Oittwiy Borough. AlailM
Region 10
53.
Pipestone County, Minnesota
Region 5
103
Pulaski County, Indiana
Region 5
4.
Aleutians East Borough, Alaska
Region 10
54.
Price County, Wisconsin
Region 5
104
Baker County. Oregon
Region 10
5.
North Slope Borough. Alaska
Region 10
55.
Clark County, Wisconsin
Region 5
105
McLean County, North Oakota
Region 8
6.
Haines Borough, Alaska
Region 10
56.
Lamoille County, Vermont
Region 1
106.
Grant County, New Mexico
Region 6
7.
Prince of Wales- Myder Census Area. Alaska
Region 10
57.
Uinta County, Wyoming
Region 8
107.
Caledonia County, Vermont
Region 1
a.
Hancock County, Maine
Region 1
58.
Day County. South Dakota
Region 8
108.
Sweetwater County. Wyoming
Region 8
9.
Sitka City and Borough, Alaska
Region 10
59.
Koochiching County, Minnesota
Region 5
109.
Wrangell City and Borough, Alaska
Region 10
10.
Hoonah- Angoon Census Area. Alaska
Region 10
60.
San Juan County, New Mexico
Region 6
110.
Huron County, Michigan
Region 5
11.
Waldo County, Maine
Region I
ftl.
Ravalli County, Montana
Region 8
111.
Lake County. Minnesota
Region 5
12.
Oukes County, Massachusetts
Region l
62.
Coconino County, Arizona
Region 9
112.
Kalkaska County, Michigan
Regions
11.
Dillingham Census Area, Alaska
Region 10
63.
Lincoln County, Maine
Region 1
113
King William County, Virginia
Region 3
14.
Kenai Peninsula Borough, Alaska
Region 10
64.
Pitkin County, Colorado
Region 8
114.
Morrison County, Minnesota
Regions
15.
Petersburg Census Area, Alaska
Region 10
65.
Blaine County, Idaho
Region 10
115.
Umatilla County, Oregon
Region 10
14.
Fairbanks North Star Borough, Alaska
Region 10
66.
Beaverhead County, Montana
Region 8
lift.
Missoula County, Montana
Region 8
17
Yakutat City and Borough, Alaska
Region 10
67.
Pembina County, North Dakota
Region 8
117.
Franklin County, Maine
Region 1
11
Maul County, Hawaii
Region 9
68.
Gunnison County. Colorado
Region 8
118.
Deschutes County, Oregon
Region 10
19.
Bonner County, Idaho
Region 10
69.
Chaffee County. Colorado
Region 8
119.
Teton County. Montana
Region 8
20.
Aleutians West Census Area, Alaska
Region 10
70.
Benton County. Indiana
Region 5
120.
Lewis County, New York
Region 2
21.
Bristol Bay Borough, Alaska
Region 10
71.
Honolulu County, Hawaii
Region 9
121.
Cass County, Minnesota
Region 5
22.
Hamilton County, New York
Region 2
72.
St. Lawrence County, New York
Region 2
122.
jasper County. Indiana
Region 5
23.
Flathead County, Montana
Regions
73.
Essex County. Vermont
Region 1
121.
Jackson County. Wisconsin
Region 5
24.
Anchorage Municipality. Alaska
Region 10
74.
Shawano County, Wisconsin
Region 5
124.
Polk County, Wisconsin
Region 5
25.
Latah County, Idaho
Region 10
75.
Sierra County. New Mexico
Region 6
125.
Winneshiek County. Iowa
Region 7
2ft.
Washington County, Maine
Region 1
76.
San Miguel County, Colorado
Region 8
126
Summit County, Colorado
Region 8
27.
Valley County, Idaho
Region 10
77.
Ward County. North Dakota
Region 8
127.
Livingston County, Illinois
Regions
28.
Addison County, Vermont
Region 1
78.
Routt County. Colorado
Region 8
128
Huntingdon County, Pennsylvania
Region 3
29.
Knox County, Maine
Region 1
79.
Chickasaw County, Iowa
Region 7
129.
Valdez-Cordova Census Area, Alaska
Region 10
30.
Lincoln County, Minnesota
Region 5
80.
Jefferson County. Montana
| Region 8
130.
Elko County. Nevada
Region 9
11.
Roberts County, South Dakota
Region 8
81.
Newton County, Indiana
Region 5
131.
Clayton County, Iowa
Region 7
32.
Kauai County. Hawaii
Region 9
82.
Forest County, Wisconsin
Region 5
132.
Wasco County, Oregon
Region 10
33.
Penobscot County, Maine
Region 1
S3.
Sawyer County, Wisconsin
Region S
133.
Deuel County, South Dakota
Region 8
34.
Pierce County, Nebraska
Region 7
84.
Grant County, Minnesota
Region 5
134.
Rio Arriba County, New Mexico
Region 6
35.
Aroostook County, Maine
Region 1
85.
Ouray County. Colorado
Region 8
11S.
Luna County. New Mexico
Region 6
36.
Carbon County, Wyoming
Region 8
86.
Oceana County, Michigan
Region 5
136.
Nez Perce County. Idaho
Region 10
37.
Itasca County, Minnesota
Region 5
87.
Sanders County. Montana
Region 8
137.
Newaygo County. Michigan
Region 5
38.
Lake and Peninsula Borough, Alaska
Region 10
88.
Piscataquis County, Maine
Region 1
138.
T loga County, Pennsylvania
Region 3
39.
Hawaii County, Hawaii
Region 9
89.
Vilas County, Wisconsin
Region 5
139-
Sanilac County, Michigan
Region 5
40.
Rutland County. Vermont
Region 1
90.
Eagle County, Colorado
Region 8
140.
La Plata County. Colorado
Region 8
41.
Somerset County, Main*
Region 1
91.
Fillmore County, Minnesota
Region 5
141.
Duchesne County. Utah
Region S
42.
Grand Isle County, Vermont
Region l
92.
Otero County. New Mexico
Region 6
142.
Missaukee County. Michigan
Region 5
43.
Boundary County, Idaho
Region 10
93.
Garfield County. Colorado
Region 8
143.
Idaho County. Idaho
Region 10
44.
Lincoln County, Montana
Region 8
94.
Grant County, South Dakota
Region 8
144.
Union County, Oregon
Region 10
45.
McKinley County. New Mexico
Region 6
95.
Navajo County, Arizona
Region 9
145.
Lassen County, California
Region 9
4ft.
Daniels County, Montana
Region 8
96.
Merrimack County. New Hampsh Region 1
146.
Benewah County, Idaho
Region 10
47.
Grafton County, New Hampshire
Region 1
97. Door County, Wisconsin
Region 5
147.
Ashland County, Wisconsin
Region 5
48.
Mono County, California
Region 9
98.
Steuben County, New York
Region 2
148.
White Pine County, Nevada
Region 9
49.
Coos County. New Hampshire
Region 1
99.
Florence County, Wisconsin
Region 5
149.
Windham County, Vermont
Region 1
50.
San Juan County. Washington
Region 10
100.
Washburn County. Wisconsin
Region 5
150-
Humboldt County. California
Region 9
C
V
A table including counties with the top
150 CRSI values in the U.S. follows the
chloropleth maps. Counties are color-
coded by EPA Region. For the EPA
Regions, the table includes counties with
the top 25 CRSI values in each Region.
1Z
Region 7
Rank
County
t
Pierce County, Nebraska
Z
Chickasaw County. Iowa
3.
Winneshiek County, Iowa
4.
Clayton County, Iowa
5.
Fayette County, Iowa
6.
Wabaunsee County, Kansas
7.
Nodaway County, Missouri
a
Marshall County, Kansas
9.
Ottawa County, Kansas
10.
Macon County, Missouri
n
Miami County, Kansas
12.
Richardson County, Nebraska
13.
Bremer County. Iowa
14.
Nemaha County, Kansas
15.
Washington County, Kansas
16.
Shelby County, Iowa
17.
Washington County, Iowa
18.
Osage County, Missouri
19.
Kossuth County, Iowa
20.
Cherokee County, lowa
21.
Lafayette County, Missouri
22.
Pottawatomie County, Kansas
23.
Brown County, Kansas
24.
Cedar County, lowa
25.
Vernon County, Missouri
Figure 4.8 Example Table of highest ranking CRSI values for all U.S. counties and counties within EPA Regions. All state and
county CRSI scores can be found in Appendices B and C.
72
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4.3.4. Breakdown of the Risk Domain
Chloropleth map for U.S./EPA
Region depicting Risk Domain
scores for each county based on
land area impacted by historic
natural hazard events and losses
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Higher n0 Data
Risk Ranee:
High - Orange, CA - 0.76
Mean - 0.23
Low- Maui, HI-0.06
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake
Drought
Coastal
Flooding
r
Counties with the highest and
lowest Risk Domain scores and
the average Risk Domain Score
for the U.S./EPA Region
Natural Hazards
Technological Hazards
Toxic Release
Superfund Haxard
RCRA
58%
Nuclear Hazard 12%
0% 20% 40% 60% 80% 100%
Loss
v
Natural Land
100% Human and Property
Dual Benefit Land
100%
c
0% 20% 40% 60% 80% 100%
U.S./EPA Region bar charts
showing: (1) the percentage of
counties affected by individual
] natural hazard exposure events
(left); (2) the percentage of
counties with potential
technological hazard risks that may
pose secondary adverse exposures
during/after a natural hazard event
(top right); and (3) the percentage
of counties that experienced
different types of loss related to
natural hazard events (bottom
right)
Figure 4.9 Example summary of Risk domain presented for the nation and the EPA Regions.
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4.3.5. Polar Plots for Nation and EPA Regions
/^^>olar bar charts show the scores for^
each of the indicators within the five
domains. Bars are color-coded by
domain. Longer bars represent higher
scores. This graphic is intended to
show the contribution of indicator
scores to the domain score. The
indicator bars are not intended to be
compared across domains. For the
Risk Domain, higher exposure and loss
indicator scores correspond to greater
risk (more vulnerability)
V
Figure 4.10 Example polar plot describing the contributions of the 20 indicators to the domain
scores.
4.3.6. National Results
The U.S. is comprised of 3,143 boroughs and counties. The Cumulative Resilience Screening
Index (CRSI) includes 3,135 counties; excluding eight boroughs from Alaska. These eight
boroughs could not be included because they had too little data and metric values could not be
imputed or interpolated accurately. With the increase in the frequency and severity of natural
hazard events over the last decade (e.g., Hurricanes Katrina, Ivan and Rita; Superstorm Sandy;
increases in flooding, hailstorms, and wildfires; increases in maximum temperatures; and
decreases in minimum temperatures), many U.S. Federal Agencies (e.g., FEMA, U.S. EPA,
DOC and DOI) have been assisting states prepare for these types of natural hazard events. The
U.S. CRSI score is 4.32 based on the average of CRSI scores for all counties in the U.S. ranging
from -2.13 to 35.4 (including Alaska increases the max to 189.17).
The CRSI and domain scores for the nation are shown in Figure 4.11. The nation is characterized
by moderate risk, moderate to high Governance, moderate to high Society, Built Environment.
The distribution of overall CRSI values as well as the domain scores by county for the U.S. are
shown in Figure 4.12. Examples of inferences that can be made from the maps are:
The western U.S., the Great Lakes area and the upper northeast have higher CRSI values
(higher resilience to natural hazard events).
The western mid-west, the southeast, western Texas and Appalachian region have lower
CRSI values.
74
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The lower northeastern coastal area, southeast/Gulf coasts, a small area associated with
southern Lake Michigan, and southern California have the highest risk domain scores
albeit for different types of risk.
• Lower risk scores are seen in the west and upper mid-west, Alaska and Hawaii.
• Higher Governance scores are seen in the northeast, mid-Atlantic and Great Lakes areas
of the U.S.
• Lower Governance scores related to natural hazard were observed in Appalachia, the
deep south and much of California.
• Higher Society scores are seen in the upper mid-west and mountain west.
• Lower Society scores are seen in Appalachia and the deep south. Both Built and Natural
Environment domain scores were higher in the west and lower in the western Midwest
and southeast.
Many other inferences can be determined from the mapped distributions.
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.11 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for
the U.S, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
75
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Governance
Society
Score
Lower
Higher No Data
Figure 4.12 The distributions of CRSI values and domain scores (Risk, Governance, Society, Built
Environment and Natural Environment).
76
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In order to provide regions and counties with examples of the higher CRSI-scored counties in the
U.S., Table 4.1 shows the 150 counties in the U.S. with the highest CRSI values. Region 10 has
the most counties in the top 150 list (43 counties) followed by Region 8 (36), Region 5 (34) and
Region 1 (15). All of the remaining EPA regions (except Regions 2, 3 and 4) have four or more
counties in the top 150. Regions 2 and 3 are represented by a single county in the top 150
counties while Region 4 has no representation in these counties. This provides most EPA regions
with several example counties to use as role models for counties characterized by lower CSRI
scores. Counties with the lowest scores (25 counties) are predominated by Region 4 (16 counties
primarily in Georgia) followed by Region 8 (5) and one each in Regions 6, 7 and 10. Risk due to
natural hazard events across the U.S. is examined in more detail in Figure 4.13.
Natural exposures due to natural hazard events are predominated by extreme high temperatures
(100% of counties experience) and extreme low temperatures (100%); inland flooding (100%);
drought (99%). All other types of exposure due to natural hazard events are represented at 8-
98%) of counties. RCRA (Resource Conservation and Recovery Act) and Superfund sites
dominated the technological exposure indicator at 44% and 28%, respectively. Technological
exposure adds potential risk to counties prone to natural hazard event exposures. Nationally,
losses are seen primarily on dual benefit and natural land use types (e.g., forests, wetlands,
agriculture). Most exposure comes from natural hazard events although 6% of exposure is due to
exacerbated exposure resulting from proximity to technological features that pose hazards. Risk
ranges from the lowest score of 0.01 in the Kodiak Island and Ketchikan boroughs of Alaska to
0.99 in Shelby County, Tennessee.
The contributions of the 20 indicators to the national domain scores are shown in Figure 4.14.
Natural resource conservation (Governance), number of vacant structures and housing
characteristics (Built Environment) as well as demographic characteristics (Society) most
strongly influenced national domain scores. Secondary influences included levels of loss (Risk),
socio-economic characteristics, social cohesion and economic diversity in the Society Domain,
community and personal preparedness (Governance) and acreage of ecosystem types (Natural
Environment).
Overall, CRSI values, domain scores and indicator contributions all paint a picture for the U.S.
of reasonable resilience to natural hazard events. However, the distribution of these scores is
broad. While there are many relatively resilient counties in the U.S., there are a number of
counties in which overall resilience to natural hazard events is low or one or more of the domain
scores are low. Therefore, more specific results and analyses should be examined for each of the
regions.
77
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Table 4.1 Top 150 counties according to CRSI values (i.e., potentially higher resilience to
natural hazard events).
EPA EPA EPA
Rank C'ountv Region Rank County Region Rank County Region
1.
Kodiak Island, Alaska
Region 10
51.
Somerset, Maine
Region 1
101.
Umatilla, Oregon
Region 10
2
Juneau City and, Alaska
Region 10
52.
Newton, Indiana
Region 5
102.
Morrison, Minnesota
Region 5
3.
Ketchikan Gateway, Alaska Reajon 10
53.
Grant, South Dakota
Region S
103.
Elmore, Idaho
Region 10
4.
Aleutians East, Alaska
Region 10
54.
Sawyer, Wisconsin
Region 5
104.
Hamlin, South Dakota
Region 8
5.
Hoonah-Angoon, Alaska
Region 10
55.
Pembina, North Dakota
Region 8
105.
Wabaunsee, Kansas
Region 7
6.
Haines, Alaska
Region 10
56.
Pitkin, Colorado
Region 8
106.
Esses, Vermont
Region 1
7.
Prince of Wales-Hvder, Alas Region 10
57.
Gunnison, Colorado
Region 8
107.
Sanders, Montana
Region 8
8.
North Slope, Alaska
Region 10
58. Washington, Maine
Region 1
108.
Marshall Kansas
Region 7
9.
Sitka City and, Alaska
Regon 10
59.
San Miguel C olorado
R^ion S
109.
Moody, South Dakota
Region 8
10.
Dillingham, Alaska
Region 10
60.
Kalkaska, Michigan
Region 5
110.
Sweetwatar, Wyoming
Region 8
11.
Petersburg, Alaska
Region 10
61.
Oneida, Wisconsin
Region 5
111.
Ea^e, Colorado
Region 8
12.
Hancock, Maine
Region 1
62.
Aroostook Maine
Region 1
112,
Sanilac, Michigan
Region 5
13.
Bristol Bay, Alaska
Region 10
63.
Duchesne, Utah
Region S
113.
King William, Virginia
Regan 3
14.
Kenai Peninsula, Alaska
Region 10
64.
Chickasaw, Iowa
Region 7
114.
Routt, Colorado
Region 8
15.
Wiangdl City and, Alaska
Region 10
65.
Oceana, Michigan
Region 5
115.
Pulaski, Indiana
Region 5
16.
Fairbanks North Star, Alask Region 10
66.
Vilas, Wisconsin
Region 5
116. Codington, SouthDakota Region 8
17.
Skagwav Municipality, Afad Region 10
67.
Pipestone, Minnesota
Region 5
117.
Uintah, Utah
Region 8
18. Waldo, Maine
Regon 1
68.
Koochiching, Minnesota
Region 5
118.
Grant, Minnesota
Region 5
19.
Aleutians West, Alaska
Region 10
69.
Price, Wisconsin
Region 5
119.
San Juan, NewMexico
Region 6
20.
Maui, Hawaii
Regon 9
70.
Washbum, Wisconsin
Region 5
120.
Ashland, Wisconsin
Region 5
21.
Yakutat City and, Alaska
Region 10
71.
Penobscot, Maine
Region 1
121.
Missaukee, Michigan
Region 5
22.
Anchorage Municipality, Ala Region 10
72.
Deschutes, Oregon
Region 10
122.
Baker, Oregon
Region 10
23.
Dukes, Mas sadius etts
Region 1
73.
Fillmore, Mrmesota
Region 5
123.
Polk Wisconsin
Region 5
24. Latah, Idaho
Region 10
74.
Shawano, Wisconsin
Region 5
124.
Newavgo, Michis^n
Region 5
25.
Robots, South Dakota
Regions
75.
Coconino, Arizona
Region 9
125.
Wilkin, Minnesota
Region 5
26.
Lake aril Peninsula, Alaska
Re^on 10
76.
Forest Wisconsin
Region 5
126.
Mineri, Montana
Region 8
27.
Bonner, Idaho
Region 10
77.
Lincoln, Maine
Region 1
127.
Cass, Minnesota
Region 5
28.
Kauai, Hawaii
Region 9
78.
Benton, Indiana
Region 5
128.
Luna, NewMexico
Region 6
29.
Lincoln, Minnesota
Region 5
79.
Asotin, Washington
Region 10
129.
Summit, Colorado
Region 8
30.
Pierce, Nebraska
Region 7
80.
Ward, North Dako ta
Region 8
130.
Taylor, Wisconsin
Region 5
31.
Hawaii, Hawaii
Region 9
81.
Malheur, Oregon
Region 10
131.
Rutland, Vermont
Region 1
32.
Valley, Idaho
Region 10
82.
Pend Oreille, Washington Region 10
132.
Cassa, Idaho
Region 10
33.
Boundary, Idaho
Region 10
83.
Beaverhead. Montana
Region 8
133.
Jaspar, Indiana
Region 5
34.
Flathead, Montana
Resjon 8
84. Huron, Michigan
Region 5
134.
Richardson, Nebraska
Region 7
35.
Addison, Vermont
Region 1
85.
Garfield, C olorado
Region S
135.
1
I
u
Region 7
36. Day, South Dakota
R^ionS
86. Wasco, Oregon
Region 10
136.
Sierra, NewMexico
Region 6
37.
Daniels, Montana
Region 8
87.
Teton, Montana
Region 8
137. Grafton, New Hampshire
Region 1
38. Cartoon, Wyoming
Regon 8
88.
Idaho,Idaho
Region 10
138.
Marshall Minnesota
Region 5
39. Hamilton, New York
Region 2
89.
Mono, California
Region 9
139.
Knox, Maine
Region 1
40. Benewah, Idaho
Region 10
90.
Adams, Idaho
Region 10
140.
Jackson, Wisconsin
Region 5
41. Uinta, Wyorang
RegjonS
91.
Grand Isle, Vermont
Region 1
141.
Grant, Oregon
Region 10
42.
Itasca, Minnesota
Region 5
92.
Granite, Montana
Region S
142.
Winneshidc Iowa
Region 7
43.
Florence, Wisconsin
Region 5
93.
Chaffee, Colorado
Region 8
143.
Toole, Montana
Region 8
44.
Blaine, Idaho
Region 10
94.
McL ean, North Dakota
Region 8
144.
Ottawa, Kansas
Region 7
45.
San Juan, Washington
Region 10
95.
1
&
1
X
RegionS
145.
Door, Wisconsin
Region 5
46.
Deud, South Dakota
Regions
96.
Whitman, Washington
Region 10
146.
Navajo, Arizona
Region 9
47.
Lincoln, Montana
Regions
97.
Coos, >fewHarrpsbire
Region 1
147.
Ftyette, Iowa
Region 7
48.
Ouray, Colorado
Regon S
98.
McKiriey, New Mexico
Region 6
148.
Milard, Utah
Region 8
49. Honolulu, Hawaii
Region 9
99.
Union, Oregon
Region 10
149.
Valdez-Cordova, Alaska
Region 10
50.
Ravalli, Montana
Region 8
100.
Nez Perce, Idaho
Region 10
150.
Iron, Wisconsin
Region 5
78
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*c
V
Lower
Score
•& «• vfc '* f
/ y
VJ*v*
Higher No Data
Risk Range:
High - Shelby, TN - 0.99 Low - Kodiak Island, AK - 0,01
Mean - 0.23
Witdfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Hurricane LS%]
High Wind
High
Temperature
Hail Storm
Earthquake
Drought
Coastal
Flooding
11%
Natural Hazards
12%
39%
39%
96%
100%
98%
100%
98%
Technological Hazards
Toxic Release 2%
Superfund Haxard 28%
RCRA 44%
Nuclear Hazard 5%
0« 20% 40% 60% 80% 100%
Loss
22%
98%
0%
20%
40%
60%
80%
Natural Land
99% Human and Property
Dual Benefit Land
100% 0% 20% 40% 60% 80% 100%
67%
. Figure 4.4 U.S. map depicting scored natural hazard risk exposure by county. Bar charts showing the percentage of counties with > 0.01% of
total land area: exposed to natural hazards by event type; at risk for secondary technological hazard exposures; and cumulative losses incurred
as a result of natural hazard events. The counties exhibiting the highest risk and lowest risk along with National risk score average (several
counties have 0.01 and 0.99 adjusted risk domain scores but Kodiak Island, AK has the lowest unadjusted calculated risk score and Shelby, TN
has the highest unadjusted risk score).
79
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
t Community
/ Preparedness
Natural
z
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
\ Infrastructure
Health
Characteristics
Socio-
Economics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.5 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the nation. The length of the bars corresponds to the indicator score. Within a domain, the higher indicator
scores show a greater contribution to the domain.
80
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4.3.7 Regional Results
The following sections depict the results for all ten EPA Regions.
EPA Region 1
EPA Region 1 serves Connecticut, New Hampshire, Maine, Massachusetts, Rhode Island, and Vermont.
Region 1 also serves ten federally recognized tribes within Maine, Massachusetts, Connecticut and
Rhode Island. Areas within Region 1 are prone to, and often impacted by, intense rainfall, sea level rise,
and heatwaves. For example, Cambridge, MA has experienced extreme rain events leading to flooding
and compromising infrastructure. Nearby, Boston, MA, is projected to experience the same types of
extreme rain events, but given its proximity to the coast, flooding will be exacerbated by sea level rise
and erosion impacts are more of a concern. Since Boston is more urban, these issues have to be dealt
with in the context of affordable housing and social inequity. The 2014 EPA Region 1 Climate Change
Adaptation Plan (USEPA-R1 2014) suggests re-nourishing coasts with dredged material, performing
marsh restoration and considering "living shorelines" to combat coastal wetland erosion. Suggested
actions around severe rainfall and sea level rise focused on determining where the impacts would occur
and focusing current restoration or infrastructure improvement efforts based on that information. For
example, prioritizing restoration of tidal wetlands that have room to migrate with sea level rise.
The CRSI and domain scores for EPA Region 1 are shown in Figure 4.15. The Region is characterized
by moderate risk, moderate to high Governance, moderate to high Society, Built Environment and
Natural Environment scores. The domain scores for Society, Built Environment and Natural
Environment showed positive influences (all higher the national averages for positive influence) on the
overall CRSI score of 4.375. The Region 1 CRSI score ranked 2nd among the ten EPA Regions.
The overall CRSI score and 5 domain scores for EPA Region 1 are depicted in Figure 4.16. The higher
CRSI values are seen in the northern counties of Maine, a number of counties in Vermont and select
counties in New Hampshire (Table 4.2). Lower CSRI scores (< 2.0) for the region are seen in
Connecticut (3 counties), Rhode Island (3), middle Massachusetts (2), and New Hampshire (1). The
highest risk domain scores are seen in middle Massachusetts, and most of Connecticut.
Risk due to natural hazard events across Region 1 is examined in more detail in Figure 4.17. Natural
exposures due to natural hazard events are dominated by high and low temperatures, wind, drought, hail
and inland flooding (99-100% of counties). Tornadoes and landslides also represent a sizeable portion
of the risk potential (87% and 57% of counties, respectively). All other types of exposure due to natural
hazard events are represented at 0-48% of counties. RCRA (Resource Conservation and Recovery Act)
sites and Superfund sites represent a majority of the technological exposure indicator at 78% and 67%
of counties, respectively. Nuclear sites contribute only 18% of the exposure potential. In the region,
losses are represented almost exclusively (96%) in natural lands, with the other 4% of regional losses
coming from dual-benefit lands. Risk ranges from a low score of 0.03 in Waldo County, Maine to a high
score of 0.65 in Hartford County, Connecticut. The mean regional risk (0.24) falls at about the national
average at 0.23.
The contributions of the 20 indicators to EPA Region 1 domain scores are shown in Figure 4.18. Higher
scores for indicators of natural resource conservation, demographic characteristics and number of vacant
structures contributed to higher scores in each respective domain. These influences combined with low
loss contribution from the risk domain are reflected in the Region's higher CRSI value of 4.375. Safety
81
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and security, labor-trade services and ecosystem condition had minimal influence on the EPA Region 1
domain scores.
82
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EPA Region 1
Risk
CRSI
7.530
(Range: 1.02-35.40)
H3— Governance
I a =~"l
|sr=l
Society
Built Environment
Natural Environment
-0.S -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.S 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Scorefdarker shade bar)
Figure 4.6 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 1 along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
83
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Region 1
CRSI
Risk
Governance
Society
Built Environment
Score
Lower
Natural Environment
Higher No Data
Figure 4.16 The distributions of EPA Region 1 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
84
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Table 4.2 Top 25 counties according to CRSI values in EPA Region 1 (i.e., higher resilience to
natural hazard events).
Region 1
Rank
County
1.
Hancock, Maine
2.
Waldo, Maine
3.
Dukes, Massachusetts
4.
Addison, Vermont
5.
Somerset, Maine
6.
Washington, Maine
7.
Aroostook, Maine
8.
Penobscot, Maine
9.
Lincoln, Maine
10.
Grand Isle, Vermont
11.
Coos, New Hampshire
12.
Essex, Vermont
13.
Rutland, Vermont
14.
Grafton, New Hampshire
15.
Knox, Maine
16.
Lamoille, Vermont
17.
Merrimack, New Hampshire
18.
Piscataquis, Maine
19.
Windham, Vermont
20.
Washington, Vermont
21.
Caledonia, Vermont
22.
Franklin, Maine
23.
Franklin, Massachusetts
24.
Cheshire, New Hampshire
25.
Sagadahoc, Maine
85
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Lower
.
M
%*
Score
Wildfire 9%
Tornado
Low
Temperature
Landslide
Higher No Data
Risk Range:
High - Hartford, CT - 0.6S Low - Waldo, ME - 0.03
Mean - 0,24
Inland Flooding
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake 1%
Drought
Coastal
Flooding
Natural Hazards
57%
87%
0%
20%
48%
40% 60%
100%
99%
100%
100%
99%
Technological Hazards
Toxic Release 13%
Superfund Haxard
RCRA
67%
78%
Nuclear Hazard 18%
0% 20% 40% 60% 80% 100%
Natural Land
Loss
34%
100%
99% Human and Property
Dual Benefit Land 31%
80% 100% 0% 20% 40% 60% 80% 100%
Figure 4.7 Map of Risk Domain scores by county for Region 1; proportion of natural exposures by climate event type, technological exposures, losses and
exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary exposure types in the
region (If a category was represented by <0.1%, it was not included).
86
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
/ Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
2 Transportation
? Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Health
Characteristics
Socio-
Economics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.8 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 1. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 2
Region 2 of the EPA serves New Jersey, New York, and the territories of Puerto Rico and the U.S.
Virgin Islands. Region 2 also serves eight federally recognized Indian Nations, all within New York.
Region 2 shares the same regional impacts as Region 1; intense rainfall, sea level rise, and heatwaves.
Cities such as New York, NY have experienced multiple impacts, including extreme heatwaves, sea
level rise, severe storms and erosion. The age and scale of New York's transportation infrastructure
87
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combined with the dense population raises some unique resilience concerns. The EPA Region 2 Climate
Change Adaptation Plan of 2014 (USEPA-R2 2014) suggests managing increased storm water using
green infrastructure and building more resistance to climate change impacts through investments in
infrastructure.
The CRSI and domain scores for EPA Region 2 are shown in Figure 4.19. The Region is characterized
by above average risk; high Governance; moderate Society; high Built Environment; and, lower Natural
Environment scores. The domain scores for Society and Built Environment showed positive influences
(particularly Built Environment) on the overall CRSI score of 3.839 while the Natural Environment
score had a negative influence on the CRSI score. Region 2 CRSI score ranked below average in terms
of overall resilience to natural hazard events among all EPA Regions. The higher resilience to natural
hazard events risk scores in EPA Region 2 were seen in upper New York while the lower risk counties
were in upper and western New York (Figure 4.20 and Table 4.3). The lower resilience scores were
observed in both New York and New Jersey with 10-13 counties in each state with low CRSI values (<
2.0). The higher risk of natural hazard events counties (Risk > 0.59) are seen primarily in New Jersey
(Ocean, Monmouth) and Westchester, New York.
Risk due to natural hazard events across Region 2 is examined in more detail in Figure 4.21. Natural
exposures due to natural hazard events are dominated by extreme high and low temperatures, high wind,
hail and tornadoes (100% of counties). Inland flooding and drought risks occurred in 99% and 90% of
counties in Region 2, respectively. All other types of exposure due to natural hazard events are
represented at 2-41% of counties. RCRA and Superfund sites represent a majority of technological
exposure indicator at 99% and 83% of counties, respectively. Nuclear hazards and TRI (Toxic Release
Inventory) sites contribute only 13-14% of the exposure potential. Natural hazard risk potential
dominates the region, with only 21% of the risk attributable to technological exposure potential. Risk
ranges from a low score of 0.06 in Hamilton County, New York to a high score of 0.81 in Ocean
County, New Jersey. The mean regional risk (0.31) falls significantly above the national average at 0.23.
The contributions of the twenty CRSI indicators are shown in Figure 4.22. Strong positive influences on
the Region 2 domain scores come from community preparedness and natural resource conservation
(Governance), demographic characteristics (Society) and vacant structures (Built Environment). In the
Society Domain, secondary positive influences are seen from economic diversity, socio-economic
characteristics and higher social cohesion scores. Weak influences (and sometimes strong negative
influences) on the Region 2 score come from safety and security and labor-trade services (Society) as
well as greater exposure risk.
88
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Domain Score (lighter shade bar)/Medtan Adjusted Score(darker shade bar)
Figure 4.9 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 2, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
89
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Region 2
CRSI
Governance
*
\
4
•> ^ /
*
Score
Lower
Risk
Society
Natural Environment
*
Higher No Data
Figure 4.20 The distributions of EPA Region 2 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
Built Environment
90
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Table 4.3 Highest 25 CRSI values in EPA Region 2 by county.
Region 2
Rank
County
1.
Hamilton, New York
2.
Steuben, New York
3.
St. Lawrence, New York
4.
Lewis, New York
5.
Essex, New York
6.
Livingston, New York
7.
Jefferson, New York
8.
Franklin, New York
9.
Herkimer, New York
10.
Clinton, New York
11.
Schuyler, New York
12.
Warren, New York
13.
Schoharie, New York
14.
Ontario, New York
15.
Tompkins, New York
16.
Cayuga, New York
17.
Yates, New York
18.
Chautauqua, New York
19.
Wyoming, New York
20.
Ulster, New York
21.
Columbia, New York
22.
Cattaraugus, New York
23.
Oneida, New York
24.
Allegany, New York
25.
Otsego, New York
91
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Natural Hazards
Lower
Score
10%
11%
Higher No Data
Risk Range:
High - Ocean, NJ •
Mean-0,31
0.81
Low - Hamilton, NY-0.06
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Hurricane
High Wind
High
Temperature
Hail Storm
Earthquake 2%
Drought
Coastal
Flooding
0%
65%
41%
20% 40%
Technological Hazards
Toxic Release 13%
83%
99%
Superfund Haxard
RCRA
Nuclear Hazard 14%
0% 20% 40% 60% 80% 100%
60%
100%
100%
99%
100%
100%
100%
Natural Land
90% Human and Property
Dual Benefit Land
80% 100%
Loss
47%
100%
47%
0% 20% 40% 60% 80% 100%
Figure 4.10 Map of Risk Domain scores by county for Region 2; proportion of natural exposures by natural hazard event type, technological
exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the
three primary exposure types in the region.
92
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
i Community
/ Preparedness
Natural \ v,
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.11 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 2. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 3
Region 3 of the EPA serves the states of Delaware, the District of Columbia, Maryland, Pennsylvania,
Virginia, and West Virginia. There is one federally recognized tribe in this region. The majority of the
Region is impacted by heatwaves, intense rainfall, and sea level rise. Washington, D.C. has been
impacted by extreme heat and rainfall events, the latter leading to flooding and infrastructure damage.
The cities' infrastructure is also a resiliency concern when it comes to evacuating during an emergency
93
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because bottlenecks could be an issue. With the exception of extreme heat events, Norfolk, VA, has
been burdened by the same concerns and damages as Washington D.C. Norfolk, a coastal city, is already
dealing with the impacts of erosion and sea level rise. Lewes, DE is another coastal city being impacted
by sea level rise and erosion. Pittsburgh, PA is forecasted to experience extreme rainfall, flooding and
erosion from storms, but also faces concerns about environmental degradation, infrastructure damage,
and eventual infrastructure failure. The EPA Region 3 Climate Change Adaptation Plan of May 2014
(USEPA-R3 2014) focuses on increasing tools and training materials available to help counties and
communities choose between the different adaptation strategies available to them.
A summary of the CRSI and domain scores is displayed in Figure 4.23. The CRSI score for Region 3
(2.934) is significantly below the national average and ranked 9th among the ten EPA Regions. The
regional Governance score is moderate, and the risk domain score is above average. The Society domain
score is average and has little influence on the CRSI score while the Built Environment and Natural
Environment domain score are below average and negatively affect the regional CRSI score. The
counties with higher resilience scores in EPA Region 3 are in upper Pennsylvania, eastern West
Virginia, and lower Virginia (Figure 4.24 and Table 4.4). The higher CRSI values in Region 3 occur in
Pennsylvania (15 counties), Virginia (8), Maryland (1) and West Virginia (1). The lower CRSI values (<
0.0) were predominantly in Virginia (20) and West Virginia (3). Risk domain scores were highest in
northwestern Chesapeake Bay counties, the District of Columbia, southeastern Virginia, northern
Delaware, and southeastern Pennsylvania.
Risk due to natural hazard events across Region 3 is examined in more detail in Figure 4.25. Natural
exposures due to natural hazard events associated with high and low temperatures, inland flooding, high
winds, hail and drought occur in virtually all counties in Region 3. Tornadoes and landslides also
represent a sizeable portion of the risk potential (95% and 80% of counties, respectively), while
representation of all other types of exposure due to natural hazard events are 0-25% of counties. RCRA
(Resource Conservation and Recovery Act) sites and Superfund sites represent a majority of
technological exposure indicator at 73% and 41%, respectively. Nuclear sites also contribute 7% of the
exposure potential. Natural hazard risk potential dominates the region, with only 12% of the risk
attributable to technological exposure potential. Risk ranges from a low score of 0.07 in Tucker County,
West Virginia to a high score of 0.71 in Chesapeake City, Virginia. The mean regional risk (0.27) falls
above the national average at 0.229.
Contributions of CRSI's twenty indicators to the overall Region 3 domain scores is displayed in Figure
4.26. The highest indicator scores contributing each domain include vacant structures (Built
Environment), demographic characteristics (Society) and natural resource conservation and community
preparedness (Governance). Secondary contributors include housing characteristics (Built
Environment), economic diversity and socio-economic factors (Society) and higher scores for the
exposure indicator influenced higher risk to natural hazard events in this Region. Lower contributors to
the Region 3 domain scores are communications and utility infrastructure (Built Environment) and
safety and security, and labor-trade services (Society).
94
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-as -0,4 -0.3 -0,2 -0.1 0 0.1 0.2 0.3 0.4 0.S 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.12 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 3, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
95
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Figure 4.24 The distributions of EPA Region 3 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
96
Natural Environment
Region 3
Society
Built Environment
-------
Table 4.4 Counties in EPA Region 3 with the highest CRSI values.
Region 3
Rank
County
1.
King William, Virginia
2.
Tioga, Pennsylvania
3.
Huntingdon, Pennsylvania
4.
Potter, Pennsylvania
5.
Tucker, West Virginia
6.
Warren, Pennsylvania
7.
Somerset, Pennsylvania
8.
Perry, Pennsylvania
9.
Clinton, Pennsylvania
10.
Bedford, Pennsylvania
11.
Powhatan, Virginia
12.
Forest, Pennsylvania
13.
Cumberland, Virginia
14.
Elk, Pennsylvania
15.
Southampton, Virginia
16.
Lycoming, Pennsylvania
17.
Somerset, Maryland
18.
Cameron, Pennsylvania
19.
Clarion, Pennsylvania
20.
King and Queen, Virginia
21.
Accomack, Virginia
22.
Sullivan, Pennsylvania
23.
Bradford, Pennsylvania
24.
Charlotte, Virginia
25.
Charles City, Virginia
97
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A
~
Lower
Score
Natural Hazards
Wildfire
Tornado
I
25%
Low
Temperature
High Wind
High
Temperature
Higher No Data
Hail Storm
Earthquake 0%
Risk Ranee:
High - Chesapeake City, VA - 0,71 Low - Tucker, WV - 0.07
Mean-0.27
Drought
Coastal
Flooding
0%
23%
20%
95%
100%
98%
100%
99%
100%
Technological Hazards
Toxic Release 9%
Superfund Haxard 41%
RCRA 73%
Nuclear Hazard 7%
0% 20% 40% 60% 80% 100%
40%
60%
80%
Natural Land
99% Human and Property
Dual Benefit Land
100%
Loss
37%
100%
57%
0% 20% 40% 60% 80% 100%
Figure 4.13 Map of Risk Domain scores by county for Region 3; proportion of natural exposures by climate event type, technological
exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as,
the three primary exposure types in the region.
98
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural ¦$,
v
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Health
Characteristics
Socio-
Economics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.14 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 3. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 4
EPA Region 4 includes Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South
Carolina, and Tennessee. Region 4 serves six federally recognized tribes in the southeast. This region is
threatened by sea level rise and extreme heat. Inland cities, such as Atlanta, GA, have suffered from
99
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rising temperature and extreme heat. Broward and Miami-Dade Counties in Florida have already been
impacted by sea level rise, and infrastructure damage from storms. Miami- Dade County, FL has also
suffered related issues with water quality and quantity as saltwater intrusion increases with sea level
rise. The EPA Region 4 Climate Change Adaptation Implementation Plan of 2014 (USEPA-R4 2014)
lists encouraging low-impact development and green infrastructure to abrogate increased storm events;
ensuring water conservation and efficiency are considered in water resource project permitting to
protect water quality and quantity; using dredge material to protect from sea level rise and storm surge,
and developing protocols for emergency dredging after hurricanes since they may become more
frequent or severe.
A summary of the EPA Region 4 CRSI and domain scores are shown in Figure 4.27. The overall CRSI,
1.443, is well below the national average and ranked lowest among EPA Regions. The CRSI values
reflects higher than average risk to natural hazard events, significantly lower Governance associated
with natural hazard events, and significantly lower than average Society and Built Environment and
lower than average Natural Environment domain scores. Figure 4.28 shows the distribution of these
scores among the counties in Region 4. The higher CRSI values are shown in some coastal North
Carolina and some Gulf of Mexico coastal counties in Florida. Areas of high risk to natural hazard
events are seen in the coastal regions of the Florida peninsula and the southern Appalachians. Lower
risk scores are seen in much of Georgia and the Big Bend area of Florida. Governance scores in Region
3 are higher in northern Kentucky and lowest in Appalachia and much of Alabama. Strong Built
Environment domain scores are seen in mid- and south peninsula Florida.
Table 4.5 lists the 25 counties in EPA Region 4 with the highest CRSI values. The higher scores are
seen in counties in North Carolina (11), South Carolina (5), Georgia (3), Florida (3) and Kentucky (1).
The counties with lower CRSI values occur almost exclusively in Georgia and in one county in
Kentucky.
Risk due to natural hazard events across Region 4 risk is examined in more detail in Figure 4.29.
Natural exposures due to natural hazard events are dominated by tornadoes, low and high temperatures,
inland flooding, high wind, hail and drought in all counties of Region 4. All other types of exposure
due to natural hazard events occur in Region 4 in 12-35% of its counties. RCRA (Recovery
Conservation and Recovery Act) sites and Superfund sites represent a majority of the technological
exposure indicator at 53% and 21% of counties, respectively. Nuclear sites also contribute a small
portion of the risk potential at 5% of counties. Natural hazard risk potential dominates the region, with
only 4% of the risk being attributable to technological exposure potential. Risk ranges from a low score
of 0.06 in Talbot, Terrell and Turner Counties, Georgia to a high score of 0.99 in Shelby County,
Tennessee. The mean regional risk (0.255) falls slightly above the national average of 0.229.
Contributions of CRSI's twenty indicators to the overall Region 4 domain scores is displayed in Figure
4.30. The strongest positive influences on the domain scores in Region 4 include vacant structures and
housing characteristics (Built Environment), and demographic characteristics (Society). Secondary
influences are seen in community preparedness and natural resource conservation (Governance),
economic diversity, social cohesion and socio-economic characteristics (Society), and exposure to
natural hazard events. Lower indicator scores are seen for safety and security and labor-trade services
(Society), and utility and communications infrastructure (Built Environment).
100
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EPA Region 4
Risk
CRSI
1.44
(Range: -3.63 - 6.S6)
5=; Governance
a =
V
Built Environment
Natural Environment
-0.5 -0.4 -O.J -0.2 -0.1 0 0.1 0.2 0.3 0.4 D.S 0,6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.15 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 4, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
101
-------
Region 4
CRSI
J >
JM
Governance
t
£'\
fc
Risk
4 %
i «
r *
V
£•* *>*.V
r
a f
Society
>U" , >
5«j «t
af, a a
'/ "Tj f^w
4* *S
Built Environment
* f ^
>
«r
4.
»
Score
Lower
Natural Environment
\
ftl
t ->
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' *m
1 HI"
*ki
. ¦ ¥
-
i
Si
f w JV
¦
w
1
Higher No Data
L
J
Figure 4.28 The distributions of EPA Region 4 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
102
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Table 4.5 Twenty-five counties in EPA Region 4 with the highest CRSI values.
Region 4
Rank
County
1.
Pender, North Carolina
2.
Washington, Georgia
3.
Jefferson, Florida
4.
Columbus, North Carolina
5.
Northampton, North Carolina
6.
Evans, Georgia
7.
Spencer, Kentucky
8.
Bertie, North Carolina
9.
Sampson, North Carolina
10.
Worth, Georgia
11.
Tattnall, Georgia
12.
Duplin, North Carolina
13.
Colleton, South Carolina
14.
Grady, Georgia
15.
Yadkin, North Carolina
16.
Martin, North Carolina
17.
Halifax, North Carolina
18.
Williamsburg, South Carolina
19.
Thomas, Georgia
20.
Orangeburg, South Carolina
21.
Colquitt, Georgia
22.
Levy, Florida
23.
Franklin, Florida
24.
Gates, North Carolina
25.
Appling, Georgia
103
-------
- * *r
c *3
Score
Higher No Data
Risk Range:
High- Shelby, TN- 0,99
Mean-0.25
Low - Turner, GA - 0.06
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Hurricane
High Wind
High
Temperature
Hail Storm
Earthquake
Orought
Coastal
Flooding
Natural Hazards
34%
35%
18%
12%
15%
100%
100%
100%
Technological Hazards
0%
20%
40%
60%
80%
Toxic Release 1%
Superfund Haxard 21%
RCRA 53%
Nuclear Hazard 5%
0% 20% 40% 60% 80% 100%
100%
100%
100%
Natural Land
100% Human and Property
Dual Benefit Land
100%
Loss
31%
100%
70%
0% 20% 40% 60% 80% 100%
Figure 4.16 Map of Risk Domain scores by county for Region 4; proportion of natural exposures by climate event type, technological
exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as,
the three primary exposure types in the region.
104
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
t Community
/ Preparedness
Natural ^
Resource '
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.17 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 4. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 5
Region 5 of the EPA includes Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Region 5
serves 35 federally recognized tribes in Michigan, Minnesota and Wisconsin. Region 5 is impacted by
extreme rainfall events that lead to flooding, and extreme heat. Minneapolis, MN has been affected by
warming trends, and flooding from extreme rainfall. Milwaukee, WI has suffered both cases of severe
drought and extreme rainfall that resulted in flooding and infrastructure damage. Grand Rapids, MI and
105
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Chicago, IL have both experienced rises in temperature, and extreme rainfall resulting in flooding,
erosion and infrastructure damage. Chicago has additional resilience issues of endemic crime, public
health, and infrastructure failure. Ann Arbor, MI is forecasted to suffer from rising temperatures. The
EPA Region 5 Climate Change Adaptation Implementation Plan of May 2014 (USEPA-R5 2014) states
that Region 5 is striving to use water source protection tools in order to improve the resilience of highly
vulnerable water systems. Additionally, remediation techniques for incorporating vegetation are in
review in order to become more tolerant of heat, excessive rain, and drought in the EPA's Superfund
processes.
A summary of the overall CRSI score and the domain scores for EPA Region 5 is shown in Figure 4.31.
The overall CRSI value of 5.476 is above the national average while the Risk domain score is slightly
lower than the national average (less risk). The Region 5 Governance domain score is relatively high as
is the Society domain score. The scores for the Built Environment and Natural Environment domains
are above the national average. Region 5 CRSI value ranked 5th among the ten EPA Regions.
The distribution of the overall CRSI values and the domain scores among the counties in Region 5 is
shown in Figure 4.32. Higher CRSI values, as shown in Figure 4.32 and Table 4.6, occur in the counties
of Wisconsin (10 counties), Indiana (3), Minnesota (7), Michigan (5) and Indiana (3). The counties with
the lower CRSI values (< 1.00) occur in Illinois (31), Indiana and Ohio (3 counties each), Michigan (2)
and Minnesota (1). Risk domain scores are generally the lowest in northern Minnesota, northern
Michigan and northwestern and middle Wisconsin. The highest risk domain scores occur along the
southwestern shore of Lake Michigan. Governance and Society domain scores are higher in many of the
counties of Wisconsin and Minnesota.
Risk due to natural hazard events across Region 5 risk is examined in more detail in Figure 4.33.
Natural exposures due to high and low temperatures, tornadoes, inland flooding high wind, hail and
drought occur in all Region 5 counties. Landslides and wildfires occur in 28% and 25% of counties,
respectively, while earthquakes occur in 7% of counties in Region 5. RCRA and Superfund dominated
the technological exposure indicator at 57% and 38% of counties, respectively. Nuclear exposure
potential is also a significant contributor to risk in this region in 5% of counties. Risk ranges from a low
score of 0.046 in Oceana County, Michigan to 0.779 in Will County, Illinois, with a regional average
score of 0.222 which is slightly lower than the national average (0.229).
The contributions of the 20 indicators to EPA Region 5 domain scores are shown in Figure 4.34. The
strongest contributors to domain scores are natural resource conservation (Governance), demographic
characteristics (Society), and vacant structures (Built Environment). Secondary contributors include
economic diversity, social cohesion, socio-economic characteristics and health characteristics (Society),
housing characteristics (Built Environment), and personal and community preparedness (Governance).
Lower indicator scores are shown for communication and utilities infrastructure in the Built
Environment domain and safety and security and labor and trade services in the Society domain.
106
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-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.18 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 5, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
107
-------
Region 5
CRSI
'-W-i
Risk
> U *
y ¦%
i' *
.»ft >
• ~ «
% >**
Built Environment
Pfc-fe*
JL
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* f
i
r
tf.
Score
Lower
• £« ¦ *
|- T* - «*
;• v
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*
Natural Environment
»
/
y,
V JaB *•
-------
Table 4.6 Twenty-five counties in EPA Region 5 with the highest CRSI values.
Region 5
Rank
County
1.
Lincoln, Minnesota
2.
Itasca, Minnesota
3.
Florence, Wisconsin
4.
Newton, Indiana
5.
Sawyer, Wisconsin
6.
Kalkaska, Michigan
7.
Oneida, Wisconsin
8.
Oceana, Michigan
9.
Vilas, Wisconsin
10.
Pipestone, Minnesota
11.
Koochiching, Minnesota
12.
Price, Wisconsin
13.
Washburn, Wisconsin
14.
Fillmore, Minnesota
15.
Shawano, Wisconsin
16.
Forest, Wisconsin
17.
Benton, Indiana
18.
Huron, Michigan
19.
Morrison, Minnesota
20.
Sanilac, Michigan
21.
Pulaski, Indiana
22.
Grant, Minnesota
23.
Ashland, Wisconsin
24.
Missaukee, Michigan
25.
Polk, Wisconsin
109
-------
Natural Hazards
1 ¦¦ *
t
mi * V
•v-.T
V
t
+ *
« V
Higher No Data
Risk Ranee:
High - Will, II - 0.78 low - Oceana, Ml - 0.05
Mean - 0.22
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake 7%
Drought
Coastal
Flooding
0%
0%
25%
28%
99%
100%
100%
100%
Technological Hazards
Toxic Release 2%
Superfund Haxard 38%
RCRA 57%
Nuclear Hazard 5%
0% 20% 40% 60% 80% 100%
100%
Loss
20%
40%
60%
100%
Natural Land 23%
100% Human and Property
Dual Benefit Land 71%
80% 100% 0% 20% 40% 60% 80% 100%
98%
Figure 4.19 Map of Risk Domain scores by county for Region 5; proportion of natural exposures by natural hazard event type,
technological exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region
110
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
/ Community
/ Preparedness
Natural *5,
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Social
Cohesion
Security
Figure 4.20 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 5. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 6
Region 6 of the EPA serves Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. Region 6
includes 66 federally recognized tribes in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. The
entire region is threatened by extreme heat events and rising temperatures. For example, Tucson, AZ,
Houston, TX, Dallas, TX, and El Paso, TX, have all experienced different issues due to warming trends.
In cities where the heat has been, or is projected to be, accompanied by drought, such as Houston, El
ill
-------
Paso and Tucson, water quality and quantity sometimes becomes a concern. In New Mexico rising
temperatures, combined with drought, and insect outbreaks, has led to increased wildfire risk. In Dallas,
TX heat waves have caused energy shortages. In Houston, TX, Dallas, TX, and El Paso, TX there has
been extreme rainfall and flooding too, resulting in erosion and damages to infrastructure in Houston;
and infrastructure damage and even failure in Dallas. The Region's coastal states, specifically Louisiana,
are threatened by sea level rise. New Orleans, LA has not only suffered infrastructure damage and
failure, but has also had issues with storm surge and erosion. Some cities in the region face other
compounding resilience issues such as social inequity in El Paso, TX and Tucson, AZ, and severe drug
and alcohol abuse in El Paso. The EPA Region 6 Climate Change Adaptation Implementation Plan of
May 2014 (USEPA-R6 2014) suggested mitigating the impact of sea level rise and coastal land loss to
erosion using restoration projects developed and implemented through three National Estuary Programs
in the region, Climate Ready Estuaries Programs, and the Coastal Wetlands Planning, Protection and
Restoration Act (CWPPRA); with a goal of protecting or restoring 9,000 acres of coastal wetlands.
A summary of the EPA Region 6 overall CRSI and the domain scores is depicted in Figure 4.35. The
overall CRSI score of 3.06 is significantly less than the national average ranks 8th among EPA Regions.
The score appears to be the result of lower than average Governance for natural hazard events and a
lower than average score for the Society domain and average scores for Built Environment and Natural
Environment domains. The distribution of these scores across the counties of Region 6 is shown in
Figure 4.36. The higher CRSI values in EPA Region 6 are in New Mexico and some scattered counties
in Texas and Oklahoma. The highest scores for the risk domain occur in coastal Louisiana, northeastern
coastal Texas and central Oklahoma. Higher Governance and Society domain scores occur in northern
Oklahoma and New Mexico. Table 4.7 lists the 25 counties with the highest CRSI values in EPA
Region 6. These counties are in New Mexico (14), Texas (10) and Oklahoma (1). The counties with the
lowest CRSI values are in Texas (9) and Oklahoma (1).
Risk due to natural hazard events across Region 6 risk is examined in more detail in Figure 4.37.
Natural exposures due to High and low temperatures, tornadoes, inland flooding, high wind, hail and
drought occur in virtually all Region countiesY Wildfires occur in nearly half of the counties (45%) and
all other hazards occur in 6-22% of region 6 counties. RCRA (Resource Conservation and Recovery
Act) and Superfund sites represent a majority of the technological exposure indicator at 31% and 18%
of counties, respectively. Most of exposure comes from natural hazard events, with only 2% resulting
from proximity to anthropogenic, technologic infrastructure. Region 6 risk ranges from the lowest score
of 0.062 in the Winkler County, Texas to one of the higher scores in the nation, 0.907 in Ascension
Parish, Louisiana with a regional average (0.239) slightly higher than the national average at 0.229.
The contributions of the 20 indicators to the domains that comprise CRSI are shown in Figure 4.38 for
EPA Region 6. The natural resource conservation indicator score is the strongest contributor to the
Governance domain. Secondary contributions are associated with vacant structures and housing
characteristics (Built Environment), and demographic characteristics (Society). Weaker contributors are
transportation and communications infrastructure scores in the Built Environment domain and (Built
Environment), and labor-trade services scores in the Society domain.
112
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-0.3
0.1
0.3
0.8
0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.21 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for the U.S,
along with domain median adjusted scores showing influence of each domain on final CRSI score (dark
colored bars).
113
-------
Region 6
CRSI Risk
i aHI * j f
~ ra i r ,4 f
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Score
Lower -« >• Higher No Data
: ¦—¦ ~
Figure 4.36 The distributions of EPA Region 6 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
114
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Table 4.7 Twenty-five counties in EPA Region 6 with the highest CRSI values.
Region 6
Rank
County
1.
McKinley, New Mexico
2.
San Juan, New Mexico
3.
Luna, New Mexico
4.
Sierra, New Mexico
5.
Wilson, Texas
6.
Grant, New Mexico
7.
Otero, New Mexico
8.
Sandoval, New Mexico
9.
Rio Arriba, New Mexico
10.
Uvalde, Texas
11.
Erath, Texas
12.
Dona Ana, New Mexico
13.
Taos, New Mexico
14.
Wharton, Texas
15.
Clay, Texas
16.
Webb, Texas
17.
Cibola, New Mexico
18.
Santa Fe, New Mexico
19.
Val Verde, Texas
20.
Quay, New Mexico
21.
Torrance, New Mexico
22.
Wise, Texas
23.
Osage, Oklahoma
24.
Live Oak, Texas
25.
Bee, Texas
115
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*
/ I*5
. »
&
\
*
Lower
Score
Higher n0 Data
Risk Range:
High —Ascension, LA-0.91
Mean-0,24
Low- Winkler,TX — 0.06
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Hurricane
High Wind
High
Temperature
Hail Storm
Earthquake 6%
Drought
Natural Hazards
45%
22%
19%
Coastal
Flooding
10%
99%
99%
100%
100%
100%
100%
Technological Hazards
Toxic Release 0%
Superfund Haxard 18%
RCRA 31%
¦
Nuclear Hazard 4%
0% 20% 40% 60% 80% 100%
Loss
99%
Natural Land 17%
100% Human and Property
Dual Benefit Land 76%
80% 100% 0% 20% 40% 60% 80% 100%
Figure 4.22 Map of Risk Domain scores by county for Region 6; proportion of natural exposures by climate event type, technological
exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as,
the three primary exposure types in the region.
116
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Ecosystem
Extent i
Exposure
-\ Condition
Vacant
Structures
/ Community
/ Preparedness
Natural %
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.23 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 6. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 7
Region 7 of the EPA serves Iowa, Kansas, Missouri, and Nebraska. Region 7 serves 7 federally
recognized tribes in Kansas, Nebraska, and Iowa. All of Region 7 has experienced extreme heat and
rising temperatures, in some instances creating increased demand for resources such as water and
energy. Parts of the region have also witnessed extreme rainfall events, and flooding. Dubuque, IA has
suffered crop failures due to extreme heat and severe drought, and infrastaicture damages due to
extreme rainfall and flooding. St. Louis, MO is projected to experience these same impacts of extreme
117
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heat and rainfall events, with the additional concern that rainfall will cause additional erosion. St. Louis
has other resilience issues, such as social inequity, endemic crime, and civil unrest. Most of the actions
being taken by Region 7 under the regional Climate Change Adaptation Implementation Plan
(USEPAR7 2014) are focused on the availability of water. Actions include prioritizing watershed
improvements to sources of drinking water impacted by nutrients and other contaminants, promoting
precipitation neutral technologies and practices for site remediation, and helping work within the region
to incorporate water conservation practices, energy conservation and green infrastructure.
A summary of the overall CRSI score and the domain scores for EPA Region 7 are provided in Figure
4.39. The overall CRSI score of 4.469 is slightly above the national average and ranks 6th among the
EPA Regions. While the Risk domain score is relatively low (0.209), the Governance and Society
domain scores are relatively high. The Built Environment and Natural Environment domain scores are
lower than the national average. Figure 4.40 shows the spatial distribution of these domain scores
across the counties comprising EPA Region 7. Table 4.8 shows the highest CRSI values are scattered
through the region with the highest county scores occuring in Iowa and Kansas (8 counties each),
Missouri (7) and Nebraska (1). The counties with lower CRSI values (< 1.0) are primarily in Nebraska
(19 counties), Missouri (4), and one county in Kansas. Lower Governance scores are seen in southern
Missouri. Risk due to natural hazard events across Region 7 is examined in more detail in Figure 4.41.
Natural exposures due to natural hazard events are dominated by tornadoes, high and low temperatures,
drought, hail and high wind in all counties. Wildfires occurred in 25% of counties while earthquakes
and landslides occurred in 6-8% of counties. RCRA and Superfund sites evenly influenced the
technological exposure indicator at 25% and 17%, respectively. Most risk exposure comes from natural
hazard events, with only 3% resulting from proximity to anthropogenic, technology. Risk ranges from a
low score of 0.05 in Pierce County, Nebraska to 0.853 in St. Louis City, Missouri with a regional
average( (0.21) being slightly under the national of 0.229.
The contributions of the twenty indicators to the overall domain scores for EPA Region 7 are shown in
Figure 4.42. The strongest contributors are natural resource conservation scores (Governance), and
vacant structures (Built Environment). Secondary contributors are the housing characteristics indicator
score in the Built Environment domain and demographic characteristics, social cohesion,
socioeconomic characteristics, economic diversity and health characteristics indicator scores in the
Society domain. Communication and utility infrastructures scores (Built Environment), and safety and
security scores (Society) are weaker contributors.
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Domain Score (lighter shade bar)/Mediari Adjusted Scorejdarker shade bar)
Figure 4.24 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 7, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
119
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Region 7
CRSI
m
l m9
|_ |«TT^
¦ ¦_¦ ¦¦
Risk
Governance
I lir.";
IMTP
Society
Built Environment
Natural Environment
. 1
*¦
ft
T
I- *
¦ 5
Score
Lower
Higher No Data
wm^m ~
Figure 4.40 The distributions of EPA Region 7 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
120
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Table 4.8 Twenty-five highest CRSI values in the counties of EPA Region 7.
Region 7
Rank
County
1.
Pierce, Nebraska
2.
Chickasaw, Iowa
3.
Wabaunsee, Kansas
4.
Marshall, Kansas
5.
Richardson, Nebraska
6.
Clayton, Iowa
7.
Winneshiek, Iowa
8.
Ottawa, Kansas
9.
Fayette, Iowa
10.
Macon, Missouri
11.
Brown, Kansas
12.
Miami, Kansas
13.
Washington, Iowa
14.
Shelby, Missouri
15.
Nodaway, Missouri
16.
Shelby, Iowa
17.
Bremer, Iowa
18.
Washington, Kansas
19.
Lafayette, Missouri
20.
Clinton, Missouri
21.
Nemaha, Kansas
22.
Vernon, Missouri
23.
Cherokee, Iowa
24.
Pottawatomie, Kansas
25.
Osage, Missouri
121
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^Hl
I ¦" -
Wildfire
Tornado
Low
Temperature
8%
*
I t
Higher No Data
Risk Ranee:
High - St. Louis, MO - 0..85
Mean -0.21
Low - Pierce, NE - 0.05
Landslide
Inland Flooding
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake 6%
Drought
Coastal
Flooding
0%
Natural Hazards
24%
100%
Technological Hazards
100%
100%
0%
20%
60%
Toxic Release 1%
Superfund Haxard 17%
RCRA 25%
Nuclear Hazard 2%
0% 20% 40% 60% 80% 100%
100%
100%
Loss
100%
Natural Land 9%
100% Human and Property 99%
Dual Benefit Land 76%
80% 100% 0% 20% 40% 60% 80% 100%
Figure 4.25 Map of Risk Domain scores by county for Region 7; proportion of natural exposures by natural hazard event type,
technological exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region.
122
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
/ Community
/ Preparedness
Natural ^
Resource *
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.26 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 7. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
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EPA Region 8
Region 8 of the EPA includes Colorado, Montana, North Dakota, South Dakota, Utah, and Wyoming.
Region 8 serves 27 federally recognized tribes, located in Colorado, Montana, North Dakota, South
Dakota, Utah, and Wyoming. The Region is threatened by extreme heat events and rising temperatures
and at risk of increased demand for energy and water resources as a result. These rising temperatures in
combination with drought and insect outbreaks, has increased the risk of wildfire for some parts of the
region, specifically Utah and Colorado. Boulder and Colorado Springs, CO have both experienced
temperature rises, extensive wildfires, air quality issues, and damages to infrastructure. Boulder, CO
has experienced extreme rainfall and flooding too. In Boulder, both extreme heat and extreme rainfall
have to be considered alongside other resilience issues like invasive species, disease and affordable
housing. Denver, CO has experienced extreme heat, temperature rises and air quality issues, but not
wildfires or infrastructure damage. It is projected that Denver will eventually experience extensive
wildfires as well. Salt Lake City, UT is forecasted to face extreme heat and temperature rises potentially
leading to wildfire risks, and water quality and quantity concerns. Efforts to improve resilience in EPA
Region 8 include working with states and tribal nations to integrate climate considerations into their
water programs and consider how funding mechanisms may support increased investments in water
infrastructure (USEPA-R8 2014).
A summary of the overall CRSI score and the domain scores for EPA Region 8 is provided in Figure
4.43. The CRSI value for Region 8 is 6.477, above the national average and ranking 3rd highest among
the EPA Regions. This Region also has a low Risk score (0.162) indicating a less risk to acute natural
hazard events. The Built Environment domain score is moderate, and the Governance and Society
domain scores are well above the national average. The spatial distribution of these scores among the
counties in Region 8 is shown in Figure 4.44. Higher overall CRSI values are seen in western Montana,
most of Wyoming and along and below the eastern slope of the Rocky Mountains in Colorado. The
highest overall CRSI values are shown in Table 4.9 and includes counties in Montana (7 counties),
Colorado (6), and South Dakota (5), North Dakota (4), Wyoming (2) and Utah (1). The counties with
lower CRSI values (<1.0) are found in South Dakota (6), Colorado (6) and Montana (3) and North
Dakota 92). Risk for natural hazard events is relatively low throughout the region.
Risk due to natural hazard events across Region 8 is examined in more detail in Figure 4.45. Natural
exposure due to natural hazard events are dominated by high and low temperatures, drought and inland
flooding in all Region 8 counties. Other high risk exposures include hail, wind, tornadoes and wildfires
(78-98% of counties). Landslides occurred in 66% of counties while earthquakes occurred in 18% of
counties. Superfund sites and RCRA sites influence a small number of counties through the
technological exposure indicator at 18% and 7%, respectively. RCRA sites have little influence and
nuclear exposure potential is non-existent. Most exposure comes from natural hazard events, with only
1% resulting from proximity to technological hazards. Risk ranges from a low score of 0.05 in Toole
County, Montana to 0.77 in Salt Lake County, Utah with a regional average (0.16) well under the
national at 0.229.
The contributions of the twenty indicators to the domain scores that comprise CRSI shown in Figure
4.46 for Region 8. The strongest contributions come from the natural resource conservation indicator
(Governance), and the vacant structures indicator (Built Environment). Secondary contributions come
from housing characteristics (Built Environment); socio-economic characteristics, demographic
characteristics and health characteristics (Society); and, exposure (risk).
124
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EPA Region 8
a Risk
2LI\
CRSI
6.48
(Range: -2.96-20.90)
|a=| Governance
4"
Society
Built Environment
Natural Environmen
1
1 1
-0.5 -0.4 -0.3 -0,2 -04 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score [lighter shade bar)/Median Adjusted Scorefdarker shade bar)
Figure 4.43 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 8, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
125
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Region 8
- m 3./
Score
Lower •< ~ Higher No Data
Governance
Society
Built Environment
Natural Environment
Figure 4.44 The distributions of EPA Region 8 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
126
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Table 4.9 Twenty-five counties in EPA Region 8 with the highest CRSI values.
Region 8
Rank
County
1.
Roberts, South Dakota
2.
Flathead, Montana
3.
Day, South Dakota
4.
Daniels, Montana
5.
Carbon, Wyoming
6.
Uinta, Wyoming
7.
Deuel, South Dakota
8.
Lincoln, Montana
9.
Ouray, Colorado
10.
Ravalli, Montana
11.
Grant, South Dakota
12.
Pembina, North Dakota
13.
Pitkin, Colorado
14.
Gunnison, Colorado
15.
San Miguel, Colorado
16.
Duchesne, Utah
17.
Ward, North Dakota
18.
Beaverhead, Montana
19.
Garfield, Colorado
20.
Teton, Montana
21.
Granite, Montana
22.
Chaffee, Colorado
23.
McLean, North Dakota
24.
Jefferson, Montana
25.
Hamlin, South Dakota
127
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I
Lower
Score
Higher No Data
Risk Ranee:
High-Salt Lake, UT-0.77
Mean - 0.16
Low - Toole, MT - 0.05
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Natural Hazards
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake
Drought
Coastal
Flooding
18%
0%
78%
66%
88%
100%
100%
Technological Hazards
Toxic Release 0%
Superfund Haxard 18%
RCRA 7%
Nuclear Hazard 0%
0% 20% 40% 60% 80% 100%
100%
98%
Loss
91%
0%
20%
40%
60%
Natural Land 7%
100% Human and Property
Dual Benefit Land 61%
80% 100% 0% 20% 40% 60% 80% 100%
Figure 4.27 Map of Risk Domain scores by county for Region 8; proportion of natural exposures by natural hazard event type,
technological exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region.
128
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
t Community
' Preparedness
Natural -5,
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
¦ r-ggi
— | 1
/ /
/ jt-J / /
Ji x
Figure 4.28 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 8. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 9
Region 9 of the EPA includes Arizona, California, Hawaii, and Nevada. Also included in this region are
the Pacific Islands (Northern Marianas, Guam, and American Samoa). Region 9 serves 148 federally
recognized tribes in Arizona, California, and Nevada. Across the region heat, drought, and insect
outbreaks have all led to increased wildfires. In Hawaii, increased ocean temperatures have heightened
risks of coral bleaching and disease. Hawaii also faces increased coastal flooding and erosion concerns.
In the San Diego Harbor region of California, extreme heat, rising temperatures, severe drought, and
extensive wildfire are all projected. Los Angeles, CA has suffered severe drought, issues in water quality
129
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and quantity, and infrastructure damage. Earthquakes and tsunamis are additional concerns in regards to
resilience in Los Angeles. Oakland, CA is projected to experience problems associated with sea level
rise, in addition to the resilience issues it already faces around social inequity, earthquakes, and
affordable housing. Across the Bay in San Francisco, CA earthquakes are also a concern, and
projections of rising temperatures and severe drought have will increase risk of wildfire. Berkley, CA
has experienced extreme heat and warming, extensive wildfires, and additional resilience issues around
earthquakes. The EPA Region 9 Climate Change Adaptation Implementation Plan (USEPA-R9 2014)
states that regional resilience goals include the promotion of water efficiency, conservation, and
recycling. The region also has a Coral Reef Strategy to reduce local pollution and increase coral reef
climate change resiliency.
A summary of the overall CRSI domain scores for EPA Region 9 is presented in Figure 4.47. The
overall CRSI score (5.524) is above the national average and ranks 4th among the ten EPA Regions.
The risk domain score is above the national average and the Governance for natural hazard events
domain score is below the national average. The Built Environment domain score is the highest in the
nation and the Natural Environment domain score is moderate to high. The spatial distribution of these
scores among the counties in EPA Region 9 is shown in Figure 4.48 with some of the higher CRSI
values in Hawaii, northern Nevada, northern Arizona and northern California. Table 4.10 shows the
counties with the highest CRSI values in Region 9 are in Arizona (9 counties), California and Nevada (6
counties each) and Hawaii (4). The counties with lower CRSI values (<1.5) are in California (2),
Nevada (1) and Hawaii (1). Low risk for natural hazard events is shown in Figure 4.48 for much of
Arizona and Nevada and all of Hawaii. High Governance domain scores are shown for Hawaii and
much of Nevada and Arizona as well as southern California. Higher Built Environment domain scores
are seen in southern California, a swath through the middle of Arizona and the Las Vegas region of
Nevada.
Risk due to natural hazard events across Region 9 is examined in more detail in Figure 4.49. Natural
exposures due to natural hazard events are dominated by wildfires, low and high temperatures, inland
flooding, and drought in virtually all counties (>97%). Earthquakes, tornadoes landslides high winds,
hail occur in 81-86% of Region 9 counties. RCRA (Resource Conservation and Recovery Act) sites and
Superfund sites represent a majority of technological exposure indicator at 58% and 49% of counties,
respectively. Nuclear sites also contribute a sizeable portion of risk potential in this region at 12% of
counties. Most exposure comes from natural hazard events, with only 5% resulting from proximity to
technological hazards. Risk ranges from a low score of 0.06 in Maui County, HI to 0.76 in Orange
County, California; with a regional average (0.23) at about the national at 0.229.
The contributions of the 20 indicators to the domains that comprise CRSI for Region 9 are shown in
Figure 4.50. The strongest contributors to the Built Environment score are vacant structure and housing
characteristics., Demographic characteristics (Society), exposure to natural hazard events (Risk), and
natural resource conservation (Governance) also show strong contributions to domain scores. Secondary
contributor indicators scores include health characteristics and economic diversity (Society), as well as
ecosystem type extent (Natural Environment). Weak contributions are shown for the following
indicators: community preparedness (Governance), safety and security and labor-trade services
(Society), and condition of ecosystems (Natural Environment).
130
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EPA Region 9
Risk
CRSI
5.52
•JLi/ (Range: 1.18-27.62)
|a =
,£—\ Governance
a==
1 Society J
Built Environment
M
~ Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.S 0.6 0.7
Domain Score (lighter shade bar)/Medlan Adjusted Score(darker shade bar)
Figure 4.29 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 9, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
131
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Natural Environment
Score
Lower « ~ Higher No Data
1=1
Figure 4.48 The distributions of EPA Region 9 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
Built Environment
132
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Table 4.10 Twenty-five counties in EPA Region 9 with the highest CRSI values.
Region 9
Rank
County
1.
Maui, Hawaii
2.
Kauai, Hawaii
3.
Hawaii, Hawaii
4.
Honolulu, Hawaii
5.
Coconino, Arizona
6.
Mono, California
7.
Navajo, Arizona
8.
Lassen, California
9.
Churchill, Nevada
10.
White Pine, Nevada
11.
Humboldt, Nevada
12.
Apache, Arizona
13.
Nye, Nevada
14.
Yavapai, Arizona
15.
Elko, Nevada
16.
Imperial, California
17.
Washoe, Nevada
18.
Mohave, Arizona
19.
Cochise, Arizona
20.
Humboldt, California
21.
Graham, Arizona
22.
Gila, Arizona
23.
San Bernardino, California
24.
Santa Barbara, California
25.
Pima, Arizona
133
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Natural Hazards
r
Score
Wildfire
Tornado
Low
Temperature
Landslide
Higher No Data
Risk Ranee:
High - Orange, CA - 0.76 Low- Maui, HI-0,06
Mean - 0.23
Inland Flooding
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake
Drought
Coastal
Flooding
0%
22%
20%
97%
86%
81%
99%
99%
84%
84%
86%
100%
Technological Hazards
Toxic Release 4%
Superfund Haxard 49%
RCRA 58%
Nuclear Hazard 12%
0% 20% 40% 60% 80% 100%
Loss
40%
60%
80%
Natural Land 20%
100% Human and Property 99%
Dual Benefit Land 62%
100% 0% 20% 40% 60% 80% 100%
Figure 4.30 Map of Risk Domain scores by county for Region 9; proportion of natural exposures by natural hazard event type,
technological exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region.
134
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
f Community
Preparedness
Natural *£,
Resource ¦
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Health
Characteristics
Socio-
Economics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.31 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 9. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
135
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EPA Region 10
Region 10 of the EPA includes Alaska, Idaho, Oregon, and Washington. EPA Region 10 office serves
271 federally recognized tribes in Alaska, Idaho, Oregon, and Washington. Regional threats include
increasing ocean acidity, sea level rise, erosion, inundation, infrastructure risks. The combination of
insect outbreaks, tree disease and wildfire are resulting in widespread tree die-offs across Region 10.
Alaska has experienced significant temperature rises, increasing at double the speed of the rest of the
United States, causing glaciers to shrink and sea ice to recede. The permafrost is thawing, leading to
more wildfires. Eugene, OR has experienced severe drought and extensive wildfire. Changing ocean
temperatures have allowed for more invasive species and diminishing cold water species. Beaverton,
OR is projected to experience temperature rises, severe drought, extensive wildfires, extreme rainfall,
flooding, and issues in water quality and quantity. King County, WA has been impacted by extreme
heat, extreme rainfall, flooding, erosion, infrastructure damage and sea level rise. According to the EPA
Region 10 Climate Change Adaptation Implementation Plan (USEPA-R10), regional actions to improve
resilience include using Water Sense to encourage water efficiency, including ocean acidification
language in NEPA review comments, and incorporating green infrastructure as part of settlement
agreements.
A summary of the overall CSRI score and the domain scores for EPA Region 10 is shown in Figure
4.51. The overall CRSI score of 15.395 - is the highest in the nation. The Risk domain score is below
the national averages. The Society domain score is similar to national average and the Governance,
Built Environment and Natural Environment domain scores are well above the national average. The
spatial distribution of the overall CRSI score and the domain scores among the counties of EPA Region
10 are shown in Figure 4.52. Table 4.11 shows the higher CRSI values occur in Alaska (10 boroughs)
and Idaho (5 counties). The lower CRSI values (< 1.50) occur in Washington (1 county) and Idaho (1).
Overall risk for natural hazard events appears moderate through the region while the Governance for
natural hazard events scores are lower in southern Oregon.
Risk due to natural hazard events across Region 10 is examined in more detail in Figure 4.53. Natural
exposures due to natural hazard events are dominated by high and low temperatures, inland flooding and
drought (>95% of counties). Wildfires (83% of counties in Region 10), high winds (80%), hail (70%),
landslides {61%) and earthquakes (46%) are the remaining dominant natural hazard influences in
Region 10. Superfund sites and RCRA (Resource Conservation and Recovery Act) sites represent the
majority of the technological exposure indicator at 65% and 27%, respectively. RCRA and nuclear sites
contribute a negligible portion of risk potential in this region at 32% and 18% of counties, respectively.
Most exposure comes from natural hazard events, with only 2% resulting from proximity to
technological hazards. Risk ranges from the lowest score in the nation at 0.010 in Kodiak Island
Borough, Alaska to 0.57 in Pierce County, Washington, with a regional average well below the national
at 2.42.
The contributions of the twenty indicators to the domain scores that comprise CRSI are shown in Figure
4.54 for EPA Region 10. The strongest contributor to the Built Environment domain score is vacant
structures, natural resource conservation indicator scores (Governance) and lower exposure and loss risk
scores (Risk) are also strong contributors. Secondary contributions are shown for the following
indicators: housing characteristics (Built Environment); demographic characteristics, health
characteristics and economic diversity (Society); and extent of ecosystems (Natural Environment). The
136
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weakest contribution scores are for safety and security (Society), utility infrastructure (Built
Environment) and ecosystem condition (Natural Environment).
EPA Region 10
Risk
CRSI
15.40
/ (Range: -0.73 -189.17)
^ Governance
Socie
Built Environment
Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Domain Score (lighter shade bar)/Mediari Adjusted Score(darker shade bar)
Figure 4.32 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 10, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
137
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Region 10
CRSI
Risk
Governance
Built Environment
Score
Lower
Society
k Hi
i\
Natural Environment
Higher No Data
Figure 4.33 The distributions of EPA Region 10 CRSI values and domain scores (Risk, Governance,
Society, Built Environment and Natural Environment).
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Table 4.11 Twenty-five counties in EPA Region 10 with the highest CRSI values.
Region 10
Rank
County
1.
Kodiak Island, Alaska
2.
Juneau City and, Alaska
3.
Ketchikan Gateway, Alaska
4.
Aleutians East, Alaska
5.
Hoonah-Angoon, Alaska
6.
Haines, Alaska
7.
Prince of Wales-Hyder, Alaska
8.
North Slope, Alaska
9.
Sitka City and, Alaska
10.
Dillingham, Alaska
11.
Petersburg, Alaska
12.
Bristol Bay, Alaska
13.
Kenai Peninsula, Alaska
14.
Wrangell City and, Alaska
15.
Fairbanks North Star, Alaska
16.
Skagway Municipality, Alaska
17.
Aleutians West, Alaska
18.
Yakutat City and, Alaska
19.
Anchorage Municipality, Alaska
20.
Latah, Idaho
21.
Lake and Peninsula, Alaska
22.
Bonner, Idaho
23.
Valley, Idaho
24.
Boundary, Idaho
25.
Benewah, Idaho
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Score
*
Higher No Data
Wildfire
Tornado
Low
Temperature
Landslide
Inland Flooding
Natural Hazards
Hurricane 0%
High Wind
High
Temperature
Hail Storm
Earthquake
Drought
Coastal
18%
Risk Range:
High - Pierce
Mean -0.14
83%
64%
67%
98%
80%
99%
70%
46%
Technological Hazards
100%
Toxic Release 1%
Superfund Haxard 32%
RCRA 18%
Nuclear Hazard 3%
0% 20% 40% 60% 80% 100%
Loss
Flooding
WA-0.S7 Low-Kodiak Island, AK-0,01 o% 20% 40% 60% 80% 100%
Natural Land 9K
95% Human and Property 95%
Dual Benefit Land 51%
0% 20% 40% 60% 80% 100%
Figure 4.34 Map of Risk Domain scores by county for Region 10; proportion of natural exposures by natural hazard event type,
technological exposures, losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region
140
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Ecosystem
Extent
Exposure
Condition
Vacant
Structures
/ Community
t Preparedness
¦a Natural "5,
\ ^
Resource •
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Housing
Characteristics
Demographics
Economic
Diversity
Communication
Infrastructure
Socio-
Economics
Health
Characteristics
Social
Services
Labor-Trade
Services
Safety
Social &
Cohesion Security
Figure 4.35 Polar plot showing the contribution of the 20 indicators associated with the domain scores for the
EPA Region 10. The length of the bars corresponds to the indicator score. Within a domain, the higher indicator
scores show a greater contribution to the domain score (sum of indicator scores).
141
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7. Future Directions for Community Resilience to
Extreme Weather Events
Every year, U.S. counties and communities face devastating losses caused by
weather-related disasters. Fires, floods, storms, other hazards and their
associated consequences have significant impacts on counties and communities,
the economy, infrastructure and the environment. The U.S. has recently
experienced several large scale and devastating natural hazard events, including
catastrophic wildfires, far reaching floods, and damaging storms. Such events
can have personal, social, economic and environmental impacts that take many years to
dissipate. The increasing prominence of extreme weather events makes it critical for governments,
businesses and individuals to examine their anticipatory adaptation and organizational resilience to these
events (Linnenluecke et al. 2012). The private sector and all levels of government are embracing
resilience as a holistic, proactive framework to reduce risk, improve services, adapt to changing
conditions, and empower citizens (e.g., National Disaster Resilience Competition; HUD 2017;
Leadership in Community Resilience; NLC 2016, 2017).
The U.S. has and continues to cope well with natural hazard events, through established and cooperative
emergency management arrangements, effective capabilities, and dedicated professional and volunteer
personnel. Americans are also renowned for their resilience to hardship, including the ability to innovate
and adapt, a strong community spirit that supports those in need and the self-reliance to withstand and
recover from disasters. A collective responsibility for resilience is needed to effectively build capacities
at multiple scales.
Our desire to have counties and communities that are minimally impacted by natural hazard events is
nearly impossible without a strong recoverability plan and its execution following an event. These plans
and their execution maintain a community at a significant distance from ecological, economic and social
tipping points (e.g., stability, sustainability, joblessness, social inequity, ecosystem condition). Little
attention has been given to the interconnectedness of the aspects of resilience (Summers et al. 2014) as
they relate to a community's natural hazard resilience. A community may be naturally vulnerable to
natural hazard events or vulnerable through anthropogenic activities but its resilience to these
vulnerabilities is guided by the combination of environmental, social, economic and governance drivers.
Given the increasing regularity and severity of natural hazard events, U.S. national, state and local
governments have recognized that an integrated, coordinated and cooperative effort is required to
enhance their capacities to withstand and recover from weather-related emergencies and disasters. A
disaster resilient community is one that works together to understand and manage the risks that it
confronts. Disaster resilience is the collective responsibility of all sectors of society, including all levels
of government, business, the non-government sector and individuals. If all these sectors work together
with a united focus and a shared sense of responsibility to improve disaster resilience, they will be far
more effective than the individual efforts of any one sector.
Potential role of governments
Governments, at all levels, have a significant role in strengthening the nation's resilience to disasters:
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• Developing and implementing effective, risk-based land management and planning arrangements
and other mitigation activities;
• Having effective arrangements in place to inform people about how to assess risks and reduce
their exposure and vulnerability to hazards;
• Having clear and effective education systems so people understand what options are available
and what the best course of action is in responding to a hazard as it approaches;
Supporting individuals and counties and communities to prepare for extreme events;
• Ensuring the most effective, well-coordinated response from our emergency services and
volunteers when disaster hits; and
• Working in a swift, compassionate and pragmatic way to help counties and communities recover
from devastation and to learn, innovate and adapt in the aftermath of disastrous events.
Local, state and national governments are working collectively to incorporate the principle of disaster
resilience into aspects of natural hazard arrangements, including preventing, preparing, responding to,
and recovering from, disasters. Further future enhancements and local applications of CRSI can provide
advancements in these disaster-related resilience activities.
The Federal Emergency Management Agency (FEMA) established the Strategic Foresight Initiative
(SFI; FEMA 2012) to address this need. This initiative has brought together a wide cross-section of the
emergency management community to explore key future issues, trends and other factors, and to work
through their implications. Working collaboratively and with urgency, we are beginning to understand
the full range of changes we could encounter and the nature of our future needs; and we can begin to
execute a shared agenda for action. One of the first tasks of this initiative group should be to bring
together the representative views of all governments, business, non-government sector and the
community into a comprehensive National Disaster Resilience Strategy. This group should also be
tasked with considering further those lessons arising from the recent bushfires, floods, tornadoes and
super-storms that could benefit from national collaboration.
Role of business
Businesses can and do play a fundamental role in supporting a community's resilience to disasters. They
provide resources, expertise and many essential services on which the community depends. Businesses,
including critical infrastructure providers, make a contribution by understanding the risks that they face
and ensuring that they are able to continue providing services during or soon after a disaster.
Role of individuals
Disaster resilience is based on individuals taking their share of responsibility for preventing, preparing
for, responding to and recovering from disasters. They can do this by drawing on guidance, resources
and policies of government and other sources such as community organizations. The disaster resilience
of people and households is significantly increased by active planning and preparation for protecting life
and property, based on an awareness of the threats relevant to their locality. It is also increased by
knowing and being involved in local community disaster or emergency management arrangements, and
for many being involved as a volunteer.
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Role of non-government organizations and volunteers
Non-government and community organizations are at the forefront of strengthening disaster resilience in
the United States. It is to them that Americans often turn for support or advice and the dedicated work of
these agencies and organizations is critical to helping counties and communities to cope with, and
recover from, a disaster. Building and fostering partnerships between U.S. national, state and local
governments and these agencies and organizations is essential to spreading the disaster resilience
message and to finding practical ways to strengthen disaster resilience in the counties and communities
they serve. Strengthening the U.S.'s disaster resilience is not a stand-alone activity that can be achieved
in a set timeframe, nor can it be achieved without a joint commitment and concerted effort by all sectors
of society. But it is an effort that is worth making, because building a more disaster resilient nation is an
investment in our future.
Potential Utility of CRSI
This report has outlined the approach and application of an index to examine the resilience of U.S.
counties, EPA Regions and the nations to extreme-weather events. Further research and application
efforts to adapt CRSI for use for individual counties and communities would clearly be useful for the
development of community-specific resilience plans. The potential of using CRSI-related information
by EPA regional staff tasked with assessing resilience in their areas of the counties seems particularly
useful. Allowing EPA regions to see in one application the specifics of risk, governance, societal
attributes, built environment information and natural environment information will be important in
further development local and county-level resilience plans. Similarly, at the county level, EPA can:
(1) Assess relative risks of differing weather-related events
(2) Disassemble CRSI to determine why the resilience of certain counties are projected to be low
and others are projected to be high
(3) Provide lessons learned from one county to the next on governance and other activities that have
increased local resilience to weather-related events
(4) Provide a comparative database permitting one way to assess where investments might have the
greatest return in terms of improved resilience
(5) Provide a database that can be updated to include the most recent information on the CRSI
metrics, indicators and domains so that improvements can be tracked.
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9. Appendices
Appendix A - CRSI Database Overview
EPA's Cumulative Resilience Screening Index (CRSI) characterizes community resilience based on a
suite of indicators that are grouped into broad categories or domains of community resiliency traits in
the context of natural hazard events. Data collected by the following institutions and organizations were
used to populate indicator metrics to quantify CRSI:
American Lung Association
Association of Religion Data Archives
Centers for Disease Control and Prevention
Instituto de Pesquisas Ecologicas Brasil
• National Telecommunication and Information Administration
• United States Census Bureau
• United States Department of Agriculture
• United States Department of Agriculture
• United States Department of Health and Human Services
• United States Department of Homeland Security
• United States Department of Housing and Urban Development
• United States Department of Justice
• United States Department of Labor
• United States Department of the Interior
• United States Department of Transportation
• United States Energy Information Administration
• United States Environmental Protection Agency
• University of Wisconsin Population Health Institute
To the extent possible, specific data sets and sources were selected for use in the development of CRSI
based on the following criteria:
• Data were publicly available and easy to obtain
• Data collection methods were credible and reliable
• Data sets were available at county-scale for population-based information and acres, meters,
hydrologic units or similar for geospatial
• Data collected was national in scope
• Data were available for all or a portion of 2000 - 2015 and were likely to collected in the future
Metrics serve as the foundation of CRSI. The following pages contain indicator heading and details
about corresponding metrics including basic information such as the data source(s) and years available,
as well as calculations performed to create the final datasets. We examined the distribution for all
metrics for pooled data (2000-2015). The distribution graphics are provided at the end of each
indicatormetric section. The y-axis scale shown in each graph reflects the true unit scale of results.
155
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Domain: Risk
Indicator: Exposure
The exposure indicator likelihood of hazard occurrence across a full spectrum of
geologic and atmospheric events as well as additional technological hazards that
may co-occur
Metric List for Domain: Risk- Indicator: Exposure
Metric Variable: SprFndExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 5-mile radius
of any listed Superfund Site. Generated using ArcMap 10.4, NLCD 2011, and superfund site
locations (U.S. EPA).
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: NukeExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 10-mile
radius of any nuclear power, weapons, research, or storage facility. Generated using ArcMap
10.4, NLCD 2011, and nuclear site locations (multi source) Data Source: Data Source: U.S.
Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: TRIExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 1/4 mile
radius of a TRI listed facility. Generated using ArcMap 10.4, NLCD 2011, and TRI site locations.
Data Source: Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: RCRAExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
156
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Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls with a Vi mile radius
of any RCRA site (LQGs, TSDs, and TRANSs). Generated using ArcMap 10.4, NLCD 2011 and
U.S. EPA FRS geodatabase.
Data Source: Data Source: U.S. Environmental Protection Agency
https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: BasicHurr
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Potential tornado exposure factor based on proximity to historic hurricane
hazard source. Generated using ArcMap 10.4 and historic hurricane data (NOAA). Data Source:
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: Basic Tndo
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Potential tornado exposure factor based on proximity to historic tornado hazard
source. Generated using ArcMap 10.4 and historic tornado data (NOAA). Data Source: U.S.
Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: Hurr_Exp Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by past hurricane
hazards. Generated using ArcMap 10.4, NLCD 2011 and historic hurricane data (NOAA). Data
Source: U.S. Environmental Protection Agency https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: TornExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by past tornado
hazards. Generated using ArcMap 10.4, NLCD 2011, and historic tornado data (NOAA). Data
Source: U.S. Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: InfloodExp
Source Measurement: Score
157
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Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land potentially impacted by inland
flooding hazards. Generated using ArcMap 10.4, NLCD 2011, and rivers and streams data (USGS).
Data Source: U.S. Environmental Protection Agency
https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: CFloodExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land potentially impacted by coastal
flooding hazards. Generated using ArcMap 10.4, NLCD 2011 coastal elevation data (EPA).
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: EQExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by earthquake
hazards at a peak ground acceleration (PGA) above the chosen threshold. Generated using ArcMap
10.4, NLCD 2011 and earthquake hazard mapping data (USGS). Data Source: U.S. Environmental
Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: FireExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by wildfire.
Generated using ArcMap 10.4, NLCD 2011 and historic wildfire data (USGS). Data Source: U.S.
Environmental Protection Agency https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: Drght Exp Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by drought.
Generated using ArcMap 10.4, NLCD 2011 and historic drought data (USGS). Data Source: U.S.
Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
158
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Metric Variable: WindExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Average number of annual wind events with gusts > 45 mph. Data Source: U.S.
Environmental Protection Agency https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: HailExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Average number of annual hail storms. Data Source: U.S. Environmental
Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: LndSldExp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land at moderate risk of exposure to
landslide activity. Generated using ArcMap 10.4, NLCD 2011 and landslide hazaed data (USGS).
Data Source: U.S. Environmental Protection Agency
https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: ExHTempExp
Source Measurement: Average deviation of annual maximum values from the 32-year average high
temps.
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Proportion of land exposed to extreme high temperatures. Three time periods are
derived from a suite of measures from 2000-2011. Data Source: U.S. Environmental Protection
Agency https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: ExLTemp Exp
Source Measurement: Average deviation of annual minimum values from the 32-year average high
temps.
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: The low temperature extreme values calculated for each U.S. County. Three
time periods derived from a suite of measures from 2000-2011. Data Source: U.S. Environmental
Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
159
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Exposure Indicator Metrics in Risk Domain
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EXPOSURE Hurricane Tornado Flooding Exposure Exposure Storm Temperature Wind Exposure Flooding
Exposure Exposure Exposure Exposure Exposure Exposure Exposure
Factor Factor
Indicator and Related Metrics
160
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Exposure (continued) Indicator Metrics in Risk Domain
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Nuclear
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RCRA
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Superfund
Hazard
Exposure
Tornado
Exposure
Toxic
Release
Exposure
Wildfire
Exposure
Indicator and Related Metrics
161
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Indicator: Loss
The loss indicator addresses an aspect of a place's vulnerability represented through
historical loss of life and property (including crops) associated with specific
Metric List for Domain: Risk- Indicator: Loss
Metric Variable: Natloss
Source Measurement: Score
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the loss of natural land to impervious surfaces.
Calculated using ArcMap 10.4, NLCD 2011, 2006 to 2011 Percent Developed Imperviousness
Change (NLCD).
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: Dualoss
Source Measurement: Score
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the loss of natural land to impervious surfaces and
crop land. Calculated using ArcMap 10.4, NLCD 2011, 2006 to 2011 Percent Developed
Imperviousness Change (NLCD) and changes in land type such as croplands and managed areas. Data
Source: U.S. Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
Metric Variable: Devloss
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the loss of human life and property as result of
adverse natural hazards. Summary of losses derived from Spatial Hazard Events and Losses
Database (SHELDUS) available at http://hvri.geog.sc.edu/ SHELDUS) Data Source: U.S.
Environmental Protection Agency https://edg.epa.gov/metadata/catalog/main/home.page
162
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Loss Indicator Metrics in Risk Domain
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Loss
of
Dual
Benefit
Land
Loss
of
Life
and
Property
Loss
of
Natural
Land
Indicator and Related Metrics
Domain: Governance
Indicator: Community Preparedness
The community preparedness indicator addresses community resilience
strengthening and structure hazard mitigation
Metric List for Domain: Risk- Community Preparedness
Metric Variable: CRS
Source Measurement: Community Rating System class designation for floodplain management Years
Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Federal Emergency Management Agency
https://www.fema.gov/medialibrary/assets/documents/27808
Metric Variable: PCX SHM
Source Measurement: Percent of Small Business Administration recovery funds spent on hazard
mitigation
163
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Years Available: 2015
Smallest Geospatial Level Available: County
C al cul ati on Method: N/A
Missing Data Handling: Zero fill
Data Source: Federal Emergency Management Agency https://www.fema.gov/data-feeds
Community Preparedness Indicator Metrics in Governance Domain
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COMMUNITY
PREPAREDNESS
Community
Rating
System
SBA
Recovery
Mitigation
Indicator and Related Metrics
Indicator: Personal Preparedness
ft
The personal preparedness indicator addresses individual or household activities
that help protect personal property from acute climate events.
Metric List for Domain Governance: Indicator: Personal Preparedness
Metric Variable: HOME INS
Source Measurement: Percent of homes with mortgages (which assumes insurance coverage).
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
164
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Metric Variable: NUMNFIP
Source Measurement: Number of National Flood Insurance Program community participants
Years Available: 2015
Smallest Geospatial Level Available: County
C al cul ati on Method: N/A
Missing Data Handling: Null fill
Data Source: Federal Emergency Management Agency https://www.fema.gov/data-feeds
Personal Preparedness Indicator Metrics in Governance Domain
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PREPAREDNESS
Home
Insurance
NFIP
Participants
Indicator and Related Metrics
165
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Indicator: Natural
Resource Conservation
The natural resource conservation indicator addresses the protection of natural
resources from anthropogenic activities which usually aids an ecosystem's ability to
recover from acute natural hazard events.
Metric List for Domain: Governance - Indicator: Natural Resource Conservation
Metric Variable: DIVCONS
Source Measurement: Land Protection Priority Index for preserving biodiversity*
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Instituto de Pesquisas Ecologicas Brasil http://www.ipe.org.br/ *
Index is an inverse ordinal scale where a zero or near-zero index is best.
Natural Resource Conservation Indicator Metrics in Governance Domain
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RESOURCE
CONSERVATION
Biodiversity
Land
Protection
Indicator and Related Metrics
166
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Domain: Society
Indicator: Demographics
The demographics indicator reflects attributes of a community's population and includes
aspects of employment potential and vulnerable populations
Metric List for Domain: Governance - Indicator: Demographics
Metric Variable: ALONE65
Source Measurement: Percent of population age 65 or greater and living alone
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of two variables—male and female
individuals over the age of 65 and living alone.
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: GRD925
Source Measurement: Percent of population age 25 years and over with less than 9th grade education
attainment
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: LINGISO
Source Measurement: Percent of population exhibiting limited English language skills
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: NODIPL25
Source Measurement: Percent of population age 25 years and over who attended high school but did
not receive a diploma
Years Available: 2006-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
167
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Metric Variable: POP5U
Source Measurement: Percent of population under 5 years of age
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-surveys/acs/
168
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Demographics Indicator Metrics in Society Domain
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0.00
DEMOGRAPHICS
Alone
and
65
Unquistic
Isolation
No
Diploma
No
High
School
Under
5
Indicator and Related Metrics
Indicator: Economic Diversity
The economic diversity indicator represents factors associated with
economic stability and recoverability within communities
Metric List for Domain: Governance - Indicator: Economic Diversity
Metric Variable: GINI
Source Measurement: Income inequality based on Gini Index
Years Available: 2006-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
169
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Metric Variable: HACHI
Source Measurement: Index of economic diversity based on Hachmann calculation method
Years Available: 2005, 2010, 2014
Smallest Geospatial Level Available: County
Calculation Method: For each county, the index is calculated as the reciprocal of the sum of location
quotients, which measures industry dependencies, weighted by the distribution of businesses as
classified by the North American Industry Classification System (NAICS).
Missing Data Handling: Null fill
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
Economic Diversity Indicator Metrics in Society Domain
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ECONOMIC
DIVERSITY
Gini
Index
Hachmann
Index
Indicator and Related Metrics
Indicator: Health Characteristics
The health characteristics indicator addresses factors associated with healthcare
access, special health vulnerability populations, and specific health problems
related to or exacerbated by acute natural hazard events.
Metric List for Domain: Society - Indicator: Health Characteristics
Metric Variable: A ST 11.VI A_ A
Source Measurement: Percent of adult population living with asthma
170
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Years Available: 2012, 2014, 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the average of adult individuals with asthma over the
total adult population counts for 2012, 2014, and 2015.
Missing Data Handling: Null fill
Data Source: American Lung Association, http://www.lung.org/ourinitiatives/research/rnonitoring-
trends-in-lung-disease/
Metric Variable: ASTHMA C
Source Measurement: Percent of population under 18 years of age living with asthma
Years Available: 2012, 2014, 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the average of individuals under 18 with asthma over
the pediatric population counts for 2012, 2014, and 2015.
Missing Data Handling: Null fill
Data Source: American Lung Association http://www.lung.org/ourinitiatives/research/monitoring-
trends-in-lung-disease/
Metric Variable: CNCR
Source Measurement: Incidence of cancer per 100,000 population Years Available: 2009-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: National Cancer Institute https://www.cancer.gov/research/resources/data-catalog
Metric Variable: DBTS
Source Measurement: Percent of population living with diabetes
Years Available: 2004-2016
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: University of Wisconsin Population Health Institute
http://www.coimtyhealthrankings.org/rankings/data
Metric Variable: HLTHINS
Source Measurement: Percent of population with at least some health insurance coverage
Years Available: 2013-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.gov/programs-siirveys/acs/
Metric Variable: HRTDS
Source Measurement: Incidence of heart disease per 1,000 population
Years Available: 2007-2013
171
-------
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: United States Department of Health and Human Services,
https://www.hhs.eov/aboiit/aeencies/omha/about/health-data-sets/index.html
Metric Variable: OBES
Source Measurement: Percent of population diagnosed with obesity
Years Available: 2004-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: University of Wisconsin Population Health Institute,
http://www.coiintyhealthrankines.ore/rankines/data
Metric Variable: SPND
Source Measurement: Percent of population with cognitive and/or physical special needs
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: STRK
Source Measurement: Incidence of stroke per 1,000 medicare population
Years Available: 2007-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: United States Department of Health and Human Services,
https://www.hhs.eov/aboiit/aeencies/omha/about/health-data-sets/index.html
172
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Health Characteristics Indicator Metrics in Society Domain
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Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: HWYCON
Source Measurement: Number of highway construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
American Community Survey https://www.census.eov/proerams-survevs/acs/
Metric Variable: MASON
Source Measurement: Number of masonry services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: PWRCON
Source Measurement: Number of power construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: ROOF
Source Measurement: Number of roofing construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: STEEL
Source Measurement: Number of steel construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
174
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Metric Variable: WTRSWCON
Source Measurement: Number of water and sewer construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Labor and Trade Services Indicator Metrics in Society Domain
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Concrete
LABOR Construction
AND
TRADE
SERVICES
Framing
Highway
Construction
Masonry
Power
Construction
Roofing
Steel Water
Construction Construction
Indicator and Related Metrics
Indicator: Safety and Security
+
i
The safety and security indicator addresses the provisioning of emergency and
civil services
Metric List for Domain: Society - Indicator: Safety and Security
Metric Variable: AMBULNCE
Source Measurement: Number of emergency and civil services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
175
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Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: LAWENFOR
Source Measurement: Number of law enforcement officers per 100,000 population Years Available:
2004-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Federal Bureau of Investigation https://ucr.fbi. gov/
Metric Variable: POLPROT
Source Measurement: Number of criminal and civil services per 100,000 population
Years Available: 2000-2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the aggregated sum of all State, Local, and Federal
government employees employed in the Police Protection field.
Missing Data Handling: Zero fill
Data Source: Bureau of Labor Statistics https://www.bls. gov/data/
Metric Variable: PUBSAFE
Source Measurement: Number of other public safety services per 100,000
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the aggregated sum of all State, Local, and Federal
government employees employed in the Police Protection field.
Missing Data Handling: Zero fill
Data Source: Bureau of Labor Statistics https://www.bls.eov/data/
176
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Safety and Security Indicator Metrics in Society Domain
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Criminal Emergency Law Other
SAFETY and Services Enforcement Public
AND Civil Officers Safety
SECURITY Enforcement
Services
Indicator and Related Metrics
Indicator; Social Cohesion
The social cohesion indicator represents the willingness of members of a
society to cooperate with each other in order to survive and prosper.
Metric List for Domain: Society - Indicator: Social Cohesion
Metric Variable: ETHNISO
Source Measurement: Degree of ethnic isolation based on calculated index
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: NATRES
Source Measurement: Percent of population born in current state of residence
Years Available: 2005-2014
Smallest Geospatial Level Available: County
177
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-------
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: TOTRATE
Source Measurement: Religious congregation participation per 1,000 population
Years Available: 2000, 2010
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Association of Religion Data Archives http://www.thearda.com/Archive/browse.asp
Social Cohesion Indicator Metrics in Society Domain
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SOCIAL
COHESION
Ethnic
Isolation
Religious
Participation
State
Native
Indicator and Related Metrics
Indicator: Social Services
The social services indicator represents a range of critical services provided by
government, private, and non-profit organizations
£ ^ Metric List for Domain: Society - Indicator: Social Services
178
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Metric Variable: AMBSURG
Source Measurement: Number of outpatient and emergency facilities per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: BDORGBNK
Source Measurement: Number of blood and organ banks per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: CHLDCARE
Source Measurement: Number of child care services per 100,000 population under 14
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: EMSOCSRV
Source Measurement: Number of emergency shelter, food and goods services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: HOSP
Source Measurement: Number of hospitals per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-surveys/acs/
Metric Variable: HPSAM
Source Measurement: Percent of population with sufficient access to mental healthcare providers
based on Healthcare Provider Service Area rating for mental health
Years Available: 2009
179
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Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa.eov/
Metric Variable: HPSAP
Source Measurement: Percent of population with sufficient access to primary healthcare
providers based on Healthcare Provider Service Area rating for primary care
Years Available: 2009
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa.gov/ Metric
Variable: INSADJ
Source Measurement: Number of insurance claims establishments per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: MHTHSERV
Source Measurement: Number of mental healthcare facilities per 100,000 population
Years Available: 2005-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: MUAP
Source Measurement: Score calculated based on the ability of population to access healthcare based
on average medically underserved area per population
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa.eov/
Metric Variable: RELIGORG
Source Measurement: Number of religions organizations per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
180
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Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: SCHOOLS
Source Measurement: Number of K-12 education and support facilities per 100,000 population ages 5
to 18
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
Metric Variable: SNFAC
Source Measurement: Number of rehabilitative service facilities per 100,000 population
Years Available: 2012-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: SOCADV
Source Measurement: Number of social advocacy services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.eov/proerams-siirveys/acs/
Metric Variable: SPNDTRAN
Source Measurement: Number of special needs transportation services per 100,000 population with
special needs
Years Available: 2005-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-surveys/acs/
181
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Social Services Indicator Metrics in Society Domain
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Adequate
SOCIAL Mental
SERVICES Healthcare
Profs
Adequate Average
Primary Medically
Healthcare Underserved
Profs Score
Blood
And
Organ
Banks
Hospitals
Per
County
Mental Outpatient Rehabilitative
Health and Services
Services Emergency
Services
Indicator and Related Metrics
182
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Social Services (continued) Indicator Metrics in Society Domain
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Child
Care
Emergency
Shelter
And
Goods
Insurance
Claims
K-12
Education
And
Support
Religions
Organizations
Social
Advocacy
Special
Needs
Transportation
Indicator and Related Metrics
Indicator: Socio-Economics
The socio-economic indicator relates to employment opportunity and issues associated
with personal economics, primarily level of income.
Metric List for Domain: Society - Indicator: Socio-Economics
Metric Variable: DEEPPOV
Source Measurement: Percent of population living at or below 150 percent of poverty threshold
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: MEDINC
Source Measurement: Median household income in inflation adjusted dollars
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
183
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-------
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: UNEMPLOY
Source Measurement: Unemployment rate of population ages 16 years and greater
Years Available: 2006-2015
Smallest Geospatial Level Available: County
C al cul ati on Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Socio-Economics Indicator Metrics in Society Domain
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SOCIO-ECONOMICS
Deep
Poverty
Median
Income
Unemployment
Indicator and Related Metrics
Domain: Built Environment
Indicator: Communications Infrastructure
The communications infrastructure represents a measure of communication continuity to
support the ability of a community to perform essential functions before, during and after
a natural hazard event
Metric List for Domain: Society - Indicator: Communication Infrastructure
184
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Metric Variable: CELLTOWER
Source Measurement: Number of cell service towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: INETACC
Source Measurement: Percent of homes with access to internet service provider(s)
Years Available: 2011, 2012, 2013, 2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: National Broadband Map Datasets https://www.broadbandmap.eov/analyze
Metric Variable: LMBROAD
Source Measurement: Number of land mobile broadcast towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: MICROTOWR
Source Measurement: Number of microwave service towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: PAGETOWR
Source Measurement: Number of paging transmission towers
Years Available: 2015
Smallest Geospatial Level Available: County
185
-------
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: RADTOWR
Source Measurement: Number of AM and FM radio broadcast transmission towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: TVTRANS
Source Measurement: Number of TV station transmitters
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level https ://hifld~dh seii. open data, arcei s. com/
186
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Communication Infrastructure Indicator Metrics in Built Environment Domain
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Cell
COMMUNICATION Phone
INFRASTRUCTURE Towers
Internet
Access
Land
Mobile
Towers
Microwave
Towers
Paging
Towers
Radio
Broadcast
Towers
TV
Stations
Indicator and Related Metrics
187
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Indicator: Housing Characteristics
/\
Housing characteristics relate to the potential resilience weaknesses that the distribution
or condition of dwellings introduce to a community in context of adverse natural hazards
Metric List for Domain: Society - Indicator: Housing Characteristics
Metric Variable: HOMEAGE
Source Measurement: Median age of residential housing
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https://www.census.gov/programs-survevs/acs/
Metric Variable: HOMECRWD
Source Measurement: Median age of residential housing
Years Available: 2009, 2013
Smallest Geospatial Level Available: County
Calculation Method: Data from the original dataset were calculated based on the sum of renter and
owner occupancy levels.
Missing Data Handling: Zero fill
Data Source: Comprehensive Housing Affordability Strategy
https://www.huduser.gov/portal/datasets/cp/CHAS/data querytool chas.html
Metric Variable: HOMEPROB
Source Measurement: Percent of homes with inadequate plumbing and kitchen facilities
Years Available: 2009, 2013
Smallest Geospatial Level Available: County
Calculation Method: Metric is the of sum of renter and owner occupant measures that reflect the
same condition.
Missing Data Handling: Zero fill
Data Source: Comprehensive Housing Affordability Strategy
https://www.huduser.gov/portal/datasets/cp/CHAS/data querytool chas.html
Metric Variable: HUDENSE
Source Measurement: Number of homes per square mile
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Calculated as total number of housing units/total square miles (within a county)
Missing Data Handling: Null fill
Data Source: U.S. Environmental Protection Agency
https://edg.epa.gov/metadata/catalog/main/home.page
188
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Metric Variable: MOBLHOME
Source Measurement: Percent of non-permanent or mobile residential structures (excluding vans,
campers, etc.)
Years Available: 2007-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.censiis.eov/proerams-siirveys/acs/
189
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Housing Characteristics Indicator Metrics in Built Environment Domain
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Metric Variable: HELIPORT
Source Measurement: Air Transportation Facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: SEAPLANE
Source Measurement: Air Transportation Facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
Metric Variable: ARTROAD
Source Measurement: Total miles of urban and rural arterial roads
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county.
Missing Data Handling: Zero fill
Data Source: National Bridge Inventory https://www.fhwa.dot.eov/bridee/nbi/ascii.cfm
Metric Variable: BRIDGES
Source Measurement: Number of roadway bridge structures
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county. Missing Data Handling: Zero fill
Data Source: National Bridge Inventory https://www.fhwa.dot.eov/bridee/nbi/ascii.cfm
Metric Variable: BRIDRATE
Source Measurement: Roadway bridge structural and functional assessment rating Years Available:
2015
Smallest Geospatial Level Available: County
191
-------
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude and
longitude provided in original dataset. Counts were then calculated as the sum of the number of data
records associated with each county.
Missing Data Handling: Null fill
Data Source: National Bridge Inventory, United States Department of Transportation:
https://www.fhwa.dot.gov/bridge/nbi/ascii.cfm
Metric Variable: HWYACC
Source Measurement: Percent population residing within 10-minute drive of highway entrance/exit.
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: Measures derived using ArcMap 10.4, U.S. Census population estimates and
ESRI interstate access points data layer.
Missing Data Handling: Zero fill
Data Source: U.S. Environmental Protection Agency
https://ede.epa.eov/metadata/cataloe/main/home.paee
Metric Variable: RAIL
Source Measurement: Miles of operating freight rails
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the miles of operating rail line reported by
major rail operators within a county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-
dhseii.opendata.arceis.com/
192
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Transportation Infrastructure Indicator Metrics in Built Environment Domain
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TRANSPORTATION
INFRASTRUCTURE
Access
To
Highway
Entrance/Exit
Air
Transportation
Facilities
Arterial
Roads
Freight
Railroad
Roadway
Bridge
Assessment
Indicator and Related Metrics
i
i
Roadway
Bridge
Structures
Indicator; Utilities Infrastructure
"~T" Utilities infrastructure refers to a measure of potential continuity for communities to promote
alaj access to critical services in context of an adverse natural hazard exposure
k
™ Metric List for Domain: Built Environment-Indicator: Utilities Infrastructure
Metric Variable: COMWATR
Source Measurement: Number of public drinking water supply facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Safe Drinking Water Information System https://www.epa.gov/ground-water-and-
drinking-water/safe-drinking-water-information-svstem-sdwis-federal-reporting
Metric Variable: POWRPLNT
Source Measurement: Number of power generating facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
193
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Data Source: United States Energy Information Administration https://www.eia.gov/
Metric Variable: WWTPLNT
Source Measurement: Number of wastewater treatment facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Enforcement and Compliance History Online https://echo.epa.gov
Utility Infrastructure Indicator Metrics in Built Environment Domain
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UTILITY
INFRASTRUCTURE
Drinking
Water
Supply
Power
Generator
Wastewater
Treatment
Facilities
Indicator and Related Metrics
Indicator: Vacant Structures
The vacant structures indicator includes the number of vacant business structures,
residential and public-access buildings in the county (e.g., hospitals, schools,
government buildings).
Metric List for Domain: Built Environment - Indicator: Vacant Structures.
Metric Variable: BUS VAC
Source Measurement: Percent of vacant business structures
194
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Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated with
each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https://www.hiiduser.eov/portal/datasets/iisps.html
Metric Variable: OTH VAC
Source Measurement: Percent of vacant structures that are not identified as business or residential
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https://www.hiidiiser.eov/port.al/datasets/usps.html
Metric Variable: RES VAC
Source Measurement: Percent of vacant residential structures
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https://www.hiidiiser.eov/port.al/datasets/usps.html
195
-------
Vacant Structures Indicator Metrics in Built Environment Domain
1.00-
0.75-
0.25 ¦
0.00-
VACANT
STRUCTURES
Vacant
Business
Structure
Vacant
Other
Building
Vacant
Residence
Indicator and Related Metrics
Domain: Natural Environment
Indicator: Extent of Ecosystem Types
The extent domain includes the spatial extent or acreage of each
ecosystem type that occurs naturally without any significant human
intervention
Metric List for Domain: Built Environment - Indicator: Extent of
Ecosystem Types
Metric Variable: AGLAND
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent agriculture area calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 81 (Pasture/Hay) and 82 (Cultivated Crops)
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011 php
196
-------
Metric Variable: CSTLWATR
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent Marine/Estuarine area calculated using county census tracts (U.S.
Census Bureau), ArcMap 10.4, and 2011 NLCD category 11 (Open Water)
Data Source: Environmental protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee
Raw Data: https://www.mrlc.gov/nlcd2011 .php
Metric Variable: FOREST
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent forested area calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 41 (Deciduous Forest), 42 (Evergreen
Forest), and 43 (Mixed Forest)
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page Raw Data:
https://www.mrlc.gov/nlcd2011 .php
Metric Variable: FRSHWATR
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of inland lakes/rivers/streams calculated using county boundaries
(U.S. Census Bureau), ArcMap 10.4, and 2011 NLCD category 11 (Open Water)
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011 .php
Metric Variable: GRASSLANDS
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of grasslands calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD category 71 (Grassland/Herbaceous)
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011 .php
197
-------
Metric Variable: ICELAND
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of ice/snow calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD category 12 (Perennial Ice/Snow)
Data Source: Environmental protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee
Raw Data: https://www.mrlc.gov/nlcd2011 .php
Metric Variable: PROTAREA
Source Measurement: Percent
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Calculated percent of area classified as conservation lands and preservations
including marine protected areas, state recreational areas and urban greenspace using county
boundaries (U.S. Census Bureau), ArcMap 10.4, and the Protected Areas Database of the United
States (USGS)
Data Source: Environmental protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee Raw Data:
https://www.mrlc.gov/nlcd2011 .php
Metric Variable: TUNDRA
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of tundra calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 72 (Sedge/Herbaceous), 73 (Lichens), and
74(Moss). Alaska only
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011 .php
Metric Variable: WETLANDS
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of wetlands calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 90 (Woody Wetlands) and 95 (Emergent
Herbaceous Wetlands)
198
-------
Data Source: Environmental protection Agency
Derived Data : https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data : https://www.mrlc.gov/nlcd2011.php
199
-------
Extent of Ecosystem Types Indicator Metrics in Natural Environment Domain
V)
O
£
o
u
CO
1.00
0.75-
0.50-
0.25-
o.oo-
EXTENT
OF
ECOSYSTEM
TYPES
Agriculture
Area
Forested
Area
Grassland
Area
Inland Marine/Estuarine Perennial
Surface Area Ice/Snow
Water Area
Area
Protected
Area
Tundra
Area
Wetland
Area
Indicator and Related Metrics
Indicator: Condition
A!.
The condition indicator is related to metrics that describe the condition of various
natural and managed ecosystems
Metric List for Domain: Built Environment- Indicator: Condition
Metric Variable: BIODIV
Source Measurement: Biodiversity based on avian taxa richness
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Jenkins, C.N., Van Houtan, K.S., Pimm, S.L., Sexton, J O. 2015. U.S. protected lands
mismatch biodiversity priorities. Proceedings of the National Academy of Sciences. 112(16): 5081 -
5086. http://biodiversitvmapping.org/wordpress/index.php/home/
Metric Variable: CLEANAIR
Source Measurement: Percent
200
-------
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Sum of days AQI rated as Good and Moderate, divided by Total number of days
with AQI data
Data Source: U.S. Environmental protection Agency
https://www.epa.eov/oiitdoor-air-qiialitY-data/air-qiiality-index-report
Metric Variable: CSTLCOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NA
Calculation Method: Great Lakes and near-coastal condition assessment score based on NARS costal
data. Overall condition scores were calculated for each geo-referenced location as follows based on
used for the national assessment (U.S. Environmental Protection Agency. Office of
Water and Office of Research and Development. 2015. National Coastal Condition Assessment 2010
(EPA 841-R-15-006). Washington, DC. December 2015). Final scores averaged by summation of all
sample points falling within county boundaries using census tracts (U.S.
Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page Raw Data:
https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: LAKECOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Interpolation
Calculation Method: Inland lakes condition assessment score based on NARS Lake data. Overall
condition scores were calculated for each geo-referenced location as follows based on used for the
national assessment (U.S. Environmental Protection Agency. 2009. National Lakes Assessment: A
Collaborative Survey of the Nation's Lakes. EPA 841-R-09-001. U.S.
Environmental Protection Agency, Office of Water and Office of Research and Development,
Washington, D.C.). These values were standardized on a 0 - 1 scale, summed and re-graded based on
actual score to highest possible score ratio. Final scores created by distance weighted average of all
sample points falling within a 70-mile radius from county centroids using county boundaries (U.S.
Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: RIVCOND
Source Measurement: Score
201
-------
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Interpolation
Calculation Method: Rivers and streams condition assessment score based on NARS Rivers and
Streams data. Overall condition scores were calculated for each geo-referenced location as follows
based on used for the national assessment (U.S. Environmental Protection Agency. Office of Water
and Office of Research and Development. National Rivers and Streams
Assessment 2008-2009: A Collaborative Survey. EPA/841/R-16/007. Washington, DC. March 2016).
These values were standardized on a 0 - 1 scale, summed and re-graded based on actual score to
highest possible score ratio. Final scores created by distance weighted average of all sample points
falling within a 50-mile radius from county centroids using county boundaries
(U.S. Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: WLNDCOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Interpolation
Calculation Method: Wetlands condition assessment score based on NARS wetlands data. Overall
condition scores were calculated for each geo-referenced location as follows based on used for the
national assessment (U.S. Environmental Protection Agency. 2016. National Wetland Condition
Assessment: Technical Report. EPA 843-R-15-006. U.S. EPA, Washington, DC). These values were
standardized on a 0 - 1 scale, summed and re-graded based on actual score to highest possible score
ratio. Final scores created by distance weighted average of all sample points falling within a 100-mile
radius from county centroids using county boundaries
(U.S. Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: FORCOND Source Measurement: Score
Years Available: 2000 - 2015
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Forest condition assessment score is a synthesized value created from four
Forest Inventory and Analysis Database (FIAB). These are: stand age, basal area of live trees, and
disturbance observations (last observation). These three metrics were consistently measures across all
years of the assessment and more nationally complete. Disturbance codes were recoded into 3 sub-
index values where no disturbance was graded best; pest, disease and anthropogenic disturbance
graded most disturb; and remaining disturbance observations (e.g., wildfire, wildlife damage) was
considered moderate disturbance. All values were standardized on a 0 - 1 scale, summed and re-
graded based on actual score to highest possible score ratio.
202
-------
Source: U.S. Environmental Protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.fs.fed.us/
Metric Variable: SOILCLASS
Source Measurement: Percent of soil classified as suitable for farming
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: The USDA cropland GIS layer and the classification field from the NCCPI
dataset were used to calculate land-area weighted estimates. Census tract results were generated using
ArcMAP 10.4. Results were summed to create a final county-level measure.
Source: U.S. Environmental Protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee
Raw Data: https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/
Metric Variable: SOILPROD
Source Measurement: Average Soil Productivity Index Score
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: The USDA cropland GIS layer and the productivity index field from the NCCPI
dataset were used to calculate land-area weighted estimates. Census tract results were generated using
ArcMAP 10.4. Results were averaged to create a final county-level measure.
Source: U.S. Environmental Protection Agency
Derived Data: https://ede.epa.eov/metadata/cataloe/main/home.paee
Raw Data: https://www.nrcs.usda.eov/wps/portal/nrcs/main/soils/survev/
203
-------
Condition Indicator Metrics in Natural Environment Domain
i.oo-
0,75-
3
£
'w
w 0.50
a
a
i—
o
o
CO
0.25
0.00
I . I
Biodiversity Coastal Forest
CONDITION Condition Condition
Good
or
Moderate
AQI
Inland
Lake
Condition
Rivers
and
Streams
Condition
Soil Soil
Productivity Suitability
Wetlands
Condition
Indicator arid Related Metrics
204
-------
7.2 Appendix B - CRSI and Domain Scores Arranged by EPA region, State and County.
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
National Average
4.321
0.597
0.229
0.393
0.516
0.413
Region 1
7.530
0.660
0.240
0.492
0.599
0.445
Connecticut
2.661
0.654
0.395
0.520
0.547
0.398
Fairfield
1.981
0.621
0.508
0.675
0.494
0.346
Hartford
1.568
0.671
0.650
0.646
0.525
0.311
Litchfield
5.120
0.694
0.224
0.489
0.657
0.431
Middlesex
1.979
0.635
0.420
0.396
0.598
0.439
New Haven
2.145
0.609
0.491
0.659
0.499
0.403
New London
3.612
0.619
0.273
0.587
0.490
0.433
Tolland
2.314
0.721
0.306
0.341
0.551
0.403
Windham
2.568
0.663
0.289
0.370
0.561
0.420
Maine
12.708
0.677
0.115
0.499
0.565
0.484
Androscoggin
4.158
0.635
0.174
0.424
0.565
0.365
Aroostook
14.019
0.687
0.101
0.744
0.546
0.413
Cumberland
5.505
0.718
0.298
0.671
0.615
0.525
Franklin
9.593
0.678
0.094
0.490
0.502
0.421
Hancock
35.399
0.670
0.038
0.543
0.559
0.603
Kennebec
6.912
0.653
0.145
0.533
0.581
0.395
Knox
11.639
0.531
0.076
0.344
0.621
0.617
Lincoln
13.316
0.772
0.080
0.309
0.613
0.548
Oxford
7.163
0.644
0.116
0.505
0.505
0.388
Penobscot
13.514
0.652
0.104
0.786
0.565
0.390
Piscataquis
10.370
0.673
0.075
0.342
0.524
0.491
Sagadahoc
7.603
0.722
0.126
0.304
0.615
0.534
Somerset
15.131
0.759
0.081
0.542
0.523
0.463
Waldo
28.446
0.684
0.032
0.410
0.538
0.486
Washington
14.359
0.672
0.092
0.483
0.628
0.588
York
6.200
0.685
0.201
0.553
0.547
0.516
Massachusetts
5.041
0.602
0.361
0.557
0.601
0.447
Barnstable
6.836
0.627
0.197
0.585
0.580
0.591
Berkshire
5.571
0.633
0.202
0.552
0.671
0.402
Bristol
2.111
0.643
0.523
0.572
0.552
0.451
Dukes
21.827
0.538
0.045
0.289
0.811
0.595
Essex
2.504
0.642
0.537
205
0.671
0.565
0.487
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Franklin
8.874
0.701
0.147
0.542
0.707
0.419
Hampden
1.666
0.606
0.576
0.649
0.517
0.340
Hampshire
3.205
0.523
0.197
0.429
0.563
0.389
Middlesex
2.200
0.660
0.591
0.819
0.585
0.259
Nantucket
6.526
0.323
0.060
0.220
0.572
0.609
Norfolk
2.315
0.621
0.497
0.633
0.618
0.387
Plymouth
2.847
0.624
0.469
0.614
0.598
0.542
Suffolk
1.495
0.648
0.465
0.417
0.465
0.426
Worcester
2.596
0.646
0.550
0.804
0.604
0.359
New Hampshire
6.561
0.670
0.229
0.519
0.596
0.421
Belknap
4.773
0.656
0.151
0.350
0.621
0.385
Carroll
5.716
0.668
0.169
0.441
0.599
0.445
Cheshire
7.642
0.620
0.108
0.457
0.574
0.400
Coos
12.682
0.699
0.112
0.536
0.640
0.549
Grafton
11.672
0.638
0.129
0.785
0.571
0.468
Hillsborough
2.559
0.722
0.461
0.631
0.574
0.334
Merrimack
10.688
0.718
0.149
0.667
0.728
0.410
Rockingham
2.066
0.691
0.555
0.578
0.554
0.420
Strafford
1.916
0.626
0.338
0.380
0.505
0.419
Sullivan
5.900
0.665
0.120
0.363
0.594
0.379
Rhode Island
2.479
0.627
0.372
0.302
0.586
0.511
Bristol
1.015
0.522
0.385
0.110
0.584
0.531
Kent
1.275
0.708
0.510
0.240
0.605
0.443
Newport
4.139
0.669
0.207
0.232
0.551
0.643
Providence
1.642
0.639
0.510
0.535
0.551
0.326
Washington
4.322
0.595
0.248
0.391
0.637
0.614
Vermont
9.382
0.708
0.135
0.450
0.671
0.417
Addison
17.298
0.825
0.089
0.473
0.712
0.490
Bennington
7.519
0.763
0.154
0.435
0.613
0.469
Caledonia
9.771
0.686
0.115
0.401
0.815
0.396
Chittenden
5.495
0.754
0.261
0.661
0.604
0.387
Essex
12.333
0.780
0.074
0.327
0.468
0.560
Franklin
3.660
0.649
0.242
0.427
0.651
0.386
Grand Isle
12.838
0.677
0.063
0.334
0.714
0.365
Lamoille
10.944
0.619
0.080
0.389
0.665
0.438
Orange
6.894
0.751
0.140
0.400
0.689
0.351
Orleans
7.130
0.732
0.185
0.436
0.793
0.439
Rutland
11.762
0.595
0.090
0.520
0.662
0.441
206
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Washington
9.925
0.681
0.110
0.469
0.720
0.381
Windham
10.089
0.753
0.104
0.467
0.638
0.364
Windsor
5.687
0.640
0.188
0.558
0.651
0.367
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 2
3.839
0.658
0.308
0.469
0.520
0.385
New Jersey
1.762
0.639
0.488
0.471
0.518
0.397
Atlantic
1.964
0.633
0.477
0.520
0.431
0.507
Bergen
0.928
0.610
0.582
0.464
0.586
0.202
Burlington
2.458
0.675
0.538
0.567
0.547
0.558
Camden
1.032
0.606
0.532
0.444
0.506
0.296
Cape May
2.180
0.609
0.382
0.401
0.462
0.565
Cumberland
2.657
0.598
0.313
0.437
0.444
0.549
Essex
0.637
0.618
0.519
0.467
0.531
0.100
Gloucester
1.137
0.571
0.553
0.453
0.489
0.379
Hudson
0.659
0.618
0.525
0.402
0.465
0.233
Hunterdon
2.868
0.644
0.386
0.512
0.598
0.480
Mercer
1.269
0.599
0.496
0.448
0.492
0.362
Middlesex
1.638
0.646
0.522
0.601
0.561
0.253
Monmouth
1.618
0.627
0.728
0.615
0.534
0.485
Morris
2.197
0.672
0.511
0.524
0.595
0.449
Ocean
1.517
0.675
0.806
0.596
0.448
0.546
Passaic
1.447
0.642
0.494
0.384
0.522
0.432
Salem
3.527
0.708
0.209
0.338
0.459
0.503
Somerset
1.457
0.679
0.585
0.486
0.573
0.339
Sussex
2.147
0.652
0.406
0.405
0.563
0.468
Union
0.878
0.652
0.337
0.388
0.561
0.129
Warren
2.795
0.679
0.339
0.441
0.513
0.493
New York
4.542
0.665
0.248
0.469
0.521
0.381
Albany
2.354
0.693
0.455
0.542
0.639
0.343
Allegany
4.919
0.657
0.118
0.482
0.385
0.343
Bronx
0.121
0.668
0.529
0.297
0.409
0.203
Broome
2.075
0.689
0.381
0.505
0.497
0.335
Cattaraugus
5.133
0.657
0.186
0.560
0.421
0.460
Cayuga
5.792
0.637
0.139
0.464
0.571
0.368
Chautauqua
5.625
0.655
0.152
0.567
0.414
0.399
Chemung
1.694
0.596
0.204
0.339
0.472
0.304
Chenango
3.796
0.699
0.160
207
0.393
0.474
0.362
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Clinton
7.373
0.699
0.154
0.587
0.534
0.412
Columbia
5.134
0.644
0.155
0.412
0.603
0.387
Cortland
3.287
0.633
0.146
0.331
0.527
0.341
Delaware
4.208
0.703
0.210
0.519
0.485
0.375
Dutchess
2.064
0.638
0.521
0.612
0.587
0.366
Erie
2.418
0.656
0.463
0.713
0.550
0.296
Essex
9.274
0.677
0.126
0.530
0.561
0.493
Franklin
8.313
0.721
0.138
0.440
0.531
0.560
Fulton
2.821
0.597
0.140
0.282
0.464
0.406
Genesee
3.131
0.617
0.205
0.377
0.544
0.393
Greene
3.807
0.659
0.188
0.465
0.495
0.356
Hamilton
16.706
0.723
0.062
0.354
0.556
0.565
Herkimer
7.873
0.643
0.112
0.503
0.422
0.490
Jefferson
8.873
0.710
0.147
0.672
0.485
0.446
Kings
0.889
0.664
0.366
0.310
0.502
0.276
Lewis
9.628
0.666
0.095
0.463
0.516
0.456
Livingston
8.946
0.717
0.116
0.480
0.539
0.449
Madison
4.735
0.585
0.144
0.420
0.538
0.403
Monroe
2.749
0.652
0.375
0.623
0.519
0.364
Montgomery
2.282
0.722
0.143
0.260
0.492
0.322
Nassau
2.935
0.598
0.351
0.564
0.703
0.342
New York
0.463
0.641
0.569
0.376
0.460
0.206
Niagara
2.781
0.680
0.279
0.432
0.481
0.425
Oneida
4.985
0.646
0.235
0.676
0.525
0.398
Onondaga
2.225
0.655
0.458
0.590
0.567
0.353
Ontario
6.110
0.641
0.159
0.528
0.555
0.413
Orange
2.310
0.623
0.520
0.704
0.591
0.362
Orleans
3.544
0.722
0.162
0.325
0.443
0.431
Oswego
4.458
0.684
0.166
0.565
0.389
0.330
Otsego
4.762
0.682
0.149
0.441
0.528
0.337
Putnam
2.320
0.657
0.411
0.403
0.633
0.463
Queens
1.885
0.546
0.336
0.467
0.578
0.318
Rensselaer
2.077
0.641
0.371
0.478
0.531
0.359
Richmond
1.146
0.562
0.508
0.314
0.579
0.431
Rockland
1.073
0.615
0.556
0.345
0.618
0.340
Saratoga
2.038
0.684
0.473
0.535
0.566
0.355
Schenectady
0.487
0.654
0.390
0.218
0.528
0.274
Schoharie
6.217
0.742
0.077
0.320
0.525
0.310
Schuyler
6.908
0.784
0.099
0.312
0.545
0.394
Seneca
4.455
0.670
0.134
0.328
0.492
0.427
St. Lawrence
10.729
0.635
0.117
0.691
0.453
0.508
Steuben
11.086
0.719
0.105
0.707
0.445
0.353
208
-------
Built
Environment
Natural
Area
CRSI
Governance
Risk
Society
Environment
Suffolk
4.063
0.606
0.383
0.738
0.683
0.512
Sullivan
4.577
0.611
0.183
0.553
0.512
0.361
Tioga
1.693
0.765
0.266
0.343
0.450
0.321
Tompkins
6.092
0.700
0.111
0.457
0.459
0.343
Ulster
5.558
0.648
0.219
0.636
0.542
0.455
Warren
6.898
0.675
0.139
0.435
0.599
0.439
Washington
4.339
0.684
0.147
0.408
0.484
0.365
Wayne
4.729
0.701
0.132
0.403
0.466
0.365
Westchester
1.431
0.618
0.591
0.544
0.589
0.310
Wyoming
5.565
0.724
0.122
0.406
0.504
0.353
Yates
5.649
0.729
0.110
0.322
0.542
0.376
Built
Environment
Natural
Area
CRSI
Governance
Risk
Society
Environment
Region 3
2.934
0.571
0.272
0.382
0.512
0.378
Delaware
Kent
New Castle
Sussex
2.443
2.816
1.647
2.867
0.606
0.638
0.647
0.534
0.474
0.434
0.609
0.380
0.586
0.566
0.546
0.646
0.472
0.463
0.520
0.434
0.547
0.609
0.437
0.596
District of Columbia 0.501
District of Columbia 0.501
0.610
0.610
0.676
0.676
0.402
0.402
0.506
0.506
0.200
0.200
aryland
3.367
Allegany
2.268
Anne Arundel
1.542
Baltimore
1.796
Baltimore city
0.185
Calvert
2.931
Caroline
5.656
Carroll
3.840
Cecil
1.785
Charles
2.643
Dorchester
4.615
Frederick
2.235
Garrett
6.420
Harford
2.158
Howard
1.295
Kent
4.053
0.622
0.604
0.642
0.571
0.611
0.664
0.626
0.707
0.567
0.674
0.588
0.662
0.585
0.626
0.628
0.522
0.366
0.277
0.675
0.494
0.594
0.317
0.155
0.296
0.432
0.484
0.172
0.529
0.140
0.465
0.565
0.190
0.494
0.421
0.539
0.567
0.393
0.440
0.389
0.501
0.522
0.562
0.373
0.632
0.583
0.513
0.399
0.428
0.518
0.441
0.553
0.585
0.381
0.541
0.494
0.620
0.398
0.565
0.447
0.603
0.497
0.531
0.627
0.492
0.463
0.429
0.446
0.363
0.156
0.472
0.568
0.429
0.483
0.524
0.601
0.373
0.409
0.485
0.355
0.555
209
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Montgomery
1.518
0.651
0.590
0.530
0.569
0.344
Prince George's
1.496
0.653
0.704
0.565
0.610
0.368
Queen Anne's
6.294
0.587
0.173
0.531
0.581
0.529
Somerset
7.335
0.676
0.124
0.407
0.390
0.609
St. Mary's
3.903
0.703
0.303
0.521
0.483
0.547
Talbot
4.838
0.594
0.197
0.422
0.570
0.555
Washington
1.960
0.627
0.470
0.549
0.497
0.419
Wicomico
3.234
0.583
0.281
0.498
0.506
0.505
Worcester
6.816
0.588
0.160
0.559
0.453
0.598
Pennsylvania
4.369
0.667
0.257
0.481
0.503
0.383
Adams
3.414
0.662
0.262
0.530
0.508
0.382
Allegheny
1.086
0.593
0.705
0.706
0.529
0.151
Armstrong
3.389
0.659
0.188
0.487
0.480
0.295
Beaver
1.460
0.629
0.360
0.430
0.497
0.290
Bedford
8.398
0.702
0.099
0.438
0.511
0.410
Berks
2.960
0.646
0.367
0.666
0.513
0.365
Blair
4.192
0.652
0.231
0.509
0.537
0.437
Bradford
6.884
0.768
0.129
0.512
0.473
0.349
Bucks
2.432
0.648
0.521
0.724
0.634
0.318
Butler
1.886
0.645
0.413
0.476
0.587
0.320
Cambria
6.761
0.677
0.165
0.580
0.587
0.388
Cameron
7.320
0.848
0.107
0.214
0.467
0.584
Carbon
4.551
0.677
0.164
0.401
0.515
0.416
Centre
4.872
0.588
0.201
0.612
0.497
0.430
Chester
2.222
0.637
0.480
0.601
0.544
0.409
Clarion
7.152
0.758
0.126
0.412
0.553
0.409
Clearfield
6.458
0.673
0.142
0.542
0.495
0.383
Clinton
8.577
0.749
0.132
0.478
0.491
0.518
Columbia
3.198
0.651
0.193
0.398
0.491
0.375
Crawford
4.921
0.695
0.173
0.461
0.462
0.443
Cumberland
1.718
0.619
0.508
0.533
0.549
0.370
Dauphin
1.583
0.603
0.534
0.524
0.558
0.370
Delaware
0.804
0.566
0.485
0.388
0.526
0.252
Elk
7.984
0.603
0.110
0.354
0.562
0.572
Erie
2.922
0.614
0.303
0.596
0.504
0.351
Fayette
3.097
0.607
0.226
0.532
0.445
0.352
Forest
8.283
0.914
0.097
0.263
0.369
0.590
Franklin
2.850
0.576
0.303
0.575
0.505
0.396
Fulton
5.826
0.688
0.090
0.357
0.427
0.394
Greene
2.410
0.619
0.144
0.409
0.386
0.290
210
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Huntingdon
10.034
0.683
0.102
0.478
0.525
0.486
Indiana
3.693
0.628
0.180
0.565
0.386
0.323
Jefferson
5.413
0.743
0.156
0.427
0.512
0.403
Juniata
4.518
0.612
0.144
0.365
0.525
0.431
Lackawanna
1.800
0.668
0.418
0.439
0.546
0.359
Lancaster
3.078
0.579
0.389
0.741
0.578
0.391
Lawrence
1.916
0.624
0.225
0.283
0.553
0.346
Lebanon
1.571
0.598
0.402
0.380
0.550
0.391
Lehigh
1.619
0.645
0.505
0.556
0.513
0.316
Luzerne
2.696
0.647
0.405
0.650
0.522
0.378
Lycoming
7.597
0.726
0.167
0.599
0.524
0.453
McKean
2.861
0.719
0.213
0.327
0.439
0.453
Mercer
3.640
0.739
0.238
0.448
0.521
0.389
Mifflin
4.004
0.624
0.161
0.332
0.512
0.466
Monroe
2.512
0.688
0.307
0.492
0.422
0.398
Montgomery
1.759
0.651
0.521
0.631
0.604
0.220
Montour
2.745
0.628
0.157
0.324
0.481
0.358
Northampton
1.531
0.650
0.500
0.522
0.509
0.320
Northumberland
3.055
0.640
0.189
0.418
0.475
0.348
Perry
8.674
0.632
0.096
0.420
0.595
0.418
Philadelphia
0.395
0.560
0.507
0.530
0.403
0.063
Pike
3.238
0.672
0.189
0.402
0.355
0.464
Potter
9.589
0.784
0.091
0.427
0.454
0.440
Schuylkill
4.131
0.652
0.244
0.617
0.508
0.365
Snyder
4.609
0.700
0.150
0.414
0.476
0.385
Somerset
8.679
0.677
0.124
0.638
0.493
0.374
Sullivan
6.896
0.806
0.102
0.349
0.470
0.413
Susquehanna
5.903
0.640
0.116
0.458
0.461
0.384
Tioga
10.408
0.713
0.087
0.558
0.427
0.383
Union
4.643
0.682
0.162
0.374
0.516
0.444
Venango
4.995
0.681
0.155
0.381
0.486
0.473
Warren
9.187
0.740
0.084
0.361
0.444
0.490
Washington
2.376
0.670
0.341
0.544
0.548
0.277
Wayne
6.147
0.658
0.152
0.486
0.588
0.393
Westmoreland
1.320
0.577
0.546
0.506
0.588
0.303
Wyoming
4.022
0.765
0.206
0.426
0.501
0.391
York
1.879
0.596
0.507
0.666
0.502
0.340
Virginia
2.484
0.520
0.297
0.331
0.548
0.378
Accomack
6.907
0.562
0.162
0.504
0.534
0.650
Albemarle
3.807
0.651
0.263
0.563
0.540
0.398
211
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Alexandria city
-0.060
0.626
0.607
0.255
0.495
0.117
Alleghany
3.302
0.492
0.198
0.349
0.657
0.451
Amelia
6.454
0.570
0.111
0.331
0.651
0.445
Amherst
2.663
0.486
0.256
0.447
0.602
0.418
Appomattox
5.256
0.572
0.132
0.343
0.628
0.433
Arlington
-0.074
0.655
0.538
0.257
0.491
0.118
Augusta
4.228
0.478
0.208
0.558
0.574
0.498
Bath
3.679
0.386
0.181
0.357
0.726
0.550
Bedford
1.132
0.169
0.234
0.541
0.601
0.384
Bland
2.727
0.246
0.082
0.291
0.485
0.480
Botetourt
1.764
0.239
0.224
0.480
0.756
0.361
Bristol city
-0.064
0.160
0.546
0.119
0.546
0.159
Brunswick
4.723
0.512
0.115
0.361
0.532
0.427
Buchanan
1.789
0.540
0.150
0.402
0.394
0.265
Buckingham
2.982
0.558
0.170
0.360
0.502
0.391
Buena Vista city
-0.362
0.475
0.395
0.095
0.424
0.250
Campbell
2.861
0.440
0.203
0.451
0.652
0.340
Caroline
6.744
0.611
0.131
0.474
0.503
0.487
Carroll
2.626
0.450
0.140
0.396
0.504
0.312
Charles City
6.855
0.583
0.101
0.329
0.524
0.520
Charlotte
6.880
0.565
0.079
0.341
0.593
0.362
Charlottesville city
-0.497
0.583
0.515
0.164
0.539
0.026
Chesapeake city
0.962
0.524
0.714
0.398
0.544
0.481
Chesterfield
1.438
0.600
0.626
0.510
0.581
0.407
Clarke
6.612
0.647
0.106
0.320
0.646
0.389
Colonial Heights city
-0.001
0.650
0.655
0.158
0.593
0.169
Covington city
-0.269
0.272
0.309
0.010
0.590
0.207
Craig
3.813
0.598
0.159
0.258
0.555
0.502
Culpeper
3.173
0.718
0.282
0.372
0.656
0.393
Cumberland
7.993
0.715
0.096
0.323
0.538
0.471
Danville city
-0.554
0.417
0.458
0.040
0.416
0.192
Dickenson
2.287
0.323
0.129
0.369
0.434
0.437
Dinwiddie
4.137
0.486
0.149
0.392
0.531
0.479
Emporia city
-0.055
0.576
0.528
0.146
0.463
0.265
Essex
3.365
0.481
0.216
0.343
0.684
0.508
Fairfax
1.311
0.669
0.569
0.474
0.557
0.308
Fairfax city
0.691
0.693
0.485
0.175
0.842
0.148
Falls Church city
1.224
0.842
0.320
0.152
0.766
0.225
Fauquier
4.935
0.646
0.243
0.546
0.678
0.436
Floyd
2.192
0.414
0.128
0.336
0.552
0.285
Fluvanna
4.426
0.740
0.158
0.397
0.511
0.359
212
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Franklin
1.926
0.401
0.194
0.422
0.514
0.325
Franklin city
-0.031
0.656
0.331
0.146
0.399
0.330
Frederick
1.359
0.673
0.487
0.451
0.522
0.308
Fredericksburg city
-0.081
0.755
0.476
0.144
0.578
0.176
Galax city
-0.796
0.479
0.272
0.114
0.391
0.193
Giles
4.168
0.380
0.113
0.342
0.576
0.485
Gloucester
2.925
0.473
0.273
0.347
0.678
0.582
Goochland
2.854
0.603
0.215
0.422
0.525
0.350
Grayson
1.800
0.288
0.124
0.363
0.463
0.365
Greene
3.695
0.643
0.155
0.340
0.536
0.378
Greensville
2.068
0.558
0.122
0.333
0.251
0.436
Halifax
2.942
0.478
0.154
0.402
0.504
0.361
Hampton city
1.103
0.572
0.576
0.285
0.483
0.570
Hanover
2.160
0.567
0.399
0.474
0.677
0.376
Harrisonburg city
-0.117
0.716
0.391
0.266
0.448
0.142
Henrico
0.839
0.588
0.656
0.374
0.588
0.319
Henry
0.720
0.397
0.370
0.369
0.452
0.325
Highland
3.465
0.297
0.113
0.266
0.695
0.507
Hopewell city
-0.111
0.442
0.561
0.136
0.462
0.240
Isle of Wight
2.488
0.625
0.348
0.390
0.585
0.487
James City
1.604
0.653
0.519
0.327
0.539
0.546
King and Queen
7.095
0.586
0.107
0.306
0.569
0.551
King George
4.097
0.861
0.252
0.418
0.464
0.478
King William
12.156
0.771
0.105
0.397
0.648
0.541
Lancaster
3.122
0.545
0.301
0.322
0.711
0.597
Lee
1.323
0.293
0.141
0.370
0.418
0.338
Lexington city
-0.524
0.430
0.240
0.067
0.681
0.078
Loudoun
1.462
0.690
0.704
0.517
0.618
0.365
Louisa
4.414
0.599
0.156
0.476
0.501
0.366
Lunenburg
3.797
0.517
0.113
0.282
0.533
0.420
Lynchburg city
-0.214
0.521
0.507
0.227
0.436
0.134
Madison
4.244
0.432
0.136
0.350
0.657
0.451
Manassas city
0.427
0.597
0.435
0.236
0.712
0.115
Manassas Park city
-0.299
0.603
0.395
0.147
0.584
0.108
Martinsville city
-0.262
0.275
0.460
0.055
0.519
0.160
Mathews
4.484
0.550
0.232
0.264
0.702
0.735
Mecklenburg
6.109
0.554
0.116
0.410
0.597
0.411
Middlesex
4.145
0.640
0.253
0.317
0.632
0.631
Montgomery
1.268
0.334
0.288
0.443
0.487
0.390
Nelson
2.723
0.414
0.150
0.417
0.485
0.375
New Kent
5.284
0.550
0.164
0.422
0.622
0.499
213
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Newport News city
1.100
0.558
0.558
0.311
0.461
0.556
Norfolk city
0.597
0.536
0.523
0.294
0.433
0.386
Northampton
4.290
0.545
0.209
0.335
0.561
0.673
Northumberland
5.351
0.754
0.214
0.355
0.612
0.557
Norton city
-0.066
0.117
0.551
0.026
0.526
0.240
Nottoway
2.985
0.426
0.128
0.298
0.530
0.431
Orange
4.099
0.651
0.158
0.393
0.502
0.392
Page
4.828
0.413
0.125
0.399
0.589
0.505
Patrick
2.045
0.415
0.156
0.369
0.522
0.309
Petersburg city
0.024
0.599
0.517
0.177
0.417
0.296
Pittsylvania
3.622
0.474
0.198
0.540
0.530
0.421
Poquoson city
2.187
0.590
0.253
0.165
0.527
0.596
Portsmouth city
0.210
0.544
0.668
0.238
0.487
0.271
Powhatan
8.299
0.800
0.140
0.390
0.650
0.461
Prince Edward
5.321
0.562
0.109
0.363
0.562
0.391
Prince George
2.580
0.551
0.319
0.445
0.482
0.554
Prince William
1.351
0.634
0.600
0.472
0.597
0.345
Pulaski
2.129
0.408
0.189
0.369
0.518
0.401
Radford city
-0.484
0.478
0.331
0.133
0.334
0.266
Rappahannock
3.657
0.438
0.132
0.346
0.561
0.439
Richmond
2.697
0.539
0.238
0.341
0.550
0.489
Richmond city
-0.194
0.591
0.551
0.245
0.463
0.105
Roanoke
0.823
0.410
0.468
0.376
0.600
0.308
Roanoke city
-0.119
0.413
0.460
0.210
0.536
0.104
Rockbridge
2.397
0.269
0.158
0.446
0.572
0.444
Rockingham
4.678
0.546
0.216
0.616
0.555
0.454
Russell
0.939
0.141
0.124
0.398
0.438
0.368
Salem city
-0.143
0.423
0.451
0.146
0.598
0.116
Scott
0.986
0.212
0.139
0.381
0.400
0.346
Shenandoah
4.175
0.600
0.196
0.456
0.599
0.394
Smyth
1.829
0.236
0.159
0.387
0.544
0.461
Southampton
7.948
0.609
0.110
0.432
0.568
0.474
Spotsylvania
1.318
0.679
0.517
0.382
0.539
0.377
Stafford
1.503
0.674
0.554
0.393
0.564
0.439
Staunton city
0.313
0.648
0.356
0.156
0.569
0.260
Suffolk city
1.440
0.574
0.557
0.494
0.489
0.454
Surry
2.349
0.556
0.242
0.305
0.494
0.504
Sussex
4.987
0.503
0.110
0.355
0.496
0.476
Tazewell
1.401
0.293
0.164
0.409
0.457
0.324
Virginia Beach city
1.389
0.507
0.468
0.377
0.509
0.520
Warren
1.510
0.616
0.363
0.333
0.577
0.354
214
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Washington
0.762
0.183
0.277
0.443
0.475
0.422
Waynesboro city
-0.202
0.451
0.456
0.133
0.514
0.175
Westmoreland
3.181
0.660
0.251
0.297
0.651
0.463
Williamsburg city
1.001
0.873
0.387
0.218
0.491
0.365
Winchester city
-0.502
0.583
0.383
0.148
0.555
0.076
Wise
1.474
0.299
0.161
0.419
0.415
0.351
Wythe
2.792
0.355
0.153
0.375
0.580
0.431
York
1.666
0.576
0.590
0.326
0.595
0.679
West Virginia
2.158
0.555
0.168
0.324
0.435
0.328
Barbour
3.176
0.616
0.090
0.327
0.445
0.293
Berkeley
0.848
0.633
0.482
0.349
0.431
0.353
Boone
1.225
0.573
0.127
0.276
0.436
0.276
Braxton
6.851
0.615
0.075
0.337
0.442
0.435
Brooke
-0.014
0.592
0.373
0.251
0.380
0.235
Cabell
1.109
0.582
0.243
0.295
0.449
0.323
Calhoun
0.607
0.433
0.135
0.267
0.370
0.305
Clay
0.735
0.402
0.119
0.292
0.301
0.345
Doddridge
1.016
0.616
0.091
0.206
0.438
0.302
Fayette
6.513
0.584
0.112
0.461
0.551
0.380
Gilmer
1.325
0.750
0.077
0.222
0.364
0.337
Grant
4.007
0.552
0.126
0.326
0.539
0.403
Greenbrier
2.565
0.233
0.102
0.394
0.554
0.402
Hampshire
4.280
0.610
0.105
0.417
0.373
0.364
Hancock
-0.305
0.513
0.354
0.168
0.359
0.259
Hardy
2.341
0.451
0.153
0.384
0.420
0.384
Harrison
2.283
0.590
0.206
0.417
0.544
0.251
Jackson
1.891
0.560
0.150
0.365
0.439
0.272
Jefferson
1.420
0.602
0.332
0.401
0.449
0.339
Kanawha
2.625
0.633
0.224
0.475
0.519
0.260
Lewis
2.937
0.658
0.111
0.282
0.443
0.354
Lincoln
0.397
0.554
0.145
0.316
0.322
0.256
Logan
1.297
0.543
0.120
0.373
0.363
0.236
Marion
1.699
0.611
0.222
0.351
0.506
0.280
Marshall
1.271
0.642
0.206
0.358
0.412
0.261
Mason
1.484
0.584
0.181
0.360
0.368
0.316
McDowell
0.548
0.507
0.224
0.330
0.335
0.286
Mercer
1.260
0.354
0.144
0.345
0.458
0.282
Mineral
2.224
0.585
0.153
0.350
0.377
0.369
Mingo
1.009
0.578
0.143
0.371
0.296
0.276
Monongalia
0.729
0.546
0.399
0.385
0.404
0.289
215
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Monroe
2.833
0.306
0.086
0.317
0.557
0.347
Morgan
1.214
0.479
0.105
0.270
0.362
0.340
Nicholas
3.237
0.510
0.112
0.357
0.407
0.391
Ohio
0.109
0.608
0.394
0.199
0.569
0.172
Pendleton
2.948
0.547
0.177
0.332
0.463
0.472
Pleasants
0.450
0.759
0.154
0.273
0.359
0.267
Pocahontas
5.274
0.424
0.108
0.309
0.588
0.554
Preston
5.307
0.648
0.129
0.460
0.529
0.321
Putnam
2.156
0.594
0.274
0.413
0.573
0.313
Raleigh
2.740
0.502
0.174
0.408
0.530
0.334
Randolph
4.628
0.426
0.103
0.387
0.519
0.433
Ritchie
0.811
0.633
0.097
0.246
0.381
0.293
Roane
2.218
0.597
0.107
0.289
0.390
0.351
Summers
3.745
0.483
0.077
0.230
0.482
0.422
Taylor
1.725
0.536
0.107
0.198
0.530
0.317
Tucker
9.580
0.641
0.066
0.320
0.486
0.476
Tyler
1.180
0.684
0.106
0.223
0.421
0.309
Upshur
4.094
0.547
0.106
0.355
0.546
0.313
Wayne
2.243
0.602
0.169
0.395
0.365
0.350
Webster
0.446
0.474
0.137
0.249
0.222
0.418
Wetzel
0.812
0.579
0.147
0.267
0.397
0.291
Wirt
-0.412
0.455
0.116
0.240
0.296
0.274
Wood
1.174
0.650
0.256
0.340
0.477
0.251
Wyoming
0.820
0.539
0.217
0.317
0.369
0.309
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 4
1.443
0.443
0.255
0.342
0.414
0.403
Alabama
1.065
0.335
0.296
0.408
0.385
0.397
Autauga
1.670
0.368
0.238
0.423
0.477
0.412
Baldwin
1.731
0.338
0.418
0.772
0.446
0.490
Barbour
2.961
0.364
0.093
0.372
0.337
0.449
Bibb
0.836
0.256
0.246
0.355
0.403
0.433
Blount
0.715
0.207
0.244
0.412
0.456
0.345
Bullock
0.666
0.161
0.085
0.266
0.332
0.403
Butler
0.867
0.309
0.276
0.356
0.405
0.419
Calhoun
0.327
0.225
0.669
0.456
0.399
0.396
Chambers
0.565
0.245
0.140
0.308
0.303
0.369
Cherokee
0.769
0.416
0.293
216
0.351
0.379
0.351
-------
Area
, Built _ . Natural
CRSI Governance Risk Society
Environment Environment
Chilton
Choctaw
Clarke
Clay
Cleburne
Coffee
Colbert
Conecuh
Coosa
Covington
Crenshaw
Cullman
Dale
Dallas
DeKalb
Elmore
Escambia
Etowah
Fayette
Franklin
Geneva
Greene
Hale
Henry
Houston
Jackson
Jefferson
Lamar
Lauderdale
Lawrence
Lee
Limestone
Lowndes
Macon
Madison
Marengo
Marion
Marshall
Mobile
Monroe
Montgomery
2.143
0.319
0.363
0.470
0.723
0.345
1.083
0.195
1.120
0.287
2.173
0.507
1.142
0.224
0.317
0.328
0.814
0.210
1.469
0.366
1.297
0.450
0.787
0.272
2.156
0.441
1.200
0.398
1.013
0.435
1.239
0.339
0.895
0.337
0.549
0.274
1.449
0.416
0.872
0.327
1.758
0.288
0.383
0.340
0.494
0.412
0.458
0.162
1.658
0.422
2.097
0.469
0.394
0.259
2.069
0.452
1.341
0.257
1.010
0.246
0.597
0.301
0.726
0.380
2.197
0.439
1.124
0.263
0.610
0.421
1.064
0.381
1.296
0.465
0.703
0.458
0.750
0.233
0.632
0.300
1.012
0.435
0.158
0.421
0.274
0.262
0.318
0.400
0.129
0.349
0.131
0.308
0.259
0.406
0.262
0.432
0.275
0.318
0.114
0.344
0.298
0.404
0.205
0.349
0.295
0.454
0.187
0.416
0.138
0.326
0.287
0.441
0.360
0.480
0.347
0.434
0.420
0.400
0.182
0.336
0.242
0.376
0.128
0.348
0.251
0.312
0.259
0.295
0.226
0.326
0.308
0.465
0.207
0.488
0.915
0.668
0.153
0.381
0.215
0.398
0.233
0.380
0.445
0.451
0.597
0.438
0.112
0.372
0.113
0.302
0.804
0.567
0.276
0.363
0.331
0.482
0.361
0.391
0.536
0.647
0.311
0.377
0.519
0.484
217
0.485
0.401
0.243
0.421
0.359
0.364
0.372
0.430
0.309
0.440
0.476
0.445
0.535
0.462
0.172
0.440
0.263
0.409
0.510
0.454
0.380
0.371
0.467
0.294
0.385
0.426
0.282
0.404
0.356
0.322
0.487
0.440
0.304
0.475
0.470
0.348
0.385
0.400
0.336
0.400
0.390
0.442
0.223
0.412
0.241
0.427
0.405
0.397
0.492
0.407
0.324
0.400
0.504
0.252
0.335
0.417
0.502
0.437
0.389
0.479
0.379
0.381
0.430
0.458
0.330
0.374
0.412
0.352
0.437
0.324
0.387
0.425
0.353
0.384
0.356
0.330
0.457
0.449
0.362
0.382
0.534
0.353
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Morgan
0.828
0.379
0.425
0.458
0.449
0.337
Perry
0.231
0.261
0.160
0.261
0.259
0.381
Pickens
1.610
0.470
0.184
0.369
0.378
0.367
Pike
2.839
0.445
0.142
0.380
0.396
0.453
Randolph
1.691
0.398
0.119
0.360
0.388
0.319
Russell
1.349
0.349
0.196
0.391
0.389
0.387
Shelby
0.952
0.339
0.531
0.597
0.495
0.376
St. Clair
0.578
0.286
0.566
0.532
0.456
0.337
Sumter
0.341
0.425
0.275
0.234
0.270
0.434
Talladega
0.601
0.191
0.303
0.485
0.358
0.390
Tallapoosa
0.833
0.295
0.336
0.404
0.451
0.400
Tuscaloosa
0.979
0.323
0.497
0.603
0.488
0.382
Walker
0.532
0.213
0.339
0.481
0.371
0.341
Washington
0.962
0.402
0.352
0.448
0.320
0.415
Wilcox
0.484
0.314
0.221
0.308
0.288
0.388
Winston
0.272
0.160
0.274
0.348
0.284
0.397
Florida
1.969
0.427
0.312
0.485
0.434
0.426
Alachua
2.929
0.408
0.241
0.700
0.468
0.385
Baker
2.876
0.390
0.131
0.274
0.476
0.530
Bay
2.768
0.464
0.266
0.588
0.520
0.406
Bradford
3.497
0.365
0.096
0.320
0.524
0.421
Brevard
1.598
0.522
0.622
0.673
0.470
0.483
Broward
1.407
0.523
0.722
0.713
0.479
0.448
Calhoun
1.014
0.205
0.111
0.243
0.396
0.458
Charlotte
2.207
0.504
0.273
0.516
0.438
0.390
Citrus
3.151
0.498
0.152
0.422
0.361
0.459
Clay
1.939
0.522
0.356
0.415
0.515
0.490
Collier
2.681
0.604
0.398
0.583
0.437
0.550
Columbia
2.317
0.255
0.156
0.459
0.506
0.488
DeSoto
0.702
0.432
0.242
0.404
0.292
0.301
Dixie
1.829
0.383
0.122
0.283
0.361
0.456
Duval
1.428
0.500
0.688
0.809
0.470
0.360
Hernando
1.051
0.404
0.253
0.348
0.374
0.405
Highlands
1.938
0.473
0.245
0.516
0.383
0.357
Hillsborough
1.710
0.538
0.604
0.867
0.445
0.298
Holmes
2.212
0.317
0.104
0.284
0.434
0.455
Indian River
0.759
0.420
0.543
0.462
0.402
0.390
Jackson
2.058
0.259
0.143
0.433
0.469
0.430
Jefferson
6.115
0.529
0.087
0.316
0.555
0.439
Lafayette
0.851
0.366
0.145
218
0.144
0.383
0.488
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lake
1.103
0.324
0.488
0.676
0.430
0.412
Lee
1.864
0.579
0.430
0.653
0.424
0.331
Leon
1.458
0.330
0.375
0.518
0.541
0.494
Levy
4.202
0.463
0.128
0.470
0.421
0.440
Liberty
0.935
0.227
0.193
0.195
0.265
0.713
Madison
3.320
0.476
0.129
0.393
0.474
0.376
Manatee
0.931
0.547
0.442
0.423
0.443
0.307
Marion
1.549
0.317
0.329
0.642
0.411
0.445
Martin
1.485
0.552
0.456
0.495
0.476
0.395
Miami-Dade
1.465
0.375
0.623
0.831
0.456
0.536
Monroe
2.661
0.199
0.156
0.595
0.489
0.628
Nassau
2.032
0.465
0.252
0.464
0.468
0.384
Okaloosa
1.583
0.296
0.318
0.595
0.482
0.474
Okeechobee
1.921
0.602
0.153
0.390
0.263
0.379
Orange
0.972
0.391
0.716
0.820
0.462
0.278
Osceola
1.471
0.479
0.441
0.652
0.404
0.335
Palm Beach
2.249
0.529
0.494
0.817
0.470
0.407
Pasco
1.483
0.465
0.380
0.588
0.378
0.367
Pinellas
1.818
0.561
0.436
0.598
0.436
0.391
Polk
1.416
0.388
0.514
0.834
0.429
0.329
Putnam
2.273
0.375
0.160
0.495
0.335
0.404
Santa Rosa
1.073
0.336
0.470
0.557
0.440
0.469
Sarasota
1.293
0.560
0.428
0.476
0.519
0.285
Seminole
0.517
0.356
0.747
0.503
0.524
0.290
St. Johns
1.989
0.533
0.388
0.533
0.448
0.466
St. Lucie
0.725
0.518
0.532
0.449
0.398
0.311
Sumter
0.841
0.370
0.381
0.427
0.346
0.426
Suwannee
2.327
0.394
0.120
0.368
0.446
0.345
Taylor
3.961
0.617
0.115
0.326
0.398
0.444
Union
2.831
0.402
0.075
0.253
0.432
0.409
Volusia
1.949
0.482
0.474
0.741
0.428
0.447
Wakulla
2.270
0.335
0.216
0.353
0.483
0.640
Walton
1.877
0.216
0.172
0.538
0.430
0.496
Washington
1.939
0.301
0.147
0.341
0.431
0.485
Georgia
1.429
0.442
0.224
0.282
0.420
0.395
Appling
4.127
0.462
0.088
0.265
0.589
0.374
Atkinson
-2.627
0.675
0.083
0.216
0.140
0.336
Bacon
0.755
0.461
0.095
0.185
0.363
0.387
Baker
0.633
0.387
0.090
0.221
0.182
0.488
Baldwin
0.892
0.314
0.176
219
0.299
0.450
0.334
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Banks
0.569
0.544
0.230
0.246
0.411
0.316
Barrow
0.222
0.461
0.503
0.251
0.470
0.265
Bartow
0.307
0.204
0.583
0.388
0.452
0.387
Ben Hill
0.479
0.263
0.109
0.217
0.373
0.360
Berrien
2.317
0.504
0.083
0.269
0.330
0.413
Bibb
0.305
0.431
0.531
0.287
0.502
0.253
Bleckley
3.087
0.429
0.100
0.267
0.449
0.456
Brantley
1.791
0.525
0.113
0.278
0.352
0.389
Brooks
2.427
0.421
0.080
0.263
0.341
0.442
Bryan
3.878
0.488
0.172
0.411
0.526
0.504
Bulloch
2.522
0.457
0.185
0.388
0.458
0.442
Burke
3.251
0.497
0.129
0.379
0.428
0.403
Butts
1.569
0.610
0.179
0.236
0.483
0.359
Calhoun
2.999
0.520
0.077
0.222
0.372
0.457
Camden
3.705
0.721
0.259
0.392
0.478
0.549
Candler
2.086
0.383
0.069
0.175
0.434
0.429
Carroll
0.728
0.366
0.304
0.373
0.442
0.301
Catoosa
0.183
0.463
0.585
0.221
0.417
0.335
Charlton
3.361
0.585
0.125
0.233
0.342
0.580
Chatham
1.541
0.517
0.697
0.625
0.566
0.530
Chattahoochee
1.236
0.385
0.167
0.260
0.238
0.559
Chattooga
0.511
0.299
0.138
0.256
0.308
0.385
Cherokee
0.370
0.261
0.611
0.354
0.535
0.354
Clarke
0.062
0.398
0.654
0.251
0.427
0.242
Clay
-0.255
0.299
0.098
0.199
0.216
0.392
Clayton
0.019
0.567
0.477
0.238
0.450
0.202
Clinch
-0.909
0.471
0.129
0.185
0.172
0.374
Cobb
0.347
0.359
0.558
0.380
0.514
0.210
Coffee
1.726
0.454
0.124
0.299
0.446
0.324
Colquitt
4.204
0.520
0.108
0.389
0.470
0.372
Columbia
1.182
0.560
0.471
0.297
0.544
0.460
Cook
1.845
0.536
0.155
0.223
0.416
0.456
Coweta
1.205
0.505
0.400
0.387
0.555
0.339
Crawford
0.660
0.518
0.121
0.267
0.259
0.380
Crisp
1.551
0.407
0.188
0.289
0.465
0.421
Dade
0.052
0.134
0.275
0.234
0.348
0.325
Dawson
0.452
0.220
0.353
0.242
0.471
0.470
Decatur
0.681
0.081
0.120
0.357
0.482
0.453
DeKalb
0.629
0.539
0.483
0.441
0.493
0.170
Dodge
1.765
0.298
0.088
0.308
0.391
0.380
Dooly
2.408
0.476
0.090
0.242
0.349
0.458
220
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Dougherty
1.146
0.324
0.269
0.342
0.491
0.436
Douglas
0.301
0.478
0.481
0.259
0.502
0.254
Early
2.699
0.461
0.096
0.253
0.390
0.456
Echols
-1.569
0.443
0.084
0.197
0.094
0.403
Effingham
2.241
0.489
0.257
0.409
0.497
0.452
Elbert
1.207
0.431
0.165
0.247
0.444
0.379
Emanuel
2.643
0.463
0.091
0.301
0.403
0.377
Evans
5.126
0.636
0.085
0.226
0.446
0.492
Fannin
1.802
0.381
0.224
0.244
0.518
0.566
Fayette
0.802
0.530
0.470
0.278
0.560
0.354
Floyd
0.489
0.352
0.382
0.288
0.423
0.379
Forsyth
0.630
0.477
0.587
0.313
0.533
0.364
Franklin
0.834
0.591
0.246
0.239
0.442
0.342
Fulton
1.198
0.482
0.551
0.709
0.490
0.210
Gilmer
0.975
0.300
0.238
0.278
0.402
0.506
Glascock
-1.372
0.564
0.103
0.125
0.342
0.302
Glynn
1.745
0.478
0.352
0.366
0.552
0.497
Gordon
0.309
0.320
0.535
0.326
0.376
0.370
Grady
4.522
0.536
0.089
0.307
0.389
0.473
Greene
2.138
0.406
0.123
0.309
0.405
0.417
Gwinnett
0.561
0.561
0.569
0.418
0.553
0.150
Habersham
0.406
0.182
0.372
0.339
0.475
0.403
Hall
0.731
0.528
0.606
0.380
0.536
0.314
Hancock
1.418
0.330
0.085
0.234
0.414
0.378
Haralson
0.330
0.351
0.182
0.249
0.383
0.307
Harris
2.349
0.446
0.158
0.373
0.447
0.389
Hart
1.536
0.582
0.225
0.275
0.401
0.436
Heard
0.581
0.512
0.138
0.228
0.351
0.350
Henry
1.149
0.631
0.457
0.341
0.540
0.351
Houston
0.753
0.506
0.504
0.329
0.470
0.387
Irwin
1.287
0.347
0.086
0.207
0.437
0.370
Jackson
0.777
0.547
0.489
0.378
0.479
0.303
Jasper
3.004
0.581
0.099
0.318
0.370
0.381
Jeff Davis
0.376
0.482
0.084
0.185
0.360
0.352
Jefferson
2.902
0.534
0.107
0.293
0.441
0.380
Jenkins
0.049
0.470
0.088
0.117
0.334
0.424
Johnson
1.213
0.562
0.133
0.269
0.308
0.392
Jones
3.045
0.434
0.113
0.340
0.481
0.382
Lamar
0.931
0.486
0.215
0.228
0.422
0.397
Lanier
1.448
0.565
0.113
0.247
0.337
0.394
Laurens
3.785
0.497
0.147
0.383
0.584
0.386
221
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lee
3.040
0.684
0.217
0.365
0.515
0.399
Liberty
2.494
0.547
0.214
0.396
0.366
0.488
Lincoln
2.998
0.539
0.120
0.233
0.360
0.545
Long
1.118
0.260
0.109
0.268
0.302
0.473
Lowndes
1.689
0.491
0.334
0.443
0.516
0.388
Lumpkin
0.971
0.279
0.251
0.273
0.393
0.558
Macon
2.381
0.466
0.093
0.218
0.398
0.452
Madison
1.003
0.443
0.214
0.275
0.513
0.303
Marion
-0.030
0.396
0.101
0.204
0.189
0.438
McDuffie
3.066
0.579
0.148
0.236
0.548
0.439
Mcintosh
1.149
0.154
0.143
0.328
0.453
0.529
Meriwether
1.775
0.400
0.145
0.309
0.439
0.390
Miller
2.100
0.564
0.173
0.204
0.486
0.466
Mitchell
2.388
0.423
0.156
0.323
0.505
0.420
Monroe
1.839
0.497
0.205
0.356
0.524
0.318
Montgomery
0.836
0.593
0.072
0.237
0.283
0.371
Morgan
2.751
0.530
0.169
0.370
0.541
0.336
Murray
0.331
0.271
0.248
0.295
0.231
0.432
Muscogee
0.435
0.338
0.472
0.281
0.490
0.365
Newton
0.691
0.532
0.412
0.311
0.475
0.314
Oconee
0.588
0.493
0.494
0.296
0.592
0.259
Oglethorpe
0.965
0.443
0.145
0.264
0.311
0.408
Paulding
0.433
0.402
0.444
0.274
0.460
0.343
Peach
0.833
0.592
0.431
0.283
0.455
0.390
Pickens
0.397
0.276
0.397
0.327
0.445
0.336
Pierce
3.446
0.534
0.098
0.290
0.421
0.420
Pike
3.933
0.565
0.128
0.322
0.523
0.408
Polk
0.307
0.268
0.378
0.291
0.416
0.342
Pulaski
2.688
0.421
0.116
0.232
0.465
0.493
Putnam
1.555
0.378
0.145
0.270
0.404
0.440
Quitman
-0.319
0.224
0.086
0.171
0.227
0.397
Rabun
1.467
0.442
0.294
0.284
0.493
0.508
Randolph
0.831
0.283
0.075
0.227
0.357
0.370
Richmond
0.727
0.492
0.591
0.341
0.556
0.355
Rockdale
0.590
0.579
0.436
0.216
0.594
0.286
Schley
0.459
0.361
0.130
0.216
0.240
0.454
Screven
3.197
0.577
0.099
0.221
0.431
0.454
Seminole
0.717
0.201
0.159
0.223
0.428
0.461
Spalding
0.556
0.505
0.391
0.238
0.466
0.359
Stephens
0.390
0.250
0.359
0.231
0.496
0.394
Stewart
0.563
0.312
0.094
0.227
0.292
0.403
222
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Sumter
3.518
0.479
0.129
0.333
0.500
0.440
Talbot
0.226
0.374
0.060
0.237
0.251
0.371
Taliaferro
-3.631
0.560
0.106
0.057
0.112
0.383
Tattnall
4.646
0.537
0.065
0.278
0.363
0.451
Taylor
1.472
0.583
0.109
0.269
0.292
0.400
Telfair
2.051
0.367
0.066
0.259
0.361
0.395
Terrell
1.127
0.527
0.060
0.187
0.332
0.398
Thomas
4.311
0.391
0.111
0.402
0.500
0.475
Tift
1.221
0.387
0.257
0.315
0.515
0.390
Toombs
3.573
0.535
0.089
0.289
0.420
0.407
Towns
2.695
0.452
0.182
0.191
0.611
0.559
Treutlen
0.504
0.250
0.080
0.165
0.459
0.334
Troup
1.240
0.414
0.290
0.366
0.448
0.412
Turner
0.958
0.410
0.060
0.239
0.275
0.392
Twiggs
2.044
0.418
0.070
0.282
0.323
0.390
Union
0.294
0.044
0.168
0.231
0.543
0.586
Upson
1.351
0.504
0.104
0.257
0.400
0.328
Walker
0.156
0.136
0.253
0.274
0.310
0.386
Walton
1.092
0.479
0.345
0.382
0.542
0.286
Ware
3.338
0.547
0.157
0.302
0.524
0.459
Warren
-0.387
0.478
0.086
0.178
0.258
0.386
Washington
6.120
0.578
0.090
0.348
0.500
0.424
Wayne
3.731
0.487
0.080
0.308
0.448
0.370
Webster
-0.656
0.333
0.100
0.130
0.189
0.441
Wheeler
1.086
0.481
0.066
0.193
0.374
0.366
White
1.147
0.455
0.265
0.219
0.527
0.426
Whitfield
0.266
0.360
0.411
0.289
0.380
0.320
Wilcox
-0.022
0.297
0.061
0.136
0.314
0.413
Wilkes
1.194
0.583
0.099
0.176
0.371
0.409
Wilkinson
3.101
0.524
0.114
0.263
0.509
0.395
Worth
4.686
0.512
0.070
0.308
0.432
0.394
Kentucky
1.041
0.534
0.200
0.255
0.388
0.371
Adair
1.691
0.550
0.110
0.259
0.433
0.325
Allen
0.282
0.438
0.132
0.265
0.259
0.354
Anderson
1.532
0.417
0.170
0.275
0.556
0.326
Ballard
1.473
0.643
0.185
0.183
0.473
0.410
Barren
1.054
0.263
0.163
0.322
0.407
0.405
Bath
-0.424
0.601
0.152
0.213
0.269
0.324
Bell
0.675
0.467
0.199
0.277
0.224
0.451
Boone
0.674
0.613
0.571
223
0.289
0.531
0.331
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Bourbon
3.942
0.679
0.124
0.284
0.439
0.447
Boyd
0.773
0.609
0.304
0.236
0.399
0.396
Boyle
1.410
0.652
0.180
0.213
0.478
0.359
Bracken
0.128
0.652
0.095
0.199
0.306
0.360
Breathitt
-0.534
0.735
0.153
0.236
0.220
0.336
Breckinridge
3.037
0.537
0.098
0.276
0.476
0.359
Bullitt
1.307
0.654
0.386
0.330
0.498
0.372
Butler
0.873
0.316
0.089
0.214
0.412
0.352
Caldwell
1.836
0.534
0.148
0.232
0.474
0.390
Calloway
0.948
0.350
0.237
0.304
0.491
0.353
Campbell
0.295
0.576
0.389
0.174
0.473
0.329
Carlisle
0.050
0.509
0.176
0.159
0.338
0.378
Carroll
0.213
0.567
0.153
0.257
0.255
0.354
Carter
0.684
0.598
0.119
0.272
0.275
0.354
Casey
0.509
0.477
0.084
0.208
0.319
0.369
Christian
1.141
0.400
0.288
0.363
0.383
0.449
Clark
1.588
0.534
0.172
0.308
0.424
0.350
Clay
0.234
0.550
0.163
0.224
0.218
0.424
Clinton
0.718
0.547
0.134
0.199
0.348
0.391
Crittenden
-0.248
0.649
0.142
0.133
0.336
0.378
Cumberland
-0.205
0.307
0.128
0.135
0.353
0.350
Daviess
1.442
0.593
0.397
0.359
0.595
0.341
Edmonson
0.640
0.451
0.099
0.199
0.266
0.443
Elliott
-1.907
0.718
0.122
0.165
0.240
0.311
Estill
0.119
0.533
0.179
0.228
0.287
0.353
Fayette
0.780
0.601
0.500
0.369
0.481
0.293
Fleming
0.361
0.546
0.163
0.225
0.369
0.319
Floyd
0.346
0.559
0.170
0.282
0.385
0.243
Franklin
1.244
0.557
0.243
0.226
0.568
0.335
Fulton
-0.616
0.472
0.201
0.107
0.206
0.426
Gallatin
0.132
0.597
0.175
0.200
0.324
0.353
Garrard
2.002
0.583
0.115
0.253
0.428
0.359
Grant
3.633
0.577
0.103
0.299
0.481
0.370
Graves
1.356
0.480
0.200
0.288
0.447
0.373
Grayson
0.534
0.571
0.242
0.238
0.401
0.327
Green
0.860
0.497
0.116
0.184
0.431
0.351
Greenup
1.168
0.624
0.253
0.346
0.358
0.345
Hancock
1.432
0.698
0.204
0.222
0.475
0.363
Hardin
1.638
0.472
0.283
0.428
0.466
0.376
Harlan
0.184
0.432
0.198
0.266
0.291
0.326
Harrison
2.757
0.692
0.135
0.236
0.479
0.394
224
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Hart
-0.204
0.300
0.138
0.215
0.264
0.332
Henderson
2.306
0.628
0.252
0.399
0.432
0.412
Henry
2.324
0.707
0.110
0.219
0.443
0.369
Hickman
0.194
0.412
0.159
0.081
0.448
0.398
Hopkins
1.968
0.480
0.198
0.368
0.494
0.350
Jackson
1.219
0.576
0.145
0.276
0.277
0.418
Jefferson
0.774
0.628
0.675
0.507
0.466
0.230
Jessamine
0.928
0.631
0.344
0.283
0.436
0.364
Johnson
-0.126
0.586
0.232
0.234
0.308
0.293
Kenton
0.233
0.608
0.667
0.210
0.474
0.311
Knott
0.192
0.592
0.123
0.287
0.215
0.346
Knox
0.636
0.389
0.131
0.222
0.316
0.407
Larue
2.345
0.488
0.097
0.212
0.513
0.364
Laurel
0.828
0.411
0.313
0.308
0.408
0.410
Lawrence
0.017
0.413
0.144
0.263
0.240
0.339
Lee
-0.200
0.743
0.174
0.232
0.233
0.356
Leslie
-0.037
0.365
0.160
0.243
0.164
0.412
Letcher
-0.300
0.407
0.143
0.278
0.204
0.306
Lewis
-1.535
0.650
0.138
0.166
0.217
0.328
Lincoln
2.338
0.411
0.121
0.306
0.511
0.352
Livingston
0.686
0.693
0.237
0.189
0.440
0.352
Logan
2.825
0.608
0.150
0.310
0.423
0.424
Lyon
2.069
0.551
0.188
0.159
0.496
0.532
Madison
1.268
0.535
0.373
0.394
0.458
0.379
Magoffin
-0.640
0.501
0.300
0.178
0.214
0.294
Marion
2.918
0.498
0.109
0.239
0.511
0.407
Marshall
0.646
0.408
0.369
0.278
0.502
0.349
Martin
-1.292
0.454
0.168
0.238
0.063
0.310
Mason
1.310
0.557
0.151
0.243
0.451
0.334
McCracken
0.586
0.510
0.615
0.290
0.517
0.373
McCreary
0.281
0.408
0.161
0.213
0.165
0.496
McLean
0.815
0.586
0.171
0.190
0.407
0.379
Meade
2.209
0.572
0.114
0.301
0.343
0.393
Menifee
1.070
0.469
0.204
0.261
0.386
0.412
Mercer
1.564
0.575
0.180
0.281
0.465
0.337
Metcalfe
-1.225
0.468
0.067
0.143
0.266
0.375
Monroe
-0.629
0.379
0.160
0.147
0.244
0.350
Montgomery
1.465
0.608
0.194
0.278
0.393
0.388
Morgan
0.300
0.592
0.339
0.253
0.381
0.304
Muhlenberg
2.933
0.541
0.129
0.293
0.508
0.374
Nelson
3.634
0.574
0.162
0.346
0.566
0.404
225
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Nicholas
0.412
0.442
0.106
0.152
0.390
0.378
Ohio
3.126
0.633
0.137
0.299
0.486
0.376
Oldham
1.095
0.663
0.410
0.288
0.538
0.346
Owen
1.738
0.426
0.096
0.270
0.410
0.352
Owsley
-0.088
0.591
0.227
0.202
0.254
0.377
Pendleton
3.494
0.649
0.119
0.253
0.520
0.384
Perry
0.217
0.433
0.195
0.306
0.279
0.299
Pike
1.982
0.525
0.151
0.478
0.320
0.271
Powell
0.553
0.352
0.200
0.272
0.298
0.408
Pulaski
0.721
0.292
0.329
0.307
0.427
0.469
Robertson
-1.436
0.716
0.128
0.184
0.195
0.353
Rockcastle
1.170
0.473
0.200
0.306
0.386
0.375
Rowan
1.992
0.612
0.225
0.321
0.444
0.408
Russell
1.489
0.386
0.147
0.192
0.478
0.454
Scott
1.808
0.625
0.252
0.338
0.453
0.382
Shelby
3.048
0.636
0.215
0.376
0.519
0.411
Simpson
1.187
0.463
0.145
0.240
0.386
0.396
Spencer
4.870
0.593
0.087
0.278
0.568
0.349
Taylor
1.796
0.414
0.151
0.215
0.469
0.472
Todd
1.655
0.561
0.125
0.230
0.340
0.443
Trigg
3.384
0.508
0.134
0.230
0.452
0.564
Trimble
-0.223
0.701
0.148
0.126
0.395
0.342
Union
2.354
0.604
0.138
0.258
0.421
0.416
Warren
0.864
0.321
0.297
0.408
0.466
0.324
Washington
3.732
0.666
0.120
0.230
0.605
0.354
Wayne
0.077
0.501
0.121
0.188
0.332
0.352
Webster
1.118
0.626
0.282
0.235
0.475
0.383
Whitley
0.625
0.423
0.210
0.236
0.374
0.384
Wolfe
-0.919
0.460
0.189
0.159
0.130
0.384
Woodford
2.647
0.616
0.195
0.303
0.471
0.450
Mississippi
1.434
0.494
0.273
0.337
0.382
0.444
Adams
2.886
0.636
0.190
0.300
0.467
0.467
Alcorn
1.459
0.347
0.122
0.317
0.440
0.327
Amite
1.966
0.516
0.204
0.358
0.364
0.449
Attala
0.973
0.507
0.229
0.299
0.353
0.386
Benton
0.072
0.602
0.332
0.233
0.164
0.445
Bolivar
2.416
0.587
0.249
0.445
0.365
0.456
Calhoun
1.578
0.512
0.114
0.326
0.301
0.363
Carroll
0.701
0.639
0.268
0.298
0.304
0.367
Chickasaw
2.654
0.529
0.176
226
0.339
0.347
0.528
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Choctaw
0.858
0.481
0.310
0.282
0.392
0.418
Claiborne
0.467
0.370
0.281
0.228
0.386
0.403
Clarke
1.112
0.398
0.251
0.347
0.384
0.415
Clay
1.147
0.390
0.244
0.341
0.306
0.490
Coahoma
2.234
0.620
0.204
0.346
0.345
0.463
Copiah
1.391
0.360
0.191
0.355
0.399
0.410
Covington
1.342
0.472
0.280
0.334
0.441
0.423
DeSoto
1.006
0.596
0.638
0.433
0.449
0.423
Forrest
0.881
0.418
0.586
0.394
0.465
0.516
Franklin
0.804
0.461
0.260
0.219
0.377
0.459
George
2.366
0.378
0.178
0.328
0.473
0.533
Greene
1.208
0.377
0.208
0.324
0.317
0.481
Grenada
1.178
0.488
0.212
0.296
0.376
0.400
Hancock
0.950
0.444
0.429
0.430
0.336
0.451
Harrison
0.730
0.464
0.754
0.462
0.428
0.453
Hinds
2.115
0.561
0.392
0.554
0.519
0.400
Holmes
1.275
0.505
0.214
0.327
0.293
0.444
Humphreys
0.919
0.591
0.281
0.229
0.242
0.550
Issaquena
-0.069
0.577
0.419
0.232
0.103
0.459
Itawamba
1.361
0.468
0.141
0.262
0.370
0.400
Jackson
1.429
0.406
0.427
0.473
0.391
0.599
Jasper
1.555
0.476
0.262
0.365
0.343
0.493
Jefferson
0.267
0.520
0.298
0.248
0.248
0.409
Jefferson Davis
0.739
0.432
0.241
0.286
0.292
0.438
Jones
1.861
0.447
0.260
0.415
0.435
0.455
Kemper
1.062
0.528
0.310
0.382
0.313
0.403
Lafayette
2.235
0.404
0.220
0.448
0.429
0.479
Lamar
0.951
0.414
0.513
0.424
0.442
0.480
Lauderdale
2.096
0.517
0.293
0.460
0.482
0.414
Lawrence
1.287
0.568
0.320
0.315
0.427
0.426
Leake
1.388
0.564
0.239
0.302
0.402
0.405
Lee
1.074
0.569
0.521
0.401
0.489
0.388
Leflore
2.371
0.611
0.185
0.329
0.380
0.445
Lincoln
1.768
0.366
0.197
0.383
0.511
0.378
Lowndes
1.352
0.413
0.391
0.398
0.497
0.505
Madison
1.703
0.560
0.478
0.510
0.539
0.420
Marion
1.734
0.519
0.271
0.364
0.428
0.445
Marshall
2.285
0.590
0.186
0.386
0.369
0.394
Monroe
3.523
0.488
0.157
0.415
0.429
0.483
Montgomery
1.246
0.598
0.225
0.235
0.521
0.333
Neshoba
1.137
0.448
0.283
0.363
0.366
0.419
Ill
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Newton
1.506
0.446
0.263
0.306
0.475
0.463
Noxubee
0.966
0.491
0.263
0.251
0.331
0.488
Oktibbeha
1.753
0.497
0.264
0.420
0.363
0.447
Panola
2.253
0.539
0.191
0.371
0.396
0.419
Pearl River
1.010
0.354
0.384
0.427
0.405
0.472
Perry
2.028
0.404
0.211
0.318
0.307
0.655
Pike
1.936
0.495
0.237
0.346
0.471
0.439
Pontotoc
1.350
0.476
0.234
0.315
0.445
0.387
Prentiss
1.402
0.473
0.142
0.292
0.419
0.333
Quitman
0.588
0.608
0.193
0.180
0.272
0.476
Rankin
1.234
0.522
0.652
0.566
0.544
0.393
Scott
1.773
0.466
0.336
0.385
0.421
0.580
Sharkey
-0.138
0.489
0.301
0.090
0.138
0.572
Simpson
1.512
0.416
0.227
0.311
0.530
0.388
Smith
1.358
0.442
0.262
0.331
0.398
0.464
Stone
0.767
0.135
0.208
0.361
0.425
0.563
Sunflower
2.889
0.637
0.164
0.357
0.339
0.458
Tallahatchie
1.523
0.494
0.118
0.256
0.318
0.430
Tate
2.016
0.622
0.172
0.284
0.401
0.411
Tippah
1.268
0.497
0.144
0.327
0.348
0.330
Tishomingo
1.699
0.371
0.125
0.278
0.342
0.471
Tunica
0.461
0.762
0.386
0.243
0.209
0.478
Union
1.706
0.492
0.165
0.338
0.424
0.342
Walthall
1.957
0.623
0.206
0.291
0.405
0.438
Warren
1.837
0.633
0.335
0.360
0.468
0.444
Washington
2.148
0.599
0.209
0.324
0.400
0.447
Wayne
0.728
0.272
0.196
0.312
0.304
0.445
Webster
0.468
0.540
0.261
0.271
0.316
0.360
Wilkinson
1.320
0.447
0.210
0.309
0.397
0.413
Winston
1.137
0.549
0.384
0.313
0.435
0.449
Yalobusha
2.471
0.443
0.095
0.260
0.360
0.460
Yazoo
1.619
0.578
0.241
0.333
0.296
0.490
North Carolina
2.141
0.435
0.273
0.419
0.463
0.431
Alamance
1.273
0.525
0.376
0.435
0.471
0.336
Alexander
1.467
0.448
0.200
0.335
0.466
0.345
Alleghany
0.907
0.201
0.123
0.341
0.384
0.362
Anson
2.841
0.483
0.109
0.356
0.385
0.381
Ashe
1.514
0.221
0.120
0.378
0.480
0.354
Avery
1.523
0.251
0.174
0.393
0.481
0.434
Beaufort
2.216
0.560
0.381
0.459
0.513
0.519
228
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Bertie
4.796
0.560
0.156
0.382
0.545
0.508
Bladen
3.478
0.386
0.132
0.482
0.367
0.481
Brunswick
2.343
0.522
0.356
0.589
0.418
0.491
Buncombe
0.269
0.110
0.506
0.502
0.551
0.332
Burke
1.031
0.289
0.286
0.444
0.383
0.441
Cabarrus
0.704
0.604
0.649
0.363
0.508
0.322
Caldwell
1.284
0.427
0.284
0.355
0.466
0.405
Camden
2.638
0.533
0.226
0.335
0.437
0.556
Carteret
3.317
0.555
0.307
0.440
0.481
0.697
Caswell
2.756
0.526
0.127
0.380
0.347
0.394
Catawba
1.204
0.471
0.506
0.519
0.509
0.370
Chatham
2.844
0.466
0.226
0.535
0.498
0.396
Cherokee
0.338
0.066
0.227
0.367
0.540
0.456
Chowan
3.880
0.500
0.143
0.287
0.463
0.582
Clay
0.731
0.100
0.169
0.325
0.556
0.522
Cleveland
1.562
0.447
0.291
0.429
0.491
0.369
Columbus
5.948
0.436
0.124
0.546
0.529
0.488
Craven
2.412
0.532
0.383
0.512
0.436
0.616
Cumberland
0.915
0.494
0.585
0.471
0.443
0.389
Currituck
3.740
0.540
0.201
0.397
0.421
0.614
Dare
2.975
0.552
0.389
0.444
0.553
0.742
Davidson
1.850
0.568
0.317
0.457
0.466
0.366
Davie
2.659
0.533
0.191
0.387
0.536
0.353
Duplin
4.644
0.491
0.105
0.520
0.339
0.383
Durham
0.490
0.386
0.585
0.394
0.451
0.328
Edgecombe
2.948
0.488
0.150
0.375
0.425
0.438
Forsyth
0.512
0.501
0.553
0.396
0.456
0.250
Franklin
2.728
0.444
0.210
0.493
0.443
0.448
Gaston
0.856
0.537
0.686
0.534
0.455
0.316
Gates
4.175
0.486
0.129
0.318
0.486
0.532
Graham
0.933
0.171
0.179
0.328
0.426
0.513
Granville
2.302
0.494
0.217
0.416
0.443
0.421
Greene
1.669
0.496
0.222
0.332
0.402
0.435
Guilford
1.179
0.569
0.570
0.514
0.470
0.360
Halifax
4.425
0.500
0.129
0.483
0.396
0.439
Harnett
2.033
0.501
0.254
0.443
0.418
0.418
Haywood
0.919
0.136
0.202
0.440
0.559
0.446
Henderson
0.363
0.206
0.474
0.403
0.520
0.301
Hertford
3.014
0.524
0.153
0.361
0.443
0.426
Hoke
1.588
0.385
0.201
0.370
0.389
0.440
Hyde
1.570
0.435
0.441
0.305
0.587
0.662
229
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Iredell
1.029
0.577
0.582
0.471
0.500
0.325
Jackson
1.324
0.250
0.238
0.457
0.520
0.414
Johnston
2.011
0.543
0.406
0.573
0.491
0.412
Jones
3.183
0.547
0.191
0.341
0.365
0.605
Lee
0.777
0.439
0.432
0.365
0.476
0.349
Lenoir
3.006
0.595
0.184
0.363
0.465
0.427
Lincoln
0.934
0.470
0.463
0.374
0.470
0.406
Macon
0.973
0.213
0.309
0.407
0.591
0.472
Madison
0.909
0.187
0.189
0.405
0.523
0.330
Martin
4.457
0.514
0.154
0.434
0.478
0.504
McDowell
0.606
0.210
0.262
0.358
0.360
0.445
Mecklenburg
0.973
0.557
0.693
0.661
0.449
0.231
Mitchell
1.083
0.208
0.148
0.306
0.522
0.378
Montgomery
2.574
0.461
0.134
0.420
0.355
0.379
Moore
2.263
0.423
0.199
0.501
0.483
0.318
Nash
2.618
0.436
0.222
0.501
0.474
0.433
New Hanover
1.185
0.498
0.588
0.389
0.531
0.537
Northampton
5.823
0.606
0.117
0.488
0.389
0.430
Onslow
1.921
0.575
0.390
0.459
0.364
0.555
Orange
2.116
0.420
0.184
0.365
0.442
0.442
Pamlico
2.457
0.525
0.290
0.308
0.502
0.631
Pasquotank
1.874
0.539
0.289
0.309
0.448
0.529
Pender
6.660
0.579
0.126
0.471
0.494
0.499
Perquimans
3.208
0.480
0.156
0.315
0.388
0.586
Person
2.918
0.421
0.132
0.360
0.514
0.385
Pitt
2.160
0.555
0.362
0.514
0.496
0.433
Polk
1.653
0.407
0.191
0.370
0.556
0.284
Randolph
3.492
0.520
0.187
0.539
0.435
0.391
Richmond
1.319
0.362
0.195
0.419
0.354
0.366
Robeson
3.475
0.426
0.176
0.561
0.429
0.445
Rockingham
2.225
0.501
0.224
0.448
0.438
0.383
Rowan
1.515
0.535
0.326
0.436
0.457
0.351
Rutherford
1.290
0.224
0.195
0.493
0.453
0.376
Sampson
4.780
0.544
0.126
0.489
0.417
0.401
Scotland
2.232
0.472
0.172
0.398
0.386
0.405
Stanly
2.067
0.472
0.204
0.380
0.545
0.329
Stokes
2.577
0.484
0.148
0.415
0.485
0.296
Surry
1.623
0.459
0.217
0.399
0.447
0.335
Swain
0.078
0.010
0.195
0.417
0.544
0.542
Transylvania
1.953
0.329
0.221
0.397
0.609
0.431
Tyrrell
1.435
0.674
0.565
0.247
0.417
0.701
230
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Union
1.515
0.581
0.445
0.484
0.518
0.347
Vance
3.069
0.497
0.141
0.336
0.490
0.411
Wake
1.128
0.457
0.714
0.736
0.509
0.326
Warren
2.776
0.455
0.136
0.407
0.347
0.432
Washington
1.551
0.454
0.288
0.268
0.399
0.604
Watauga
1.142
0.243
0.165
0.385
0.450
0.353
Wayne
2.309
0.450
0.206
0.473
0.432
0.385
Wilkes
1.767
0.415
0.150
0.379
0.401
0.341
Wilson
1.636
0.471
0.308
0.380
0.484
0.451
Yadkin
4.462
0.548
0.112
0.404
0.469
0.370
Yancey
0.265
0.063
0.159
0.281
0.499
0.380
South Carolina
1.969
0.462
0.279
0.393
0.437
0.420
Abbeville
1.504
0.292
0.153
0.335
0.464
0.400
Aiken
1.764
0.498
0.371
0.509
0.491
0.403
Allendale
-0.074
0.505
0.192
0.182
0.268
0.390
Anderson
1.431
0.539
0.536
0.506
0.506
0.441
Bamberg
1.312
0.505
0.115
0.275
0.333
0.370
Barnwell
2.140
0.440
0.153
0.278
0.412
0.487
Beaufort
1.340
0.484
0.432
0.372
0.493
0.502
Berkeley
2.543
0.577
0.445
0.565
0.428
0.656
Calhoun
3.102
0.558
0.142
0.338
0.511
0.361
Charleston
1.980
0.587
0.586
0.553
0.508
0.611
Cherokee
0.509
0.444
0.295
0.376
0.355
0.260
Chester
1.697
0.521
0.166
0.365
0.370
0.342
Chesterfield
1.681
0.410
0.148
0.373
0.343
0.383
Clarendon
3.264
0.494
0.142
0.401
0.380
0.457
Colleton
4.613
0.537
0.150
0.419
0.496
0.488
Darlington
2.134
0.427
0.172
0.402
0.409
0.400
Dillon
2.725
0.483
0.098
0.341
0.338
0.399
Dorchester
1.670
0.611
0.458
0.409
0.489
0.489
Edgefield
2.193
0.472
0.157
0.282
0.371
0.510
Fairfield
4.046
0.551
0.109
0.376
0.469
0.357
Florence
1.593
0.381
0.323
0.466
0.501
0.456
Georgetown
3.306
0.384
0.157
0.458
0.432
0.524
Greenville
0.805
0.480
0.785
0.575
0.513
0.315
Greenwood
1.426
0.420
0.226
0.325
0.504
0.370
Hampton
2.371
0.412
0.105
0.287
0.411
0.420
Horry
1.611
0.444
0.409
0.531
0.433
0.495
Jasper
2.450
0.403
0.159
0.331
0.526
0.427
Kershaw
1.844
0.371
0.207
231
0.434
0.526
0.341
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lancaster
1.349
0.550
0.271
0.361
0.436
0.344
Laurens
1.426
0.430
0.200
0.398
0.398
0.333
Lee
1.241
0.298
0.105
0.298
0.322
0.412
Lexington
1.117
0.563
0.615
0.476
0.561
0.346
Marion
3.977
0.369
0.097
0.354
0.444
0.498
Marlboro
1.577
0.402
0.102
0.296
0.295
0.419
McCormick
2.143
0.470
0.169
0.278
0.343
0.551
Newberry
2.943
0.494
0.172
0.326
0.549
0.437
Oconee
0.867
0.283
0.360
0.411
0.459
0.449
Orangeburg
4.303
0.523
0.152
0.511
0.456
0.404
Pickens
1.055
0.427
0.420
0.427
0.491
0.380
Richland
0.811
0.455
0.694
0.502
0.515
0.360
Saluda
1.881
0.414
0.107
0.286
0.329
0.439
Spartanburg
0.697
0.482
0.747
0.521
0.483
0.302
Sumter
1.516
0.454
0.287
0.374
0.486
0.409
Union
1.576
0.409
0.140
0.363
0.353
0.358
Williamsburg
4.397
0.399
0.103
0.365
0.459
0.512
York
0.699
0.586
0.698
0.451
0.457
0.299
Tennessee
0.888
0.370
0.260
0.305
0.409
0.370
Anderson
0.307
0.259
0.392
0.308
0.437
0.319
Bedford
0.657
0.312
0.228
0.358
0.422
0.283
Benton
0.940
0.394
0.148
0.212
0.398
0.409
Bledsoe
0.408
0.372
0.161
0.258
0.352
0.324
Blount
0.340
0.235
0.626
0.346
0.498
0.408
Bradley
0.288
0.366
0.520
0.293
0.459
0.295
Campbell
0.620
0.252
0.177
0.291
0.345
0.399
Cannon
0.257
0.387
0.104
0.239
0.377
0.281
Carroll
2.032
0.503
0.174
0.324
0.420
0.413
Carter
0.532
0.138
0.174
0.277
0.418
0.451
Cheatham
2.588
0.476
0.141
0.306
0.485
0.406
Chester
1.342
0.374
0.137
0.244
0.413
0.419
Claiborne
0.624
0.271
0.164
0.305
0.319
0.382
Clay
0.980
0.522
0.130
0.225
0.297
0.432
Cocke
0.341
0.158
0.209
0.293
0.350
0.400
Coffee
1.068
0.391
0.312
0.307
0.523
0.409
Crockett
1.977
0.530
0.139
0.312
0.409
0.359
Cumberland
0.744
0.321
0.256
0.344
0.412
0.353
Davidson
0.741
0.488
0.595
0.524
0.447
0.255
Decatur
0.346
0.380
0.150
0.155
0.331
0.436
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
DeKalb
0.715
0.297
0.155
0.282
0.375
0.360
Dickson
2.568
0.391
0.136
0.358
0.512
0.379
Dyer
1.684
0.522
0.288
0.365
0.478
0.414
Fayette
2.536
0.654
0.234
0.424
0.457
0.358
Fentress
0.376
0.437
0.230
0.240
0.330
0.370
Franklin
1.055
0.290
0.207
0.355
0.436
0.386
Gibson
1.148
0.520
0.359
0.356
0.485
0.363
Giles
1.470
0.334
0.124
0.337
0.416
0.337
Grainger
0.214
0.327
0.158
0.228
0.314
0.357
Greene
0.883
0.323
0.223
0.341
0.411
0.363
Grundy
0.890
0.419
0.151
0.303
0.327
0.354
Hamblen
0.108
0.188
0.471
0.244
0.406
0.336
Hamilton
0.562
0.476
0.832
0.517
0.470
0.276
Hancock
0.208
0.290
0.133
0.259
0.318
0.317
Hardeman
1.067
0.640
0.165
0.286
0.302
0.373
Hardin
1.081
0.345
0.150
0.274
0.311
0.460
Hawkins
0.706
0.350
0.169
0.317
0.362
0.319
Haywood
1.793
0.575
0.149
0.310
0.329
0.404
Henderson
1.383
0.472
0.201
0.284
0.434
0.399
Henry
2.164
0.500
0.165
0.334
0.425
0.402
Hickman
1.092
0.305
0.150
0.234
0.460
0.410
Houston
0.552
0.545
0.203
0.170
0.425
0.373
Humphreys
2.554
0.475
0.157
0.283
0.513
0.441
Jackson
0.029
0.424
0.168
0.265
0.210
0.363
Jefferson
0.798
0.381
0.252
0.291
0.442
0.360
Johnson
1.324
0.337
0.115
0.260
0.356
0.431
Knox
0.410
0.277
0.657
0.521
0.501
0.245
Lake
0.774
0.673
0.229
0.194
0.262
0.501
Lauderdale
1.311
0.397
0.184
0.314
0.300
0.480
Lawrence
1.025
0.297
0.201
0.322
0.453
0.384
Lewis
0.063
0.311
0.130
0.218
0.299
0.349
Lincoln
0.576
0.226
0.162
0.271
0.462
0.319
Loudon
0.331
0.259
0.471
0.338
0.460
0.324
Macon
0.262
0.173
0.150
0.243
0.390
0.331
Madison
1.295
0.539
0.478
0.412
0.501
0.432
Marion
0.708
0.442
0.254
0.302
0.375
0.351
Marshall
0.582
0.283
0.201
0.304
0.437
0.302
Maury
0.967
0.314
0.264
0.366
0.500
0.347
McMinn
0.656
0.357
0.368
0.356
0.442
0.349
McNairy
2.041
0.446
0.149
0.346
0.377
0.415
Meigs
-0.220
0.379
0.213
0.150
0.286
0.372
233
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Monroe
0.483
0.249
0.384
0.319
0.379
0.469
Montgomery
0.873
0.456
0.429
0.359
0.418
0.425
Moore
1.574
0.377
0.112
0.237
0.518
0.333
Morgan
1.107
0.369
0.154
0.286
0.431
0.347
Obion
1.663
0.418
0.229
0.336
0.449
0.461
Overton
0.652
0.305
0.145
0.253
0.425
0.326
Perry
0.323
0.263
0.112
0.203
0.381
0.345
Pickett
0.173
0.333
0.157
0.102
0.413
0.406
Polk
0.930
0.266
0.241
0.316
0.389
0.503
Putnam
0.894
0.281
0.215
0.375
0.470
0.309
Rhea
0.351
0.281
0.291
0.295
0.354
0.359
Roane
0.289
0.338
0.402
0.306
0.408
0.295
Robertson
1.831
0.530
0.254
0.355
0.492
0.392
Rutherford
0.371
0.312
0.597
0.376
0.483
0.309
Scott
0.314
0.187
0.182
0.268
0.361
0.359
Sequatchie
0.317
0.428
0.198
0.268
0.319
0.327
Sevier
0.551
0.332
0.509
0.385
0.424
0.401
Shelby
0.719
0.582
0.990
0.595
0.443
0.312
Smith
0.901
0.386
0.135
0.258
0.422
0.325
Stewart
2.304
0.528
0.147
0.250
0.338
0.532
Sullivan
0.533
0.284
0.443
0.427
0.448
0.330
Sumner
0.717
0.455
0.543
0.401
0.515
0.315
Tipton
1.056
0.567
0.365
0.354
0.413
0.377
Trousdale
0.649
0.422
0.118
0.157
0.452
0.356
Unicoi
0.533
0.140
0.159
0.258
0.403
0.458
Union
0.223
0.226
0.162
0.204
0.366
0.364
Van Buren
-0.018
0.137
0.175
0.251
0.210
0.364
Warren
0.404
0.153
0.116
0.303
0.392
0.296
Washington
0.338
0.338
0.540
0.286
0.482
0.338
Wayne
0.566
0.309
0.195
0.259
0.374
0.379
Weakley
3.270
0.507
0.124
0.321
0.502
0.390
White
0.598
0.213
0.144
0.282
0.406
0.348
Williamson
0.813
0.387
0.501
0.407
0.547
0.362
Wilson
1.143
0.443
0.414
0.423
0.566
0.337
234
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 5
5.476
0.696
0.222
0.407
0.572
0.434
Illinois
4.561
0.662
0.242
0.414
0.515
0.489
Adams
5.568
0.585
0.181
0.519
0.592
0.478
Alexander
0.466
0.576
0.279
0.175
0.176
0.575
Bond
5.471
0.703
0.126
0.345
0.470
0.464
Boone
1.471
0.677
0.471
0.378
0.453
0.458
Brown
3.680
0.629
0.095
0.249
0.415
0.439
Bureau
8.620
0.751
0.172
0.531
0.604
0.560
Calhoun
4.750
0.671
0.133
0.320
0.526
0.428
Carroll
3.081
0.641
0.224
0.336
0.511
0.478
Cass
4.928
0.677
0.117
0.283
0.449
0.493
Champaign
5.572
0.693
0.240
0.655
0.467
0.516
Christian
6.446
0.668
0.182
0.433
0.610
0.569
Clark
3.559
0.523
0.141
0.353
0.446
0.466
Clay
4.927
0.608
0.126
0.344
0.509
0.448
Clinton
5.959
0.701
0.189
0.475
0.641
0.441
Coles
3.620
0.501
0.146
0.394
0.413
0.486
Cook
1.641
0.673
0.687
0.828
0.498
0.199
Crawford
2.889
0.647
0.186
0.282
0.450
0.487
Cumberland
4.937
0.653
0.152
0.375
0.487
0.484
De Witt
2.327
0.602
0.349
0.327
0.597
0.530
DeKalb
3.705
0.704
0.273
0.511
0.515
0.433
Douglas
3.546
0.590
0.222
0.372
0.580
0.489
DuPage
1.383
0.701
0.698
0.636
0.629
0.181
Edgar
6.427
0.619
0.102
0.317
0.470
0.526
Edwards
3.622
0.660
0.144
0.245
0.566
0.418
Effingham
9.995
0.711
0.127
0.462
0.676
0.498
Fayette
7.301
0.703
0.117
0.373
0.526
0.485
Ford
10.504
0.665
0.084
0.358
0.585
0.499
Franklin
4.522
0.609
0.148
0.399
0.477
0.447
Fulton
7.392
0.661
0.149
0.477
0.543
0.539
Gallatin
2.934
0.638
0.185
0.277
0.326
0.598
Greene
4.099
0.693
0.157
0.293
0.525
0.454
Grundy
3.070
0.712
0.457
0.511
0.521
0.646
Hamilton
5.025
0.648
0.118
0.285
0.511
0.469
Hancock
5.955
0.681
0.177
0.417
0.558
0.546
Hardin
3.782
0.767
0.161
0.228
0.141
0.776
Henderson
3.744
0.762
0.263
235
0.348
0.535
0.533
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Henry
3.253
0.633
0.303
0.457
0.511
0.543
Iroquois
9.831
0.679
0.105
0.527
0.544
0.425
Jackson
3.724
0.559
0.215
0.468
0.424
0.549
Jasper
7.863
0.643
0.101
0.317
0.571
0.516
Jefferson
4.515
0.690
0.151
0.392
0.482
0.404
Jersey
6.736
0.644
0.113
0.350
0.545
0.478
Jo Daviess
4.529
0.693
0.216
0.404
0.617
0.455
Johnson
4.582
0.673
0.198
0.372
0.372
0.660
Kane
1.835
0.709
0.633
0.595
0.553
0.393
Kankakee
3.498
0.638
0.282
0.486
0.500
0.515
Kendall
3.163
0.734
0.354
0.447
0.594
0.475
Knox
5.308
0.659
0.175
0.406
0.448
0.589
Lake
2.011
0.687
0.621
0.613
0.540
0.458
LaSalle
5.155
0.706
0.301
0.686
0.566
0.511
Lawrence
3.478
0.601
0.162
0.279
0.490
0.499
Lee
9.234
0.667
0.128
0.523
0.538
0.537
Livingston
10.658
0.691
0.130
0.558
0.593
0.548
Logan
4.057
0.688
0.213
0.369
0.531
0.500
Macon
3.549
0.625
0.205
0.468
0.472
0.403
Macoupin
8.944
0.691
0.132
0.511
0.619
0.460
Madison
1.722
0.706
0.696
0.660
0.497
0.391
Marion
6.510
0.746
0.141
0.404
0.501
0.475
Marshall
4.795
0.659
0.183
0.351
0.634
0.467
Mason
4.782
0.653
0.150
0.342
0.505
0.487
Massac
2.049
0.516
0.216
0.296
0.436
0.494
McDonough
5.075
0.621
0.153
0.434
0.430
0.508
McHenry
1.925
0.662
0.612
0.574
0.604
0.432
McLean
6.167
0.654
0.203
0.677
0.469
0.484
Menard
6.054
0.712
0.126
0.319
0.513
0.492
Mercer
3.641
0.674
0.287
0.351
0.684
0.517
Monroe
3.240
0.635
0.242
0.422
0.563
0.408
Montgomery
7.197
0.657
0.159
0.449
0.646
0.520
Morgan
2.881
0.662
0.285
0.407
0.559
0.429
Moultrie
8.786
0.692
0.098
0.334
0.513
0.551
Ogle
4.523
0.661
0.274
0.580
0.531
0.523
Peoria
2.090
0.650
0.437
0.424
0.522
0.508
Perry
5.875
0.675
0.142
0.354
0.607
0.446
Piatt
6.937
0.719
0.130
0.400
0.558
0.445
Pike
6.633
0.720
0.149
0.404
0.538
0.503
Pope
4.759
0.621
0.225
0.272
0.529
0.796
Pulaski
0.608
0.697
0.222
0.212
0.239
0.471
236
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Putnam
1.377
0.767
0.508
0.346
0.484
0.422
Randolph
4.504
0.617
0.199
0.434
0.650
0.414
Richland
4.979
0.705
0.116
0.290
0.458
0.465
Rock Island
2.272
0.643
0.333
0.411
0.489
0.456
Saline
3.026
0.555
0.202
0.343
0.416
0.556
Sangamon
4.220
0.654
0.286
0.618
0.555
0.452
Schuyler
5.259
0.581
0.110
0.286
0.536
0.479
Scott
4.954
0.813
0.134
0.267
0.461
0.487
Shelby
5.994
0.636
0.156
0.390
0.570
0.531
St. Clair
1.077
0.679
0.728
0.506
0.464
0.364
Stark
5.463
0.838
0.204
0.283
0.590
0.577
Stephenson
5.137
0.698
0.159
0.355
0.557
0.460
Tazewell
2.253
0.659
0.534
0.549
0.567
0.507
Union
3.742
0.489
0.227
0.356
0.604
0.652
Vermilion
3.786
0.552
0.222
0.464
0.451
0.571
Wabash
2.836
0.631
0.151
0.290
0.395
0.459
Warren
7.124
0.761
0.139
0.321
0.517
0.581
Washington
4.753
0.764
0.184
0.402
0.513
0.433
Wayne
4.119
0.640
0.151
0.342
0.537
0.409
White
2.595
0.550
0.147
0.322
0.414
0.416
Whiteside
5.791
0.641
0.153
0.420
0.471
0.545
Will
2.081
0.678
0.779
0.776
0.576
0.484
Williamson
3.088
0.609
0.328
0.461
0.557
0.545
Winnebago
1.408
0.674
0.631
0.541
0.481
0.379
Woodford
4.250
0.668
0.242
0.449
0.641
0.442
Indiana
5.153
0.659
0.219
0.360
0.570
0.452
Adams
4.932
0.607
0.118
0.315
0.545
0.427
Allen
1.474
0.600
0.587
0.531
0.554
0.381
Bartholomew
1.937
0.605
0.340
0.322
0.552
0.466
Benton
13.290
0.682
0.069
0.341
0.609
0.506
Blackford
4.177
0.666
0.111
0.228
0.559
0.402
Boone
3.272
0.714
0.318
0.414
0.565
0.506
Brown
8.511
0.546
0.095
0.348
0.585
0.572
Carroll
6.378
0.690
0.146
0.353
0.592
0.507
Cass
6.771
0.691
0.108
0.304
0.518
0.499
Clark
0.980
0.670
0.572
0.338
0.484
0.400
Clay
5.698
0.673
0.149
0.307
0.646
0.477
Clinton
7.123
0.727
0.130
0.326
0.599
0.500
Crawford
2.185
0.663
0.191
0.191
0.525
0.442
Daviess
5.299
0.594
0.160
237
0.369
0.671
0.459
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Dearborn
3.413
0.714
0.237
0.330
0.606
0.432
Decatur
5.217
0.642
0.176
0.353
0.655
0.490
DeKalb
4.572
0.698
0.197
0.404
0.553
0.458
Delaware
2.322
0.664
0.216
0.316
0.485
0.391
Dubois
4.213
0.653
0.241
0.450
0.691
0.405
Elkhart
1.449
0.750
0.572
0.430
0.485
0.408
Fayette
4.939
0.712
0.106
0.240
0.501
0.451
Floyd
0.681
0.637
0.539
0.286
0.538
0.309
Fountain
8.493
0.688
0.080
0.294
0.500
0.497
Franklin
7.832
0.722
0.126
0.311
0.693
0.478
Fulton
8.504
0.545
0.089
0.329
0.653
0.501
Gibson
3.268
0.620
0.228
0.384
0.582
0.421
Grant
2.948
0.634
0.200
0.377
0.506
0.378
Greene
6.208
0.542
0.115
0.379
0.558
0.494
Hamilton
1.517
0.604
0.453
0.454
0.577
0.323
Hancock
2.055
0.686
0.406
0.376
0.616
0.408
Harrison
4.408
0.601
0.135
0.353
0.537
0.405
Hendricks
2.110
0.685
0.477
0.475
0.583
0.430
Henry
5.095
0.731
0.146
0.324
0.562
0.427
Howard
1.823
0.647
0.268
0.249
0.525
0.430
Huntington
7.416
0.654
0.121
0.396
0.611
0.452
Jackson
4.188
0.607
0.189
0.355
0.601
0.479
Jasper
11.749
0.716
0.102
0.479
0.612
0.484
Jay
4.631
0.705
0.118
0.302
0.529
0.378
Jefferson
6.849
0.729
0.133
0.335
0.539
0.526
Jennings
5.770
0.560
0.113
0.337
0.558
0.476
Johnson
1.749
0.659
0.513
0.387
0.592
0.475
Knox
3.804
0.625
0.205
0.383
0.570
0.449
Kosciusko
4.578
0.740
0.251
0.468
0.548
0.499
LaGrange
7.024
0.552
0.097
0.411
0.515
0.459
Lake
1.023
0.678
0.777
0.602
0.467
0.265
La Porte
2.861
0.772
0.296
0.462
0.504
0.356
Lawrence
6.492
0.652
0.131
0.367
0.606
0.463
Madison
2.851
0.669
0.218
0.346
0.524
0.398
Marion
1.009
0.625
0.723
0.667
0.444
0.210
Marshall
5.834
0.599
0.164
0.440
0.615
0.497
Martin
5.946
0.577
0.129
0.300
0.576
0.571
Miami
6.795
0.743
0.115
0.283
0.563
0.485
Monroe
2.219
0.614
0.361
0.417
0.439
0.542
Montgomery
9.013
0.667
0.109
0.392
0.630
0.483
Morgan
4.860
0.664
0.213
0.465
0.566
0.493
238
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Newton
14.854
0.784
0.070
0.372
0.556
0.508
Noble
4.860
0.671
0.163
0.377
0.480
0.503
Ohio
4.407
0.573
0.124
0.241
0.620
0.446
Orange
4.994
0.530
0.126
0.287
0.654
0.464
Owen
3.747
0.579
0.181
0.331
0.554
0.489
Parke
9.701
0.639
0.080
0.326
0.573
0.495
Perry
5.300
0.727
0.163
0.269
0.600
0.526
Pike
5.840
0.674
0.132
0.327
0.571
0.468
Porter
1.652
0.685
0.615
0.544
0.564
0.377
Posey
4.324
0.737
0.198
0.343
0.602
0.434
Pulaski
12.135
0.669
0.074
0.342
0.647
0.470
Putnam
8.358
0.620
0.116
0.406
0.618
0.514
Randolph
7.661
0.648
0.093
0.311
0.575
0.465
Ripley
8.659
0.772
0.157
0.348
0.780
0.527
Rush
6.921
0.696
0.143
0.296
0.743
0.478
Scott
3.054
0.696
0.161
0.233
0.497
0.452
Shelby
4.544
0.683
0.186
0.323
0.599
0.487
Spencer
5.657
0.672
0.167
0.375
0.655
0.455
St. Joseph
1.575
0.673
0.513
0.480
0.473
0.403
Starke
5.839
0.706
0.105
0.344
0.469
0.418
Steuben
5.484
0.646
0.158
0.404
0.599
0.443
Sullivan
7.415
0.636
0.112
0.391
0.574
0.460
Switzerland
4.118
0.642
0.118
0.295
0.460
0.435
Tippecanoe
2.321
0.589
0.344
0.469
0.501
0.457
Tipton
10.332
0.616
0.079
0.318
0.696
0.454
Union
5.558
0.681
0.094
0.244
0.465
0.491
Vanderburgh
0.556
0.603
0.752
0.278
0.549
0.355
Vermillion
3.657
0.684
0.181
0.288
0.537
0.467
Vigo
2.009
0.622
0.332
0.357
0.525
0.442
Wabash
9.771
0.670
0.099
0.365
0.667
0.475
Warren
8.347
0.689
0.102
0.310
0.541
0.546
Warrick
2.817
0.672
0.324
0.404
0.632
0.423
Washington
5.500
0.637
0.119
0.313
0.571
0.433
Wayne
3.372
0.664
0.184
0.319
0.534
0.422
Wells
5.983
0.674
0.129
0.308
0.611
0.457
White
8.785
0.670
0.120
0.447
0.587
0.502
Whitley
4.188
0.694
0.183
0.324
0.573
0.455
Michigan
5.939
0.705
0.177
0.412
0.492
0.418
Alcona
7.999
0.702
0.086
0.347
0.474
0.457
Alger
6.418
0.583
0.104
0.301
0.602
0.473
239
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Allegan
6.546
0.650
0.135
0.510
0.555
0.372
Alpena
6.390
0.729
0.131
0.354
0.516
0.485
Antrim
5.528
0.728
0.129
0.387
0.499
0.395
Arenac
10.083
0.732
0.066
0.369
0.492
0.389
Baraga
3.106
0.619
0.255
0.380
0.555
0.480
Barry
10.447
0.670
0.068
0.447
0.487
0.372
Bay
3.440
0.690
0.186
0.408
0.506
0.345
Benzie
6.497
0.699
0.106
0.365
0.566
0.366
Berrien
3.265
0.708
0.216
0.513
0.375
0.363
Branch
4.145
0.662
0.133
0.373
0.414
0.417
Calhoun
2.384
0.742
0.250
0.384
0.425
0.385
Cass
2.771
0.723
0.187
0.363
0.358
0.426
Charlevoix
5.438
0.745
0.138
0.384
0.594
0.333
Cheboygan
10.002
0.686
0.095
0.447
0.561
0.446
Chippewa
9.257
0.762
0.098
0.490
0.480
0.381
Clare
8.214
0.839
0.082
0.368
0.427
0.401
Clinton
5.429
0.695
0.165
0.431
0.600
0.390
Crawford
6.001
0.567
0.133
0.344
0.442
0.660
Delta
10.163
0.735
0.105
0.445
0.603
0.440
Dickinson
6.106
0.682
0.203
0.418
0.691
0.546
Eaton
3.769
0.756
0.234
0.431
0.533
0.395
Emmet
8.298
0.706
0.141
0.410
0.649
0.523
Genesee
0.921
0.591
0.511
0.438
0.415
0.331
Gladwin
9.357
0.588
0.065
0.378
0.421
0.485
Gogebic
5.478
0.666
0.125
0.270
0.532
0.516
Grand Traverse
4.157
0.692
0.205
0.407
0.603
0.393
Gratiot
6.946
0.832
0.114
0.406
0.466
0.386
Hillsdale
5.464
0.753
0.084
0.311
0.441
0.370
Houghton
3.939
0.660
0.164
0.335
0.533
0.423
Huron
13.117
0.792
0.082
0.513
0.594
0.335
Ingham
1.908
0.669
0.328
0.442
0.480
0.332
Ionia
4.796
0.695
0.132
0.371
0.481
0.398
Iosco
5.399
0.700
0.131
0.349
0.497
0.450
Iron
5.873
0.701
0.169
0.330
0.653
0.510
Isabella
8.512
0.754
0.114
0.446
0.416
0.522
Jackson
3.626
0.685
0.161
0.367
0.484
0.376
Kalamazoo
1.547
0.659
0.410
0.423
0.473
0.369
Kalkaska
14.188
0.846
0.073
0.384
0.425
0.558
Kent
2.224
0.682
0.450
0.622
0.523
0.319
Keweenaw
1.809
0.824
0.222
0.311
0.279
0.451
Lake
7.407
0.708
0.109
0.341
0.444
0.553
240
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lapeer
5.369
0.707
0.136
0.383
0.513
0.407
Leelanau
3.669
0.770
0.243
0.384
0.622
0.371
Lenawee
4.896
0.758
0.168
0.424
0.497
0.398
Livingston
2.487
0.728
0.374
0.446
0.617
0.358
Luce
2.443
0.749
0.226
0.280
0.389
0.500
Mackinac
8.331
0.773
0.103
0.423
0.513
0.394
Macomb
1.179
0.674
0.587
0.520
0.501
0.267
Manistee
4.463
0.693
0.144
0.328
0.522
0.418
Marquette
4.201
0.613
0.276
0.609
0.593
0.448
Mason
5.944
0.782
0.130
0.347
0.520
0.424
Mecosta
8.961
0.671
0.070
0.398
0.431
0.417
Menominee
6.193
0.805
0.099
0.376
0.425
0.375
Midland
4.703
0.685
0.137
0.356
0.527
0.389
Missaukee
11.971
0.770
0.073
0.368
0.471
0.502
Monroe
1.382
0.665
0.361
0.455
0.488
0.235
Montcalm
8.655
0.786
0.086
0.432
0.434
0.382
Montmorency
8.142
0.700
0.092
0.365
0.414
0.524
Muskegon
2.383
0.631
0.194
0.356
0.430
0.382
Newaygo
11.919
0.728
0.075
0.417
0.445
0.501
Oakland
1.845
0.648
0.557
0.723
0.523
0.256
Oceana
13.909
0.677
0.046
0.390
0.511
0.368
Ogemaw
5.104
0.715
0.150
0.363
0.452
0.493
Ontonagon
2.868
0.658
0.237
0.270
0.582
0.476
Osceola
10.986
0.790
0.073
0.409
0.448
0.424
Oscoda
4.737
0.618
0.123
0.351
0.317
0.563
Otsego
8.667
0.714
0.110
0.379
0.562
0.499
Ottawa
4.399
0.732
0.247
0.584
0.588
0.314
Presque Isle
4.783
0.718
0.129
0.355
0.436
0.430
Roscommon
9.028
0.677
0.090
0.388
0.395
0.567
Saginaw
4.168
0.627
0.180
0.510
0.439
0.396
Sanilac
12.192
0.773
0.083
0.467
0.451
0.477
Schoolcraft
6.955
0.687
0.121
0.350
0.496
0.535
Shiawassee
5.805
0.739
0.120
0.373
0.515
0.380
St. Clair
5.042
0.692
0.188
0.541
0.480
0.403
St. Joseph
3.664
0.708
0.162
0.403
0.376
0.416
Tuscola
9.652
0.680
0.093
0.490
0.509
0.415
Van Buren
2.904
0.617
0.162
0.425
0.453
0.301
Washtenaw
2.234
0.708
0.405
0.546
0.477
0.360
Wayne
0.941
0.663
0.608
0.651
0.373
0.160
Wexford
7.379
0.705
0.135
0.414
0.565
0.486
241
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Minnesota
7.125
0.772
0.220
0.389
0.735
0.442
Aitkin
9.811
0.798
0.126
0.352
0.696
0.508
Anoka
1.080
0.700
0.655
0.358
0.673
0.299
Becker
11.035
0.804
0.154
0.455
0.769
0.567
Beltrami
8.976
0.711
0.172
0.515
0.654
0.616
Benton
4.470
0.794
0.208
0.343
0.745
0.323
Big Stone
8.121
0.835
0.096
0.187
0.649
0.472
Blue Earth
4.504
0.737
0.285
0.462
0.743
0.427
Brown
7.449
0.711
0.142
0.358
0.822
0.375
Carlton
6.576
0.748
0.190
0.444
0.673
0.473
Carver
2.275
0.736
0.480
0.429
0.688
0.403
Cass
11.914
0.812
0.134
0.468
0.631
0.599
Chippewa
6.123
0.873
0.207
0.273
0.815
0.456
Chisago
5.958
0.756
0.201
0.421
0.724
0.421
Clay
7.735
0.689
0.157
0.531
0.661
0.424
Clearwater
8.179
0.862
0.140
0.238
0.622
0.601
Cook
10.545
0.740
0.120
0.318
0.913
0.434
Cottonwood
3.482
0.707
0.273
0.339
0.799
0.354
Crow Wing
6.314
0.738
0.175
0.420
0.703
0.406
Dakota
1.671
0.664
0.672
0.552
0.632
0.397
Dodge
8.997
0.774
0.143
0.353
0.823
0.449
Douglas
6.746
0.771
0.199
0.376
0.836
0.445
Faribault
9.614
0.763
0.147
0.400
0.830
0.473
Fillmore
13.448
0.864
0.131
0.362
0.832
0.588
Freeborn
3.353
0.766
0.327
0.353
0.695
0.458
Goodhue
7.162
0.695
0.169
0.424
0.753
0.460
Grant
12.032
0.752
0.104
0.361
0.886
0.391
Hennepin
1.475
0.665
0.748
0.692
0.607
0.254
Houston
8.833
0.779
0.169
0.384
0.723
0.599
Hubbard
8.164
0.766
0.120
0.298
0.729
0.427
Isanti
5.363
0.755
0.160
0.347
0.672
0.363
Itasca
16.343
0.745
0.106
0.534
0.692
0.630
Jackson
8.388
0.758
0.185
0.442
0.826
0.508
Kanabec
6.329
0.840
0.140
0.260
0.733
0.375
Kandiyohi
6.682
0.782
0.204
0.462
0.739
0.429
Kittson
7.940
0.751
0.136
0.386
0.724
0.402
Koochiching
13.669
0.697
0.099
0.429
0.628
0.635
Lac qui Parle
5.516
0.846
0.233
0.272
0.847
0.458
Lake
10.333
0.645
0.121
0.359
0.699
0.653
Lake of the Woods
8.091
0.701
0.162
0.249
0.848
0.628
Le Sueur
4.655
0.834
0.241
0.317
0.754
0.415
242
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lincoln
19.250
0.862
0.076
0.351
0.935
0.376
Lyon
10.965
0.761
0.116
0.455
0.846
0.324
Mahnomen
5.220
0.817
0.236
0.319
0.380
0.778
Marshall
11.660
0.882
0.136
0.486
0.710
0.449
Martin
9.469
0.802
0.144
0.392
0.765
0.469
McLeod
6.964
0.756
0.177
0.373
0.788
0.441
Meeker
5.382
0.869
0.229
0.334
0.776
0.409
Mille Lacs
7.765
0.771
0.144
0.351
0.776
0.404
Morrison
12.485
0.832
0.111
0.408
0.794
0.414
Mower
10.109
0.755
0.117
0.457
0.640
0.446
Murray
8.648
0.837
0.142
0.345
0.810
0.389
Nicollet
3.893
0.721
0.232
0.341
0.682
0.407
Nobles
5.500
0.809
0.231
0.460
0.681
0.410
Norman
5.217
0.830
0.220
0.340
0.744
0.413
Olmsted
2.727
0.688
0.423
0.505
0.667
0.414
Otter Tail
7.984
0.827
0.195
0.562
0.754
0.366
Pennington
8.151
0.739
0.129
0.348
0.738
0.427
Pine
9.063
0.781
0.126
0.362
0.705
0.452
Pipestone
13.755
0.689
0.078
0.391
0.786
0.396
Polk
9.813
0.751
0.153
0.599
0.704
0.414
Pope
6.228
0.843
0.176
0.273
0.791
0.414
Ramsey
0.807
0.666
0.621
0.448
0.607
0.149
Red Lake
5.106
0.769
0.152
0.313
0.597
0.407
Redwood
9.590
0.852
0.144
0.351
0.825
0.431
Renville
6.358
0.936
0.238
0.342
0.853
0.419
Rice
3.250
0.706
0.303
0.392
0.692
0.404
Rock
5.375
0.768
0.187
0.370
0.670
0.410
Roseau
5.202
0.773
0.278
0.441
0.794
0.462
Scott
2.059
0.729
0.504
0.421
0.666
0.404
Sherburne
2.200
0.689
0.472
0.411
0.770
0.366
Sibley
6.258
0.767
0.187
0.333
0.795
0.438
St. Louis
10.592
0.671
0.165
0.861
0.607
0.453
Stearns
5.040
0.774
0.314
0.587
0.746
0.412
Steele
2.559
0.712
0.389
0.376
0.708
0.408
Stevens
5.011
0.759
0.229
0.322
0.843
0.405
Swift
5.064
0.766
0.213
0.310
0.783
0.426
Todd
4.762
0.781
0.161
0.288
0.657
0.375
Traverse
2.327
0.771
0.162
0.093
0.645
0.397
Wabasha
9.016
0.751
0.154
0.320
0.792
0.584
Wadena
4.373
0.811
0.215
0.279
0.726
0.401
Waseca
6.830
0.766
0.152
0.315
0.746
0.429
243
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Washington
1.849
0.711
0.536
0.426
0.683
0.373
Watonwan
4.507
0.831
0.231
0.341
0.690
0.401
Wilkin
11.916
0.933
0.134
0.368
0.870
0.415
Winona
9.077
0.764
0.149
0.398
0.628
0.598
Wright
3.048
0.758
0.455
0.469
0.762
0.440
Yellow Medicine
8.103
0.927
0.185
0.336
0.818
0.454
Ohio
3.446
0.651
0.246
0.421
0.514
0.352
Adams
3.033
0.598
0.129
0.365
0.387
0.375
Allen
2.404
0.650
0.271
0.405
0.554
0.339
Ashland
5.372
0.637
0.134
0.421
0.540
0.385
Ashtabula
2.435
0.613
0.220
0.427
0.466
0.335
Athens
2.194
0.577
0.193
0.436
0.388
0.329
Auglaize
5.451
0.645
0.143
0.431
0.583
0.373
Belmont
3.083
0.631
0.177
0.403
0.504
0.325
Brown
3.342
0.632
0.147
0.358
0.477
0.362
Butler
1.108
0.662
0.625
0.526
0.508
0.262
Carroll
4.409
0.627
0.115
0.354
0.549
0.319
Champaign
6.413
0.643
0.111
0.396
0.477
0.453
Clark
1.573
0.649
0.313
0.321
0.482
0.389
Clermont
1.358
0.631
0.346
0.352
0.524
0.317
Clinton
1.814
0.576
0.223
0.365
0.463
0.333
Columbiana
2.248
0.675
0.237
0.379
0.506
0.320
Coshocton
4.046
0.660
0.130
0.370
0.495
0.341
Crawford
4.891
0.703
0.163
0.357
0.554
0.443
Cuyahoga
1.704
0.627
0.502
0.710
0.490
0.207
Darke
5.336
0.687
0.171
0.413
0.591
0.432
Defiance
5.939
0.632
0.120
0.368
0.623
0.374
Delaware
2.277
0.691
0.418
0.457
0.522
0.461
Erie
2.580
0.664
0.228
0.413
0.507
0.321
Fairfield
2.505
0.661
0.330
0.457
0.517
0.413
Fayette
3.316
0.641
0.194
0.386
0.510
0.396
Franklin
1.276
0.635
0.662
0.671
0.463
0.256
Fulton
4.898
0.679
0.170
0.424
0.669
0.320
Gallia
1.810
0.574
0.170
0.372
0.351
0.347
Geauga
3.522
0.599
0.262
0.481
0.612
0.428
Greene
1.280
0.651
0.480
0.412
0.460
0.383
Guernsey
4.330
0.709
0.158
0.433
0.513
0.325
Hamilton
0.924
0.609
0.557
0.571
0.502
0.137
Hancock
3.282
0.673
0.249
0.455
0.545
0.379
Hardin
9.517
0.726
0.083
244
0.355
0.522
0.453
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Harrison
4.509
0.664
0.090
0.349
0.477
0.303
Henry
8.193
0.774
0.116
0.393
0.641
0.376
Highland
4.425
0.673
0.114
0.292
0.552
0.360
Hocking
4.345
0.621
0.132
0.370
0.529
0.366
Holmes
5.595
0.650
0.123
0.395
0.656
0.293
Huron
6.240
0.673
0.141
0.447
0.539
0.430
Jackson
2.868
0.646
0.150
0.362
0.461
0.323
Jefferson
1.610
0.625
0.221
0.381
0.443
0.277
Knox
6.219
0.702
0.108
0.398
0.520
0.356
Lake
1.812
0.642
0.302
0.361
0.587
0.300
Lawrence
1.489
0.646
0.286
0.384
0.365
0.374
Licking
3.253
0.688
0.266
0.460
0.528
0.404
Logan
5.960
0.623
0.117
0.429
0.548
0.366
Lorain
1.291
0.649
0.519
0.498
0.492
0.300
Lucas
0.847
0.640
0.521
0.483
0.476
0.190
Madison
5.194
0.638
0.139
0.392
0.516
0.434
Mahoning
1.859
0.668
0.368
0.460
0.525
0.312
Marion
2.370
0.560
0.213
0.341
0.457
0.445
Medina
1.773
0.637
0.444
0.424
0.614
0.365
Meigs
1.163
0.612
0.204
0.334
0.351
0.328
Mercer
10.150
0.719
0.103
0.406
0.681
0.422
Miami
1.786
0.653
0.365
0.354
0.567
0.384
Monroe
3.722
0.692
0.123
0.342
0.475
0.333
Montgomery
0.996
0.608
0.666
0.544
0.479
0.284
Morgan
2.179
0.623
0.161
0.333
0.474
0.303
Morrow
5.430
0.612
0.094
0.337
0.481
0.402
Muskingum
2.505
0.674
0.212
0.389
0.492
0.318
Noble
2.780
0.588
0.125
0.334
0.476
0.311
Ottawa
2.432
0.645
0.225
0.375
0.579
0.289
Paulding
5.471
0.670
0.119
0.425
0.493
0.356
Perry
3.094
0.681
0.163
0.420
0.393
0.345
Pickaway
4.060
0.642
0.212
0.480
0.508
0.432
Pike
3.475
0.701
0.155
0.395
0.425
0.359
Portage
2.096
0.683
0.417
0.476
0.508
0.412
Preble
4.379
0.627
0.131
0.374
0.448
0.423
Putnam
6.349
0.757
0.159
0.423
0.723
0.318
Richland
3.475
0.626
0.231
0.489
0.567
0.351
Ross
3.538
0.591
0.167
0.443
0.432
0.394
Sandusky
3.326
0.562
0.188
0.438
0.573
0.332
Scioto
2.442
0.597
0.198
0.448
0.372
0.360
Seneca
4.981
0.778
0.180
0.397
0.565
0.401
245
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Shelby
3.975
0.659
0.161
0.377
0.564
0.350
Stark
1.467
0.612
0.497
0.548
0.545
0.268
Summit
1.045
0.647
0.633
0.514
0.543
0.238
Trumbull
2.578
0.662
0.310
0.491
0.468
0.398
Tuscarawas
2.931
0.665
0.260
0.490
0.558
0.301
Union
3.947
0.672
0.198
0.429
0.527
0.402
Van Wert
2.346
0.711
0.270
0.336
0.531
0.388
Vinton
2.068
0.610
0.162
0.333
0.363
0.385
Warren
1.007
0.644
0.644
0.408
0.529
0.357
Washington
3.024
0.619
0.208
0.469
0.484
0.332
Wayne
4.164
0.657
0.260
0.550
0.635
0.382
Williams
6.116
0.684
0.138
0.468
0.535
0.379
Wood
2.952
0.687
0.354
0.594
0.529
0.367
Wyandot
6.245
0.723
0.133
0.391
0.555
0.413
Wisconsin
7.140
0.746
0.220
0.457
0.623
0.441
Adams
4.533
0.672
0.161
0.423
0.506
0.394
Ashland
11.999
0.802
0.107
0.364
0.737
0.484
Barron
7.481
0.796
0.173
0.429
0.704
0.449
Bayfield
8.767
0.762
0.113
0.309
0.640
0.497
Brown
2.072
0.735
0.587
0.599
0.608
0.351
Buffalo
8.072
0.863
0.119
0.365
0.599
0.393
Burnett
2.454
0.727
0.325
0.253
0.582
0.520
Calumet
2.894
0.691
0.350
0.443
0.623
0.431
Chippewa
5.956
0.697
0.210
0.576
0.624
0.420
Clark
10.816
0.590
0.102
0.491
0.668
0.508
Columbia
4.530
0.758
0.309
0.578
0.653
0.417
Crawford
7.014
0.800
0.138
0.376
0.641
0.384
Dane
2.727
0.673
0.572
0.803
0.623
0.388
Dodge
6.097
0.720
0.236
0.603
0.624
0.475
Door
11.358
0.681
0.107
0.507
0.732
0.407
Douglas
5.774
0.785
0.183
0.409
0.570
0.461
Dunn
10.202
0.810
0.123
0.519
0.630
0.377
Eau Claire
3.966
0.754
0.264
0.470
0.590
0.396
Florence
16.089
0.954
0.081
0.347
0.472
0.617
Fond du Lac
5.726
0.737
0.237
0.538
0.647
0.463
Forest
13.330
0.779
0.098
0.359
0.613
0.616
Grant
10.607
0.770
0.151
0.663
0.679
0.397
Green
4.055
0.765
0.248
0.424
0.667
0.354
Green Lake
7.829
0.789
0.134
0.345
0.689
0.430
Iowa
10.844
0.828
0.115
0.475
0.699
0.353
246
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Iron
11.109
0.784
0.094
0.321
0.661
0.480
Jackson
11.614
0.767
0.094
0.408
0.599
0.470
Jefferson
4.327
0.730
0.297
0.525
0.636
0.452
Juneau
6.166
0.654
0.158
0.464
0.570
0.460
Kenosha
1.409
0.627
0.472
0.421
0.477
0.407
Kewaunee
7.562
0.700
0.140
0.373
0.698
0.465
La Crosse
2.458
0.656
0.377
0.455
0.600
0.416
Lafayette
9.550
0.782
0.117
0.425
0.713
0.366
Langlade
9.330
0.735
0.116
0.330
0.655
0.533
Lincoln
5.770
0.748
0.209
0.400
0.672
0.497
Manitowoc
3.555
0.746
0.324
0.487
0.628
0.413
Marathon
5.490
0.621
0.243
0.703
0.615
0.432
Marinette
9.129
0.672
0.124
0.583
0.560
0.422
Marquette
9.450
0.787
0.115
0.422
0.655
0.393
Menominee
2.719
0.700
0.269
0.331
0.193
0.725
Milwaukee
2.410
0.666
0.363
0.564
0.467
0.363
Monroe
7.830
0.770
0.156
0.511
0.616
0.411
Oconto
9.305
0.832
0.137
0.491
0.634
0.398
Oneida
14.162
0.703
0.096
0.498
0.691
0.509
Outagamie
2.625
0.717
0.558
0.603
0.690
0.438
Ozaukee
3.323
0.664
0.266
0.347
0.646
0.463
Pepin
5.268
0.981
0.171
0.295
0.592
0.395
Pierce
4.407
0.759
0.248
0.423
0.678
0.399
Polk
11.957
0.799
0.114
0.416
0.729
0.473
Portage
6.917
0.783
0.164
0.499
0.618
0.367
Price
13.610
0.714
0.092
0.409
0.735
0.499
Racine
1.644
0.666
0.443
0.374
0.543
0.419
Richland
9.231
0.701
0.086
0.382
0.610
0.375
Rock
3.392
0.699
0.320
0.566
0.516
0.419
Rusk
4.822
0.747
0.185
0.340
0.568
0.477
Sauk
7.189
0.726
0.174
0.566
0.659
0.375
Sawyer
14.673
0.825
0.080
0.308
0.586
0.593
Shawano
13.389
0.747
0.091
0.503
0.639
0.418
Sheboygan
4.710
0.711
0.227
0.460
0.616
0.434
St. Croix
4.311
0.761
0.312
0.520
0.673
0.431
Taylor
11.802
0.834
0.098
0.385
0.628
0.460
Trempealeau
10.594
0.840
0.113
0.476
0.594
0.401
Vernon
8.075
0.762
0.119
0.408
0.643
0.371
Vilas
13.874
0.810
0.113
0.436
0.611
0.640
Walworth
2.856
0.683
0.370
0.510
0.519
0.474
Washburn
13.514
0.829
0.086
0.324
0.663
0.500
247
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Washington
2.180
0.674
0.494
0.463
0.706
0.398
Waukesha
1.899
0.701
0.609
0.522
0.671
0.381
Waupaca
8.698
0.790
0.152
0.509
0.726
0.365
Waushara
4.208
0.793
0.247
0.454
0.606
0.370
Winnebago
1.730
0.669
0.535
0.460
0.578
0.416
Wood
4.618
0.698
0.258
0.540
0.631
0.417
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 6
3.060
0.584
0.239
0.394
0.474
0.422
Arkansas
2.668
0.533
0.235
0.393
0.451
0.445
Arkansas
3.820
0.577
0.216
0.395
0.566
0.513
Ashley
4.147
0.533
0.139
0.346
0.527
0.456
Baxter
3.380
0.489
0.180
0.433
0.455
0.486
Benton
1.568
0.524
0.596
0.710
0.466
0.396
Boone
3.461
0.512
0.171
0.383
0.585
0.401
Bradley
2.627
0.409
0.123
0.309
0.416
0.468
Calhoun
3.076
0.609
0.150
0.310
0.379
0.483
Carroll
4.470
0.498
0.114
0.482
0.484
0.319
Chicot
1.746
0.560
0.165
0.267
0.387
0.424
Clark
4.836
0.495
0.134
0.407
0.546
0.469
Clay
2.893
0.594
0.164
0.366
0.424
0.402
Cleburne
1.799
0.600
0.352
0.421
0.490
0.395
Cleveland
2.031
0.528
0.119
0.301
0.329
0.412
Columbia
2.736
0.559
0.229
0.384
0.494
0.457
Conway
2.380
0.648
0.259
0.357
0.516
0.401
Craighead
2.665
0.532
0.281
0.542
0.487
0.410
Crawford
4.860
0.557
0.177
0.519
0.474
0.499
Crittenden
1.538
0.676
0.469
0.427
0.419
0.449
Cross
4.306
0.673
0.182
0.375
0.558
0.435
Dallas
1.906
0.429
0.140
0.257
0.487
0.398
Desha
1.788
0.605
0.232
0.307
0.379
0.457
Drew
3.640
0.567
0.114
0.351
0.385
0.424
Faulkner
1.464
0.628
0.505
0.461
0.494
0.398
Franklin
2.781
0.494
0.214
0.392
0.411
0.551
Fulton
1.716
0.429
0.185
0.360
0.438
0.374
Garland
1.428
0.460
0.345
0.426
0.441
0.435
Grant
4.859
0.621
0.123
248
0.394
0.422
0.440
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Greene
1.799
0.589
0.274
0.416
0.429
0.360
Hempstead
1.664
0.451
0.308
0.468
0.397
0.451
Hot Spring
3.079
0.471
0.143
0.356
0.487
0.419
Howard
1.219
0.468
0.299
0.362
0.389
0.427
Independence
2.508
0.499
0.199
0.461
0.460
0.354
Izard
1.407
0.445
0.266
0.379
0.485
0.356
Jackson
2.143
0.567
0.278
0.398
0.458
0.444
Jefferson
4.122
0.539
0.161
0.447
0.442
0.475
Johnson
3.113
0.573
0.233
0.387
0.373
0.611
Lafayette
1.482
0.666
0.251
0.238
0.406
0.458
Lawrence
2.250
0.520
0.185
0.368
0.413
0.410
Lee
1.702
0.536
0.142
0.215
0.378
0.461
Lincoln
2.312
0.473
0.127
0.286
0.369
0.460
Little River
1.970
0.520
0.260
0.364
0.406
0.495
Logan
4.224
0.556
0.171
0.402
0.534
0.481
Lonoke
2.929
0.603
0.319
0.536
0.511
0.455
Madison
2.536
0.457
0.128
0.359
0.392
0.399
Marion
1.686
0.430
0.225
0.380
0.442
0.408
Miller
1.076
0.451
0.389
0.324
0.467
0.466
Mississippi
4.030
0.596
0.172
0.492
0.450
0.394
Monroe
3.072
0.651
0.230
0.324
0.511
0.497
Montgomery
3.243
0.452
0.145
0.324
0.412
0.555
Nevada
1.641
0.472
0.208
0.299
0.461
0.416
Newton
1.714
0.508
0.258
0.355
0.329
0.521
Ouachita
2.953
0.484
0.141
0.315
0.465
0.450
Perry
4.582
0.690
0.175
0.352
0.503
0.504
Phillips
2.083
0.543
0.131
0.279
0.307
0.471
Pike
4.136
0.564
0.144
0.367
0.471
0.469
Poinsett
4.563
0.623
0.126
0.404
0.419
0.416
Polk
1.360
0.333
0.323
0.372
0.540
0.516
Pope
4.192
0.545
0.177
0.450
0.470
0.504
Prairie
3.033
0.658
0.244
0.365
0.514
0.464
Pulaski
1.185
0.546
0.880
0.726
0.536
0.374
Randolph
3.596
0.551
0.144
0.375
0.500
0.390
Saline
2.045
0.581
0.419
0.485
0.521
0.471
Scott
5.446
0.601
0.151
0.349
0.511
0.584
Searcy
1.008
0.487
0.249
0.338
0.356
0.373
Sebastian
1.942
0.564
0.353
0.457
0.452
0.451
Sevier
0.562
0.203
0.263
0.341
0.326
0.480
Sharp
2.093
0.433
0.206
0.380
0.554
0.364
St. Francis
5.139
0.624
0.135
0.378
0.479
0.473
249
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Stone
0.986
0.288
0.247
0.356
0.441
0.419
Union
2.235
0.558
0.338
0.452
0.522
0.458
Van Buren
1.616
0.583
0.331
0.400
0.458
0.387
Washington
2.490
0.513
0.322
0.668
0.458
0.359
White
2.804
0.615
0.292
0.515
0.463
0.427
Woodruff
1.573
0.623
0.219
0.310
0.263
0.492
Yell
3.653
0.503
0.179
0.414
0.423
0.556
Louisiana
2.501
0.570
0.338
0.430
0.479
0.457
Acadia
2.915
0.535
0.271
0.493
0.480
0.496
Allen
2.317
0.549
0.295
0.372
0.470
0.540
Ascension
0.889
0.584
0.907
0.461
0.558
0.429
Assumption
1.627
0.649
0.354
0.331
0.439
0.465
Avoyelles
2.668
0.617
0.316
0.445
0.472
0.508
Beauregard
2.922
0.614
0.261
0.442
0.448
0.482
Bienville
1.799
0.553
0.259
0.356
0.408
0.445
Bossier
1.484
0.568
0.477
0.446
0.474
0.459
Caddo
1.763
0.550
0.611
0.686
0.532
0.442
Calcasieu
2.502
0.510
0.467
0.770
0.557
0.465
Caldwell
3.485
0.532
0.129
0.350
0.395
0.464
Cameron
1.388
0.583
0.503
0.386
0.516
0.470
Catahoula
1.972
0.530
0.149
0.267
0.318
0.496
Claiborne
1.022
0.514
0.228
0.230
0.408
0.422
Concordia
3.010
0.518
0.160
0.342
0.374
0.523
De Soto
4.545
0.638
0.183
0.468
0.475
0.459
East Baton Rouge
1.325
0.553
0.666
0.589
0.588
0.354
East Carroll
0.897
0.543
0.293
0.210
0.377
0.482
East Feliciana
1.270
0.566
0.265
0.364
0.363
0.371
Evangeline
2.070
0.554
0.257
0.359
0.421
0.480
Franklin
4.685
0.553
0.125
0.348
0.504
0.462
Grant
6.240
0.560
0.132
0.452
0.426
0.579
Iberia
1.417
0.506
0.360
0.399
0.454
0.429
Iberville
1.600
0.605
0.524
0.427
0.584
0.448
Jackson
1.888
0.515
0.244
0.318
0.521
0.416
Jefferson
1.347
0.632
0.626
0.483
0.578
0.371
Jefferson Davis
1.949
0.508
0.314
0.403
0.477
0.485
La Salle
5.079
0.681
0.156
0.392
0.487
0.472
Lafayette
1.564
0.564
0.655
0.596
0.588
0.437
Lafourche
1.601
0.538
0.442
0.521
0.484
0.397
Lincoln
2.068
0.536
0.255
0.432
0.400
0.426
Livingston
1.392
0.553
0.515
250
0.495
0.476
0.423
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Madison
2.752
0.568
0.224
0.265
0.494
0.573
Morehouse
5.154
0.618
0.146
0.364
0.481
0.528
Natchitoches
5.113
0.605
0.171
0.485
0.418
0.543
Orleans
0.694
0.626
0.692
0.388
0.398
0.386
Ouachita
2.257
0.570
0.406
0.551
0.535
0.443
Plaquemines
2.280
0.603
0.338
0.532
0.486
0.368
Pointe Coupee
2.348
0.609
0.352
0.434
0.537
0.465
Rapides
4.264
0.592
0.313
0.717
0.559
0.507
Red River
2.446
0.517
0.202
0.353
0.448
0.462
Richland
4.671
0.520
0.129
0.315
0.574
0.483
Sabine
5.044
0.621
0.119
0.420
0.430
0.408
St. Bernard
2.222
0.693
0.321
0.450
0.519
0.330
St. Charles
1.642
0.629
0.554
0.437
0.589
0.457
St. Helena
1.305
0.384
0.206
0.290
0.443
0.430
St. James
1.549
0.617
0.473
0.362
0.550
0.464
St. John the Baptist
1.591
0.621
0.484
0.392
0.595
0.416
St. Landry
3.488
0.553
0.261
0.547
0.510
0.481
St. Martin
1.814
0.511
0.345
0.416
0.529
0.437
St. Mary
2.304
0.534
0.351
0.501
0.541
0.453
St. Tammany
0.968
0.410
0.698
0.597
0.518
0.423
Tangipahoa
2.005
0.569
0.404
0.573
0.438
0.422
Tensas
1.712
0.564
0.269
0.344
0.300
0.532
Terrebonne
1.800
0.535
0.335
0.475
0.491
0.364
Union
3.218
0.551
0.207
0.377
0.475
0.517
Vermilion
2.519
0.653
0.350
0.500
0.443
0.467
Vernon
4.454
0.655
0.215
0.498
0.412
0.538
Washington
0.882
0.302
0.254
0.348
0.371
0.441
Webster
3.840
0.690
0.234
0.449
0.497
0.460
West Baton Rouge
1.721
0.584
0.596
0.451
0.691
0.487
West Carroll
5.039
0.701
0.113
0.328
0.367
0.495
West Feliciana
2.169
0.712
0.314
0.370
0.490
0.409
Winn
4.071
0.561
0.145
0.364
0.464
0.475
New Mexico
6.490
0.621
0.166
0.472
0.505
0.498
Bernalillo
1.949
0.609
0.581
0.611
0.557
0.461
Catron
4.783
0.586
0.179
0.394
0.504
0.576
Chaves
3.253
0.568
0.283
0.554
0.511
0.465
Cibola
7.995
0.593
0.100
0.429
0.454
0.534
Colfax
6.315
0.669
0.155
0.463
0.553
0.465
Curry
3.256
0.593
0.190
0.482
0.528
0.293
De Baca
5.443
0.814
0.132
0.258
0.583
0.427
251
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Do?a Ana
8.704
0.609
0.175
0.736
0.497
0.635
Eddy
5.114
0.526
0.204
0.540
0.490
0.645
Grant
10.633
0.604
0.114
0.530
0.646
0.535
Guadalupe
5.862
0.702
0.104
0.330
0.585
0.341
Harding
0.429
0.752
0.087
0.206
0.299
0.372
Hidalgo
4.926
0.573
0.099
0.298
0.336
0.567
Lea
5.582
0.611
0.180
0.570
0.469
0.490
Lincoln
4.285
0.543
0.248
0.576
0.603
0.502
Los Alamos
1.152
0.344
0.365
0.256
0.596
0.557
Luna
11.857
0.627
0.077
0.472
0.349
0.617
McKinley
12.664
0.625
0.095
0.624
0.469
0.541
Mora
2.648
0.695
0.118
0.329
0.411
0.310
Otero
10.251
0.542
0.110
0.601
0.438
0.659
Quay
7.873
0.724
0.116
0.412
0.608
0.392
Rio Arriba
10.097
0.629
0.134
0.619
0.553
0.574
Roosevelt
1.837
0.569
0.221
0.372
0.487
0.311
San Juan
12.005
0.615
0.122
0.693
0.519
0.620
San Miguel
5.675
0.648
0.130
0.467
0.435
0.423
Sandoval
10.102
0.724
0.147
0.642
0.510
0.544
Santa Fe
7.908
0.640
0.157
0.592
0.629
0.458
Sierra
11.673
0.567
0.087
0.360
0.484
0.762
Socorro
3.522
0.619
0.249
0.446
0.398
0.589
Taos
8.605
0.555
0.106
0.545
0.562
0.443
Torrance
7.751
0.675
0.102
0.430
0.516
0.411
Union
3.766
0.711
0.145
0.234
0.592
0.398
Valencia
6.263
0.634
0.164
0.507
0.490
0.534
Oklahoma
3.179
0.649
0.244
0.384
0.530
0.401
Adair
1.875
0.591
0.152
0.328
0.322
0.396
Alfalfa
1.816
0.676
0.257
0.188
0.736
0.303
Atoka
1.979
0.621
0.252
0.377
0.416
0.400
Beaver
2.239
0.652
0.347
0.353
0.597
0.438
Beckham
4.391
0.613
0.180
0.437
0.674
0.326
Blaine
1.940
0.677
0.361
0.277
0.659
0.407
Bryan
4.140
0.583
0.192
0.464
0.495
0.469
Caddo
3.878
0.753
0.268
0.489
0.519
0.428
Canadian
1.841
0.623
0.497
0.511
0.577
0.395
Carter
3.017
0.649
0.250
0.423
0.559
0.380
Cherokee
3.339
0.577
0.189
0.423
0.390
0.489
Choctaw
5.920
0.645
0.135
0.417
0.439
0.516
Cimarron
2.640
0.710
0.269
252
0.273
0.595
0.450
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Cleveland
0.770
0.715
0.889
0.388
0.493
0.389
Coal
1.218
0.735
0.318
0.226
0.443
0.428
Comanche
2.584
0.666
0.312
0.538
0.462
0.354
Cotton
1.860
0.683
0.188
0.271
0.422
0.391
Craig
6.090
0.607
0.150
0.369
0.619
0.529
Creek
3.071
0.717
0.273
0.473
0.502
0.375
Custer
4.251
0.575
0.212
0.449
0.691
0.412
Delaware
2.099
0.562
0.257
0.459
0.407
0.381
Dewey
3.116
0.733
0.269
0.429
0.569
0.358
Ellis
2.967
0.728
0.288
0.346
0.634
0.409
Garfield
4.687
0.581
0.161
0.469
0.557
0.389
Garvin
3.309
0.623
0.168
0.408
0.490
0.342
Grady
3.236
0.641
0.247
0.464
0.578
0.357
Grant
2.516
0.701
0.302
0.320
0.693
0.353
Greer
1.710
0.599
0.199
0.169
0.606
0.378
Harmon
-0.986
0.614
0.256
0.077
0.325
0.303
Harper
2.964
0.657
0.287
0.341
0.696
0.415
Haskell
3.131
0.715
0.168
0.312
0.466
0.401
Hughes
1.638
0.601
0.200
0.288
0.474
0.344
Jackson
1.678
0.633
0.346
0.380
0.472
0.398
Jefferson
1.920
0.695
0.209
0.216
0.486
0.426
Johnston
2.151
0.685
0.221
0.331
0.383
0.430
Kay
5.864
0.761
0.179
0.437
0.555
0.458
Kingfisher
4.885
0.749
0.201
0.359
0.706
0.393
Kiowa
2.918
0.680
0.279
0.320
0.711
0.386
Latimer
2.248
0.532
0.191
0.319
0.470
0.420
Le Flore
2.851
0.436
0.236
0.548
0.393
0.533
Lincoln
4.431
0.745
0.206
0.402
0.632
0.372
Logan
3.181
0.675
0.210
0.392
0.511
0.384
Love
1.989
0.614
0.236
0.294
0.502
0.405
Major
2.479
0.627
0.206
0.288
0.665
0.302
Marshall
1.521
0.568
0.237
0.309
0.415
0.405
Mayes
4.002
0.584
0.180
0.495
0.487
0.389
McClain
3.727
0.686
0.240
0.433
0.639
0.363
McCurtain
3.334
0.487
0.157
0.478
0.345
0.458
Mcintosh
3.487
0.638
0.171
0.376
0.439
0.434
Murray
4.068
0.641
0.153
0.331
0.598
0.373
Muskogee
4.355
0.572
0.201
0.497
0.529
0.474
Noble
4.963
0.682
0.161
0.366
0.580
0.432
Nowata
3.127
0.751
0.191
0.260
0.553
0.413
Okfuskee
2.791
0.712
0.145
0.298
0.428
0.379
253
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Oklahoma
0.868
0.564
0.783
0.575
0.522
0.264
Okmulgee
4.724
0.642
0.134
0.368
0.513
0.406
Osage
7.378
0.683
0.177
0.503
0.501
0.644
Ottawa
3.231
0.734
0.237
0.416
0.411
0.459
Pawnee
5.848
0.740
0.126
0.346
0.516
0.431
Payne
2.937
0.649
0.293
0.482
0.489
0.439
Pittsburg
4.262
0.590
0.193
0.522
0.556
0.369
Pontotoc
4.237
0.564
0.168
0.432
0.596
0.383
Pottawatomie
3.184
0.688
0.194
0.392
0.489
0.364
Pushmataha
4.008
0.654
0.139
0.389
0.412
0.410
Roger Mills
2.428
0.755
0.300
0.354
0.649
0.303
Rogers
4.186
0.749
0.291
0.501
0.545
0.496
Seminole
2.142
0.672
0.180
0.272
0.511
0.344
Sequoyah
3.207
0.499
0.156
0.439
0.414
0.414
Stephens
3.337
0.615
0.157
0.376
0.521
0.336
Texas
4.574
0.623
0.205
0.483
0.554
0.459
Tillman
2.456
0.662
0.171
0.257
0.476
0.412
Tulsa
1.117
0.679
0.832
0.628
0.486
0.301
Wagoner
3.678
0.646
0.235
0.498
0.466
0.445
Washington
2.702
0.694
0.223
0.320
0.561
0.371
Washita
3.242
0.659
0.239
0.331
0.699
0.375
Woods
3.845
0.667
0.173
0.347
0.625
0.342
Woodward
6.006
0.598
0.147
0.484
0.672
0.351
Texas
2.835
0.577
0.223
0.377
0.459
0.403
Anderson
4.889
0.557
0.138
0.475
0.412
0.461
Andrews
2.048
0.486
0.108
0.339
0.349
0.352
Angelina
3.534
0.515
0.195
0.516
0.448
0.441
Aransas
3.070
0.573
0.180
0.334
0.404
0.522
Archer
5.967
0.603
0.123
0.431
0.603
0.361
Armstrong
0.550
0.570
0.330
0.203
0.462
0.354
Atascosa
6.983
0.587
0.091
0.451
0.410
0.436
Austin
5.903
0.594
0.111
0.420
0.535
0.379
Bailey
0.779
0.538
0.236
0.241
0.405
0.367
Bandera
6.551
0.565
0.085
0.373
0.529
0.387
Bastrop
1.739
0.493
0.291
0.524
0.465
0.293
Baylor
0.635
0.404
0.135
0.134
0.440
0.403
Bee
7.086
0.773
0.102
0.351
0.413
0.484
Bell
1.036
0.281
0.496
0.680
0.447
0.463
Bexar
1.156
0.261
0.530
0.884
0.521
0.391
Blanco
1.374
0.238
0.237
0.351
0.691
0.438
254
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Borden
0.417
0.665
0.139
0.133
0.386
0.396
Bosque
6.477
0.586
0.114
0.423
0.554
0.424
Bowie
2.191
0.488
0.312
0.519
0.458
0.454
Brazoria
2.694
0.662
0.602
0.776
0.524
0.549
Brazos
1.739
0.497
0.323
0.531
0.426
0.357
Brewster
5.862
0.642
0.151
0.360
0.590
0.511
Briscoe
2.225
0.678
0.237
0.289
0.529
0.394
Brooks
-0.129
0.557
0.270
0.228
0.165
0.408
Brown
3.701
0.535
0.159
0.414
0.578
0.350
Burleson
4.754
0.647
0.128
0.337
0.511
0.420
Burnet
4.301
0.585
0.203
0.431
0.596
0.477
Caldwell
2.825
0.465
0.138
0.403
0.509
0.312
Calhoun
2.808
0.505
0.217
0.435
0.429
0.490
Callahan
3.683
0.564
0.159
0.384
0.537
0.389
Cameron
4.001
0.575
0.334
0.702
0.384
0.690
Camp
1.030
0.623
0.278
0.244
0.467
0.362
Carson
2.129
0.762
0.468
0.380
0.635
0.426
Cass
2.324
0.565
0.206
0.380
0.402
0.425
Castro
1.560
0.558
0.203
0.233
0.324
0.533
Chambers
1.567
0.615
0.571
0.511
0.440
0.500
Cherokee
4.782
0.528
0.133
0.506
0.393
0.441
Childress
0.866
0.375
0.198
0.239
0.481
0.356
Clay
8.308
0.662
0.108
0.413
0.674
0.380
Cochran
-0.606
0.487
0.247
0.030
0.396
0.339
Coke
0.272
0.594
0.102
0.270
0.228
0.357
Coleman
2.797
0.700
0.133
0.229
0.501
0.380
Collin
2.168
0.699
0.549
0.678
0.527
0.351
Collingsworth
1.058
0.629
0.250
0.168
0.644
0.288
Colorado
6.818
0.589
0.095
0.379
0.559
0.403
Comal
0.981
0.290
0.380
0.505
0.595
0.313
Comanche
4.608
0.679
0.115
0.338
0.433
0.417
Concho
3.620
0.719
0.102
0.208
0.502
0.396
Cooke
4.780
0.606
0.135
0.471
0.496
0.339
Coryell
5.613
0.624
0.143
0.445
0.446
0.497
Cottle
0.341
0.688
0.315
0.228
0.387
0.320
Crane
1.158
0.563
0.143
0.159
0.435
0.413
Crockett
6.122
0.543
0.102
0.304
0.581
0.489
Crosby
2.219
0.472
0.147
0.292
0.476
0.397
Culberson
0.754
0.670
0.214
0.187
0.456
0.344
Dallam
1.140
0.658
0.299
0.258
0.509
0.338
Dallas
1.232
0.635
0.844
0.789
0.485
0.236
255
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Dawson
2.599
0.591
0.097
0.295
0.463
0.298
Deaf Smith
1.506
0.475
0.200
0.304
0.455
0.377
Delta
0.881
0.673
0.289
0.227
0.376
0.421
Denton
2.632
0.676
0.512
0.711
0.493
0.460
DeWitt
4.451
0.517
0.102
0.298
0.475
0.468
Dickens
0.061
0.674
0.212
0.091
0.403
0.401
Dimmit
5.251
0.466
0.080
0.328
0.475
0.443
Donley
1.274
0.730
0.345
0.315
0.527
0.295
Duval
1.758
0.548
0.108
0.327
0.275
0.380
Eastland
3.250
0.471
0.120
0.354
0.491
0.374
Ector
1.459
0.581
0.382
0.507
0.448
0.296
Edwards
0.592
0.498
0.118
0.205
0.385
0.341
El Paso
2.536
0.637
0.417
0.709
0.443
0.367
Ellis
2.991
0.711
0.446
0.716
0.538
0.371
Erath
8.733
0.698
0.116
0.452
0.558
0.469
Falls
2.417
0.523
0.155
0.326
0.405
0.426
Fannin
6.984
0.647
0.115
0.452
0.501
0.428
Fayette
6.177
0.451
0.091
0.423
0.584
0.395
Fisher
1.176
0.569
0.174
0.183
0.475
0.382
Floyd
2.534
0.596
0.238
0.212
0.552
0.553
Foard
0.790
0.464
0.096
0.249
0.308
0.364
Fort Bend
3.545
0.644
0.411
0.785
0.580
0.420
Franklin
0.984
0.718
0.231
0.271
0.393
0.334
Freestone
2.167
0.511
0.198
0.406
0.432
0.370
Frio
4.964
0.632
0.085
0.335
0.378
0.419
Gaines
2.636
0.546
0.135
0.294
0.487
0.370
Galveston
1.257
0.610
0.753
0.608
0.472
0.408
Garza
0.911
0.469
0.251
0.219
0.520
0.359
Gillespie
5.654
0.513
0.132
0.385
0.691
0.436
Glasscock
0.417
0.603
0.195
0.162
0.386
0.384
Goliad
2.783
0.540
0.126
0.277
0.409
0.452
Gonzales
5.614
0.622
0.075
0.356
0.378
0.400
Gray
-0.014
0.638
0.262
0.210
0.478
0.202
Grayson
5.524
0.615
0.180
0.685
0.512
0.320
Gregg
0.785
0.564
0.613
0.396
0.561
0.283
Grimes
1.601
0.529
0.237
0.372
0.468
0.328
Guadalupe
2.822
0.578
0.253
0.557
0.476
0.331
Hale
3.020
0.631
0.252
0.317
0.496
0.563
Hall
0.691
0.559
0.235
0.126
0.484
0.408
Hamilton
3.915
0.599
0.131
0.311
0.484
0.439
Hansford
1.306
0.649
0.262
0.340
0.450
0.299
256
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Hardeman
1.224
0.390
0.108
0.235
0.453
0.335
Hardin
1.725
0.594
0.357
0.365
0.546
0.403
Harris
1.345
0.611
0.758
0.837
0.491
0.192
Harrison
2.173
0.605
0.356
0.523
0.432
0.422
Hartley
1.316
0.722
0.233
0.319
0.440
0.289
Haskell
3.094
0.513
0.119
0.214
0.492
0.482
Hays
0.943
0.301
0.454
0.587
0.557
0.310
Hemphill
0.609
0.575
0.393
0.251
0.489
0.320
Henderson
5.915
0.606
0.141
0.536
0.439
0.441
Hidalgo
2.938
0.548
0.485
0.731
0.390
0.767
Hill
4.291
0.477
0.136
0.470
0.459
0.436
Hockley
3.576
0.684
0.178
0.387
0.519
0.357
Hood
2.698
0.588
0.280
0.469
0.535
0.399
Hopkins
6.848
0.630
0.104
0.425
0.559
0.367
Houston
2.119
0.595
0.273
0.407
0.394
0.453
Howard
4.099
0.598
0.146
0.322
0.465
0.502
Hudspeth
3.255
0.617
0.080
0.316
0.256
0.438
Hunt
2.584
0.568
0.243
0.576
0.384
0.331
Hutchinson
1.115
0.629
0.264
0.318
0.486
0.271
Irion
1.992
0.712
0.240
0.245
0.433
0.475
Jack
2.441
0.672
0.132
0.297
0.423
0.348
Jackson
5.510
0.586
0.121
0.337
0.538
0.481
Jasper
1.900
0.586
0.371
0.431
0.463
0.465
Jeff Davis
1.305
0.386
0.143
0.292
0.318
0.439
Jefferson
2.005
0.534
0.530
0.698
0.521
0.449
Jim Hogg
1.029
0.606
0.262
0.219
0.470
0.381
Jim Wells
3.181
0.572
0.109
0.328
0.392
0.391
Johnson
2.782
0.600
0.359
0.595
0.515
0.434
Jones
4.895
0.645
0.116
0.266
0.520
0.468
Karnes
3.957
0.618
0.098
0.320
0.406
0.398
Kaufman
2.182
0.688
0.446
0.547
0.506
0.392
Kendall
4.833
0.456
0.144
0.432
0.678
0.423
Kenedy
2.512
0.656
0.117
0.304
0.273
0.447
Kent
0.166
0.709
0.308
0.214
0.270
0.395
Kerr
6.409
0.450
0.098
0.487
0.550
0.412
Kimble
4.393
0.496
0.134
0.208
0.747
0.474
King
0.943
0.861
0.324
0.215
0.452
0.363
Kinney
1.693
0.542
0.097
0.315
0.225
0.416
Kleberg
3.410
0.600
0.124
0.314
0.379
0.458
Knox
4.216
0.647
0.129
0.338
0.490
0.398
La Salle
-0.964
0.629
0.090
0.176
0.292
0.333
257
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Lamar
4.756
0.589
0.169
0.481
0.511
0.439
Lamb
2.592
0.681
0.157
0.246
0.480
0.406
Lampasas
4.618
0.573
0.111
0.336
0.526
0.393
Lavaca
6.844
0.568
0.098
0.357
0.618
0.414
Lee
4.309
0.600
0.101
0.337
0.519
0.328
Leon
4.069
0.625
0.146
0.434
0.457
0.363
Liberty
2.381
0.568
0.316
0.508
0.467
0.428
Limestone
3.567
0.637
0.176
0.398
0.468
0.408
Lipscomb
1.241
0.774
0.334
0.332
0.375
0.371
Live Oak
7.268
0.560
0.094
0.384
0.547
0.458
Llano
5.349
0.576
0.101
0.381
0.482
0.396
Loving
1.779
0.787
0.092
0.223
0.308
0.411
Lubbock
1.818
0.545
0.492
0.599
0.495
0.437
Lynn
3.234
0.711
0.207
0.340
0.448
0.469
Madison
1.661
0.645
0.148
0.243
0.442
0.352
Marion
0.781
0.431
0.249
0.256
0.341
0.447
Martin
1.494
0.543
0.157
0.227
0.528
0.321
Mason
3.040
0.394
0.130
0.323
0.592
0.400
Matagorda
2.677
0.545
0.256
0.440
0.431
0.503
Maverick
3.521
0.677
0.143
0.446
0.269
0.419
McCulloch
3.778
0.558
0.098
0.275
0.414
0.456
McLennan
3.588
0.539
0.326
0.691
0.534
0.521
McMullen
-0.695
0.624
0.110
0.213
0.233
0.347
Medina
4.297
0.423
0.121
0.490
0.491
0.388
Menard
1.540
0.523
0.191
0.209
0.528
0.394
Midland
1.660
0.623
0.314
0.461
0.443
0.299
Milam
3.343
0.505
0.128
0.400
0.458
0.357
Mills
2.185
0.506
0.178
0.323
0.456
0.412
Mitchell
2.219
0.558
0.134
0.257
0.439
0.400
Montague
2.572
0.575
0.142
0.320
0.422
0.388
Montgomery
1.539
0.614
0.590
0.644
0.496
0.320
Moore
1.442
0.584
0.357
0.423
0.423
0.376
Morris
0.852
0.627
0.271
0.231
0.446
0.357
Motley
0.398
0.547
0.245
0.187
0.412
0.354
Nacogdoches
3.253
0.519
0.157
0.425
0.413
0.424
Navarro
2.113
0.636
0.324
0.498
0.434
0.364
Newton
0.860
0.556
0.272
0.262
0.357
0.415
Nolan
2.486
0.458
0.152
0.407
0.455
0.344
Nueces
2.518
0.639
0.465
0.699
0.477
0.419
Ochiltree
1.168
0.623
0.289
0.283
0.451
0.367
Oldham
0.684
0.860
0.328
0.278
0.382
0.313
258
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Orange
0.848
0.501
0.624
0.446
0.458
0.394
Palo Pinto
3.404
0.611
0.171
0.455
0.411
0.378
Panola
2.427
0.512
0.186
0.395
0.378
0.443
Parker
4.123
0.647
0.253
0.630
0.528
0.366
Parmer
3.377
0.651
0.174
0.342
0.412
0.480
Pecos
4.720
0.590
0.157
0.502
0.512
0.372
Polk
1.733
0.558
0.336
0.481
0.322
0.459
Potter
1.500
0.471
0.477
0.482
0.607
0.424
Presidio
4.401
0.596
0.098
0.343
0.337
0.466
Rains
3.426
0.610
0.139
0.355
0.530
0.322
Randall
1.625
0.593
0.474
0.408
0.594
0.425
Reagan
1.490
0.559
0.188
0.196
0.525
0.385
Real
2.888
0.609
0.086
0.199
0.407
0.439
Red River
1.089
0.639
0.262
0.298
0.359
0.385
Reeves
2.043
0.605
0.110
0.292
0.425
0.308
Refugio
3.961
0.631
0.116
0.266
0.443
0.468
Roberts
0.382
0.689
0.349
0.187
0.470
0.314
Robertson
2.598
0.600
0.173
0.355
0.405
0.410
Rockwall
1.656
0.706
0.476
0.333
0.609
0.420
Runnels
2.629
0.684
0.231
0.220
0.546
0.500
Rusk
2.966
0.586
0.209
0.507
0.407
0.368
Sabine
2.456
0.464
0.127
0.327
0.305
0.490
San Augustine
1.390
0.421
0.132
0.205
0.344
0.494
San Jacinto
1.294
0.542
0.263
0.275
0.347
0.493
San Patricio
3.860
0.615
0.189
0.489
0.402
0.444
San Saba
2.593
0.626
0.161
0.188
0.625
0.382
Schleicher
2.540
0.458
0.113
0.229
0.497
0.423
Scurry
2.706
0.674
0.149
0.324
0.413
0.375
Shackelford
1.336
0.718
0.247
0.292
0.336
0.415
Shelby
1.991
0.565
0.144
0.340
0.287
0.422
Sherman
0.740
0.625
0.244
0.239
0.444
0.317
Smith
2.703
0.590
0.366
0.630
0.532
0.387
Somervell
2.255
0.370
0.175
0.341
0.529
0.455
Starr
4.902
0.581
0.174
0.473
0.193
0.745
Stephens
0.471
0.447
0.262
0.188
0.465
0.351
Sterling
3.273
0.683
0.170
0.244
0.611
0.392
Stonewall
0.292
0.490
0.291
0.048
0.554
0.389
Sutton
1.267
0.400
0.230
0.225
0.591
0.393
Swisher
2.365
0.637
0.208
0.251
0.382
0.550
Tarrant
1.406
0.633
0.683
0.717
0.497
0.255
Taylor
2.963
0.633
0.312
0.559
0.511
0.394
259
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Terrell
-0.068
0.786
0.169
0.245
0.207
0.376
Terry
2.306
0.630
0.114
0.305
0.377
0.351
Throckmorton
2.527
0.531
0.109
0.194
0.442
0.461
Titus
3.436
0.558
0.152
0.454
0.413
0.371
Tom Green
4.164
0.389
0.156
0.530
0.556
0.473
Travis
0.856
0.207
0.489
0.844
0.514
0.308
Trinity
2.014
0.626
0.201
0.310
0.381
0.436
Tyler
1.388
0.563
0.276
0.371
0.370
0.392
Upshur
3.405
0.515
0.151
0.406
0.510
0.369
Upton
4.293
0.531
0.107
0.292
0.448
0.492
Uvalde
9.970
0.611
0.067
0.378
0.484
0.460
Val Verde
7.901
0.617
0.082
0.485
0.424
0.375
Van Zandt
6.769
0.634
0.099
0.444
0.492
0.369
Victoria
6.348
0.533
0.141
0.512
0.541
0.510
Walker
2.458
0.548
0.223
0.422
0.463
0.391
Waller
1.823
0.555
0.311
0.417
0.508
0.371
Ward
2.833
0.522
0.099
0.351
0.396
0.334
Washington
2.064
0.386
0.225
0.356
0.541
0.484
Webb
8.063
0.626
0.133
0.725
0.399
0.404
Wharton
8.445
0.518
0.079
0.453
0.505
0.444
Wheeler
0.566
0.631
0.322
0.253
0.436
0.309
Wichita
2.513
0.560
0.260
0.504
0.485
0.354
Wilbarger
4.432
0.654
0.098
0.255
0.472
0.430
Willacy
3.530
0.560
0.163
0.285
0.257
0.718
Williamson
1.566
0.270
0.368
0.725
0.558
0.451
Wilson
10.829
0.689
0.081
0.474
0.551
0.380
Winkler
-0.490
0.527
0.062
0.189
0.352
0.303
Wise
7.711
0.659
0.136
0.609
0.459
0.432
Wood
3.787
0.672
0.152
0.396
0.528
0.310
Yoakum
1.288
0.527
0.154
0.248
0.407
0.372
Young
4.115
0.630
0.133
0.381
0.527
0.333
Zapata
2.299
0.609
0.154
0.384
0.265
0.422
Zavala
0.176
0.477
0.110
0.255
0.191
0.401
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 7
4.469
0.683
0.209
0.358
0.609
0.380
260
-------
Area
, Built _ . Natural
CRSI Governance Risk Society
Environment Environment
wa
5.263
0.704
0.210
0.382
0.653
0.419
Adair
5.225
0.786
0.229
0.421
0.690
0.423
Adams
1.628
0.790
0.303
0.206
0.647
0.327
Allamakee
6.770
0.711
0.131
0.369
0.678
0.378
Appanoose
3.713
0.686
0.164
0.306
0.562
0.395
Audubon
4.278
0.735
0.202
0.255
0.788
0.387
Benton
7.419
0.721
0.158
0.481
0.700
0.393
Black Hawk
2.088
0.708
0.486
0.527
0.561
0.376
Boone
4.680
0.645
0.171
0.364
0.636
0.414
Bremer
9.923
0.735
0.149
0.434
0.792
0.526
Buchanan
5.753
0.679
0.185
0.437
0.675
0.435
Buena Vista
7.584
0.752
0.147
0.425
0.614
0.465
Butler
6.096
0.721
0.202
0.398
0.736
0.488
Calhoun
3.035
0.726
0.271
0.324
0.666
0.392
Carroll
6.262
0.686
0.170
0.439
0.800
0.331
Cass
6.089
0.657
0.191
0.384
0.843
0.445
Cedar
8.664
0.717
0.146
0.441
0.677
0.511
Cerro Gordo
4.168
0.653
0.217
0.423
0.618
0.424
Cherokee
9.518
0.678
0.101
0.319
0.830
0.380
Chickasaw
13.918
0.717
0.092
0.376
0.777
0.513
Clarke
3.006
0.596
0.177
0.297
0.547
0.418
Clay
6.025
0.635
0.188
0.379
0.801
0.487
Clayton
11.687
0.718
0.113
0.477
0.781
0.417
Clinton
4.947
0.743
0.217
0.439
0.565
0.473
Crawford
5.054
0.663
0.146
0.418
0.607
0.327
Dallas
3.339
0.766
0.399
0.532
0.586
0.477
Davis
2.076
0.697
0.201
0.290
0.460
0.375
Decatur
2.984
0.724
0.182
0.291
0.509
0.396
Delaware
3.677
0.687
0.297
0.430
0.771
0.378
Des Moines
3.001
0.705
0.282
0.336
0.561
0.489
Dickinson
6.185
0.654
0.186
0.431
0.721
0.485
Dubuque
3.285
0.671
0.322
0.494
0.684
0.372
Emmet
5.979
0.673
0.151
0.302
0.688
0.483
Fayette
11.248
0.685
0.096
0.362
0.730
0.479
Floyd
6.656
0.693
0.132
0.385
0.635
0.405
Franklin
3.991
0.712
0.198
0.312
0.641
0.415
Fremont
3.682
0.775
0.258
0.253
0.728
0.456
Greene
5.692
0.723
0.170
0.353
0.739
0.386
Grundy
8.028
0.764
0.156
0.379
0.732
0.486
Guthrie
4.664
0.707
0.189
0.390
0.625
0.397
Hamilton
3.703
0.663
0.173
0.390
0.578
0.322
261
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Hancock
7.334
0.724
0.131
0.321
0.674
0.468
Hardin
5.135
0.675
0.188
0.403
0.683
0.413
Harrison
3.509
0.790
0.263
0.332
0.677
0.387
Henry
7.179
0.760
0.156
0.395
0.599
0.504
Howard
8.467
0.720
0.115
0.312
0.673
0.486
Humboldt
3.489
0.683
0.153
0.328
0.542
0.343
Ida
8.366
0.736
0.105
0.287
0.748
0.389
Iowa
5.631
0.706
0.188
0.417
0.675
0.430
Jackson
3.958
0.698
0.233
0.428
0.614
0.396
Jasper
3.825
0.666
0.200
0.424
0.565
0.369
Jefferson
3.401
0.658
0.189
0.271
0.630
0.413
Johnson
3.598
0.681
0.332
0.550
0.548
0.493
Jones
4.844
0.705
0.204
0.366
0.719
0.412
Keokuk
5.122
0.723
0.167
0.340
0.600
0.446
Kossuth
8.941
0.712
0.142
0.459
0.745
0.447
Lee
3.816
0.662
0.256
0.401
0.624
0.481
Linn
1.863
0.692
0.750
0.681
0.623
0.398
Louisa
4.685
0.721
0.190
0.322
0.570
0.511
Lucas
2.074
0.696
0.177
0.258
0.472
0.371
Lyon
7.903
0.731
0.131
0.364
0.740
0.403
Madison
5.130
0.731
0.205
0.375
0.694
0.438
Mahaska
5.443
0.712
0.170
0.337
0.630
0.472
Marion
4.977
0.606
0.185
0.428
0.706
0.404
Marshall
4.005
0.690
0.181
0.417
0.532
0.364
Mills
4.233
0.758
0.230
0.401
0.649
0.382
Mitchell
8.104
0.728
0.140
0.355
0.735
0.475
Monona
4.388
0.694
0.157
0.305
0.583
0.422
Monroe
2.814
0.684
0.222
0.284
0.629
0.373
Montgomery
2.717
0.584
0.172
0.215
0.646
0.390
Muscatine
3.346
0.650
0.243
0.365
0.546
0.490
O'Brien
8.334
0.663
0.123
0.380
0.712
0.463
Osceola
4.570
0.682
0.236
0.390
0.716
0.462
Page
5.157
0.622
0.128
0.292
0.634
0.422
Palo Alto
6.431
0.744
0.158
0.375
0.697
0.406
Plymouth
9.143
0.715
0.114
0.467
0.722
0.323
Pocahontas
3.096
0.723
0.313
0.302
0.690
0.479
Polk
1.733
0.657
0.746
0.715
0.581
0.375
Pottawattamie
3.993
0.725
0.246
0.530
0.540
0.359
Poweshiek
5.775
0.678
0.177
0.436
0.702
0.391
Ringgold
1.737
0.717
0.360
0.228
0.664
0.390
Sac
3.558
0.759
0.262
0.335
0.697
0.393
262
-------
Area
, Built _ . Natural
CRSI Governance Risk Society
Environment Environment
Scott
1.745
0.627
0.477
0.472
0.567
0.389
Shelby
9.977
0.727
0.108
0.368
0.784
0.391
Sioux
6.450
0.675
0.194
0.484
0.831
0.378
Story
3.478
0.708
0.311
0.587
0.530
0.376
Tama
4.247
0.738
0.177
0.369
0.523
0.409
Taylor
1.137
0.741
0.292
0.174
0.539
0.376
Union
3.125
0.640
0.221
0.289
0.705
0.375
Van Buren
4.164
0.798
0.172
0.340
0.461
0.442
Wapello
1.991
0.642
0.265
0.347
0.498
0.374
Warren
4.842
0.645
0.200
0.435
0.620
0.458
Washington
10.068
0.768
0.131
0.420
0.740
0.464
Wayne
1.801
0.717
0.261
0.256
0.522
0.386
Webster
4.654
0.718
0.203
0.423
0.620
0.396
Winnebago
7.573
0.744
0.145
0.331
0.767
0.442
Winneshiek
11.567
0.670
0.093
0.403
0.776
0.410
Woodbury
4.907
0.770
0.232
0.563
0.579
0.342
Worth
5.903
0.780
0.157
0.339
0.573
0.471
Wright
5.833
0.730
0.143
0.325
0.639
0.416
Kansas
4.594
0.698
0.195
0.332
0.651
0.369
Allen
6.363
0.670
0.126
0.355
0.614
0.425
Anderson
8.097
0.718
0.125
0.298
0.802
0.425
Atchison
5.877
0.600
0.106
0.328
0.584
0.412
Barber
3.689
0.689
0.137
0.258
0.656
0.308
Barton
2.752
0.690
0.334
0.425
0.684
0.347
Bourbon
4.195
0.699
0.185
0.275
0.647
0.451
Brown
10.405
0.850
0.130
0.338
0.742
0.502
Butler
5.826
0.651
0.214
0.611
0.639
0.419
Chase
2.427
0.762
0.226
0.273
0.506
0.406
Chautauqua
2.762
0.754
0.155
0.150
0.685
0.335
Cherokee
3.056
0.558
0.222
0.368
0.568
0.455
Cheyenne
4.606
0.851
0.163
0.296
0.627
0.349
Clark
2.315
0.669
0.200
0.306
0.555
0.319
Clay
7.463
0.637
0.102
0.276
0.748
0.400
Cloud
6.558
0.680
0.107
0.305
0.751
0.304
Coffey
9.115
0.817
0.134
0.405
0.777
0.363
Comanche
3.907
0.775
0.137
0.245
0.541
0.397
Cowley
3.245
0.631
0.182
0.427
0.500
0.333
Crawford
4.833
0.592
0.174
0.468
0.554
0.441
Decatur
4.265
0.669
0.141
0.222
0.704
0.377
Dickinson
7.469
0.670
0.132
0.397
0.721
0.406
263
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Doniphan
9.177
0.811
0.103
0.332
0.610
0.438
Douglas
3.609
0.600
0.254
0.514
0.545
0.443
Edwards
1.797
0.681
0.211
0.152
0.633
0.369
Elk
1.464
0.787
0.213
0.275
0.421
0.340
Ellis
3.994
0.568
0.181
0.422
0.734
0.288
Ellsworth
6.656
0.750
0.154
0.335
0.744
0.411
Finney
4.658
0.653
0.154
0.429
0.632
0.290
Ford
5.126
0.710
0.186
0.429
0.569
0.440
Franklin
6.232
0.629
0.129
0.393
0.634
0.402
Geary
3.437
0.756
0.197
0.365
0.470
0.407
Gove
4.817
0.756
0.181
0.325
0.812
0.282
Graham
2.483
0.707
0.245
0.196
0.704
0.389
Grant
1.936
0.826
0.254
0.242
0.597
0.316
Gray
3.893
0.731
0.203
0.382
0.680
0.297
Greeley
0.625
0.704
0.139
0.213
0.418
0.299
Greenwood
3.665
0.738
0.141
0.233
0.639
0.336
Hamilton
2.314
0.670
0.104
0.240
0.405
0.375
Harper
2.518
0.723
0.234
0.278
0.591
0.371
Harvey
5.831
0.632
0.157
0.413
0.666
0.422
Haskell
3.616
0.830
0.201
0.260
0.612
0.400
Hodgeman
1.327
0.654
0.282
0.266
0.517
0.345
Jackson
7.410
0.719
0.142
0.391
0.760
0.377
Jefferson
8.296
0.691
0.131
0.440
0.670
0.441
Jewell
1.160
0.600
0.383
0.220
0.680
0.332
Johnson
1.354
0.675
0.670
0.528
0.589
0.317
Kearny
5.831
0.844
0.171
0.283
0.726
0.409
Kingman
5.428
0.733
0.164
0.317
0.712
0.396
Kiowa
2.508
0.646
0.293
0.330
0.748
0.320
Labette
2.702
0.636
0.283
0.361
0.572
0.454
Lane
2.441
0.794
0.187
0.237
0.563
0.341
Leavenworth
3.229
0.698
0.269
0.437
0.575
0.387
Lincoln
4.708
0.678
0.105
0.216
0.716
0.305
Linn
6.039
0.704
0.194
0.425
0.660
0.504
Logan
5.799
0.662
0.124
0.239
0.868
0.302
Lyon
6.409
0.658
0.135
0.449
0.543
0.428
Marion
8.371
0.743
0.126
0.333
0.731
0.447
Marshall
12.238
0.758
0.082
0.330
0.819
0.342
McPherson
6.926
0.621
0.162
0.488
0.771
0.402
Meade
3.233
0.698
0.281
0.341
0.750
0.375
Miami
10.175
0.696
0.120
0.493
0.733
0.406
Mitchell
3.272
0.696
0.225
0.304
0.718
0.340
264
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Montgomery
4.483
0.645
0.177
0.419
0.526
0.438
Morris
5.363
0.697
0.153
0.304
0.715
0.390
Morton
1.778
0.617
0.172
0.249
0.434
0.398
Nemaha
9.777
0.681
0.113
0.380
0.859
0.377
Neosho
6.130
0.601
0.140
0.383
0.676
0.439
Ness
2.860
0.726
0.188
0.230
0.558
0.420
Norton
2.500
0.686
0.259
0.271
0.728
0.323
Osage
6.423
0.709
0.134
0.410
0.580
0.400
Osborne
2.731
0.759
0.216
0.215
0.688
0.346
Ottawa
11.483
0.787
0.101
0.324
0.838
0.394
Pawnee
2.831
0.522
0.161
0.244
0.690
0.354
Phillips
3.888
0.743
0.226
0.302
0.787
0.337
Pottawatomie
9.454
0.730
0.124
0.478
0.739
0.358
Pratt
2.532
0.742
0.314
0.311
0.719
0.338
Rawlins
2.903
0.678
0.129
0.211
0.672
0.273
Reno
4.696
0.770
0.246
0.466
0.679
0.375
Republic
3.982
0.754
0.182
0.290
0.705
0.327
Rice
4.229
0.730
0.168
0.326
0.585
0.388
Riley
3.611
0.683
0.225
0.437
0.508
0.417
Rooks
3.003
0.704
0.232
0.283
0.705
0.346
Rush
3.069
0.584
0.204
0.351
0.679
0.325
Russell
3.278
0.674
0.136
0.272
0.564
0.337
Saline
7.297
0.766
0.172
0.449
0.670
0.458
Scott
4.089
0.572
0.144
0.269
0.761
0.332
Sedgwick
1.459
0.604
0.795
0.714
0.559
0.372
Seward
1.377
0.522
0.241
0.373
0.509
0.262
Shawnee
2.335
0.628
0.358
0.447
0.610
0.382
Sheridan
2.996
0.740
0.188
0.274
0.710
0.259
Sherman
4.047
0.734
0.178
0.277
0.720
0.336
Smith
2.155
0.718
0.257
0.221
0.689
0.337
Stafford
1.674
0.716
0.205
0.227
0.604
0.279
Stanton
3.661
0.664
0.097
0.253
0.500
0.358
Stevens
2.249
0.677
0.106
0.268
0.498
0.267
Sumner
5.097
0.647
0.193
0.467
0.650
0.404
Thomas
5.368
0.651
0.166
0.389
0.760
0.342
Trego
2.723
0.689
0.289
0.257
0.791
0.367
Wabaunsee
12.348
0.809
0.107
0.418
0.828
0.361
Wallace
1.208
0.687
0.175
0.224
0.519
0.282
Washington
9.922
0.708
0.096
0.310
0.805
0.379
Wichita
7.151
0.856
0.092
0.230
0.664
0.344
Wilson
6.477
0.719
0.135
0.283
0.735
0.414
265
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Woodson
1.478
0.830
0.214
0.135
0.581
0.358
Wyandotte
0.320
0.654
0.653
0.294
0.454
0.262
Missouri
4.396
0.626
0.206
0.399
0.530
0.389
Adair
6.492
0.622
0.113
0.404
0.488
0.466
Andrew
7.454
0.739
0.116
0.379
0.551
0.435
Atchison
7.979
0.706
0.089
0.326
0.585
0.401
Audrain
8.086
0.592
0.108
0.456
0.623
0.422
Barry
2.697
0.508
0.208
0.466
0.489
0.366
Barton
2.694
0.648
0.246
0.350
0.545
0.414
Bates
5.285
0.703
0.161
0.407
0.478
0.483
Benton
4.953
0.519
0.147
0.440
0.517
0.494
Bollinger
2.108
0.612
0.179
0.327
0.467
0.336
Boone
2.924
0.691
0.360
0.596
0.501
0.387
Buchanan
3.561
0.673
0.199
0.362
0.530
0.426
Butler
2.165
0.460
0.248
0.441
0.511
0.401
Caldwell
9.203
0.724
0.081
0.333
0.554
0.428
Callaway
7.770
0.720
0.137
0.574
0.552
0.351
Camden
3.593
0.587
0.206
0.533
0.584
0.282
Cape Girardeau
2.423
0.504
0.338
0.540
0.664
0.358
Carroll
6.187
0.699
0.104
0.333
0.579
0.365
Carter
1.103
0.523
0.379
0.329
0.367
0.489
Cass
3.888
0.710
0.297
0.600
0.598
0.348
Cedar
2.637
0.648
0.296
0.306
0.629
0.469
Chariton
6.389
0.692
0.105
0.329
0.571
0.395
Christian
2.468
0.590
0.339
0.483
0.609
0.381
Clark
2.241
0.802
0.178
0.287
0.341
0.432
Clay
1.049
0.634
0.752
0.490
0.543
0.355
Clinton
9.805
0.773
0.104
0.387
0.649
0.412
Cole
3.431
0.545
0.249
0.483
0.694
0.373
Cooper
6.827
0.615
0.107
0.395
0.583
0.403
Crawford
5.373
0.599
0.136
0.395
0.589
0.409
Dade
2.087
0.605
0.151
0.240
0.427
0.424
Dallas
2.552
0.553
0.159
0.357
0.447
0.368
Daviess
6.875
0.799
0.119
0.343
0.540
0.425
DeKalb
7.420
0.719
0.108
0.381
0.496
0.458
Dent
6.137
0.666
0.103
0.293
0.575
0.422
Douglas
2.476
0.583
0.154
0.323
0.377
0.427
Dunklin
2.678
0.533
0.177
0.382
0.423
0.423
Franklin
2.560
0.610
0.388
266
0.601
0.590
0.354
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Gasconade
4.424
0.541
0.109
0.310
0.621
0.345
Gentry
3.086
0.771
0.215
0.229
0.651
0.396
Greene
1.034
0.600
0.674
0.541
0.518
0.285
Grundy
6.222
0.669
0.111
0.297
0.663
0.382
Harrison
6.562
0.769
0.102
0.305
0.574
0.377
Henry
7.539
0.621
0.128
0.419
0.586
0.524
Hickory
3.216
0.529
0.113
0.335
0.367
0.437
Holt
8.481
0.737
0.100
0.292
0.688
0.416
Howard
5.567
0.712
0.105
0.282
0.580
0.379
Howell
4.298
0.516
0.161
0.432
0.611
0.403
Iron
1.639
0.555
0.287
0.314
0.448
0.461
Jackson
1.604
0.689
0.709
0.723
0.491
0.309
Jasper
1.313
0.605
0.658
0.614
0.460
0.362
Jefferson
1.578
0.600
0.590
0.609
0.564
0.333
Johnson
6.269
0.619
0.148
0.572
0.524
0.385
Knox
1.448
0.747
0.148
0.240
0.378
0.368
Laclede
3.111
0.626
0.170
0.410
0.474
0.333
Lafayette
9.829
0.724
0.112
0.466
0.711
0.360
Lawrence
2.496
0.549
0.259
0.406
0.591
0.380
Lewis
3.442
0.702
0.187
0.323
0.548
0.398
Lincoln
3.926
0.654
0.240
0.535
0.539
0.388
Linn
6.916
0.705
0.106
0.370
0.534
0.408
Livingston
5.838
0.624
0.104
0.319
0.636
0.357
Macon
10.470
0.707
0.112
0.466
0.666
0.450
Madison
2.432
0.581
0.198
0.330
0.492
0.399
Maries
2.430
0.503
0.134
0.357
0.467
0.317
Marion
4.559
0.597
0.152
0.395
0.578
0.396
McDonald
1.812
0.528
0.163
0.387
0.447
0.264
Mercer
2.984
0.736
0.087
0.177
0.498
0.367
Miller
5.434
0.608
0.121
0.480
0.545
0.297
Mississippi
0.858
0.624
0.329
0.310
0.347
0.384
Moniteau
7.139
0.657
0.109
0.366
0.658
0.372
Monroe
6.691
0.692
0.123
0.325
0.622
0.448
Montgomery
7.410
0.702
0.105
0.380
0.573
0.396
Morgan
5.190
0.628
0.125
0.455
0.485
0.354
New Madrid
1.324
0.553
0.325
0.418
0.393
0.362
Newton
2.345
0.589
0.318
0.526
0.542
0.326
Nodaway
10.017
0.623
0.099
0.509
0.601
0.429
Oregon
2.033
0.480
0.178
0.271
0.451
0.465
Osage
9.390
0.615
0.094
0.407
0.790
0.322
Ozark
2.437
0.462
0.187
0.332
0.570
0.399
267
-------
Area
, Built _ . Natural
CRSI Governance Risk Society
Environment Environment
Pemiscot
1.461
0.595
0.235
0.310
0.396
0.397
Perry
4.027
0.644
0.191
0.333
0.752
0.338
Pettis
5.253
0.672
0.160
0.444
0.530
0.418
Phelps
5.511
0.606
0.136
0.502
0.510
0.364
Pike
5.451
0.628
0.138
0.402
0.538
0.433
Platte
2.349
0.737
0.384
0.495
0.523
0.357
Polk
4.151
0.563
0.160
0.404
0.548
0.417
Pulaski
1.882
0.567
0.293
0.481
0.388
0.378
Putnam
2.930
0.607
0.079
0.275
0.399
0.351
Ralls
6.976
0.705
0.127
0.388
0.608
0.418
Randolph
7.289
0.636
0.112
0.414
0.640
0.375
Ray
7.713
0.668
0.100
0.380
0.563
0.421
Reynolds
3.532
0.480
0.150
0.323
0.574
0.453
Ripley
2.095
0.487
0.141
0.263
0.385
0.467
Saline
5.479
0.632
0.108
0.364
0.536
0.370
Schuyler
4.128
0.751
0.071
0.282
0.391
0.353
Scotland
1.080
0.715
0.164
0.211
0.357
0.400
Scott
2.253
0.563
0.274
0.431
0.557
0.347
Shannon
3.294
0.694
0.152
0.339
0.317
0.486
Shelby
10.041
0.741
0.080
0.348
0.581
0.412
St. Charles
1.249
0.668
0.796
0.558
0.602
0.333
St. Clair
5.508
0.681
0.126
0.296
0.471
0.533
St. Francois
2.232
0.545
0.271
0.446
0.493
0.388
St. Louis
0.737
0.590
0.853
0.620
0.530
0.152
St. Louis city
-0.589
0.625
0.597
0.254
0.374
0.010
Ste. Genevieve
4.631
0.478
0.120
0.397
0.632
0.350
Stoddard
3.068
0.523
0.169
0.452
0.446
0.370
Stone
4.621
0.556
0.179
0.477
0.587
0.431
Sullivan
6.698
0.705
0.082
0.331
0.463
0.400
Taney
2.378
0.480
0.298
0.569
0.413
0.468
Texas
8.409
0.685
0.099
0.441
0.536
0.402
Vernon
9.540
0.675
0.103
0.424
0.587
0.477
Warren
3.698
0.623
0.179
0.460
0.491
0.355
Washington
4.494
0.579
0.151
0.369
0.514
0.479
Wayne
1.899
0.479
0.237
0.344
0.450
0.463
Webster
4.367
0.572
0.151
0.498
0.574
0.283
Worth
2.938
0.688
0.107
0.248
0.417
0.398
Wright
4.457
0.535
0.117
0.367
0.500
0.413
Nebraska
3.572
0.714
0.226
0.311
0.613
0.340
Adams
2.943
0.678
0.289
0.361
0.620
0.436
268
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Antelope
8.791
0.702
0.079
0.283
0.753
0.308
Arthur
-0.202
0.666
0.186
0.119
0.535
0.234
Banner
-3.158
0.914
0.153
0.171
0.236
0.224
Blaine
0.240
0.749
0.353
0.153
0.477
0.312
Boone
8.097
0.701
0.077
0.306
0.681
0.299
Box Butte
3.848
0.522
0.118
0.318
0.636
0.314
Boyd
0.631
0.797
0.407
0.191
0.586
0.270
Brown
2.198
0.646
0.272
0.196
0.818
0.323
Buffalo
3.869
0.662
0.282
0.510
0.736
0.344
Burt
7.826
0.725
0.082
0.252
0.659
0.376
Butler
8.718
0.752
0.124
0.355
0.723
0.435
Cass
6.888
0.674
0.130
0.498
0.588
0.342
Cedar
3.890
0.704
0.257
0.395
0.737
0.374
Chase
2.106
0.754
0.232
0.326
0.473
0.345
Cherry
2.919
0.737
0.264
0.367
0.627
0.339
Cheyenne
6.465
0.702
0.116
0.367
0.678
0.311
Clay
2.581
0.810
0.416
0.313
0.614
0.523
Colfax
6.004
0.760
0.087
0.313
0.467
0.380
Cuming
8.769
0.676
0.096
0.332
0.719
0.387
Custer
4.087
0.763
0.234
0.471
0.724
0.234
Dakota
4.885
0.796
0.137
0.352
0.446
0.416
Dawes
3.361
0.664
0.150
0.251
0.681
0.306
Dawson
3.121
0.723
0.218
0.395
0.641
0.255
Deuel
2.484
0.681
0.130
0.278
0.449
0.347
Dixon
5.098
0.771
0.124
0.336
0.531
0.359
Dodge
7.063
0.726
0.143
0.431
0.623
0.416
Douglas
0.929
0.723
0.810
0.589
0.555
0.155
Dundy
2.753
0.699
0.121
0.275
0.502
0.307
Fillmore
2.693
0.699
0.388
0.322
0.800
0.433
Franklin
0.730
0.780
0.268
0.191
0.538
0.279
Frontier
1.814
0.809
0.254
0.233
0.558
0.347
Furnas
2.925
0.794
0.319
0.394
0.640
0.353
Gage
3.087
0.711
0.264
0.392
0.689
0.303
Garden
0.845
0.718
0.294
0.234
0.521
0.285
Garfield
0.877
0.536
0.196
0.234
0.531
0.266
Gosper
1.825
0.687
0.311
0.280
0.642
0.333
Grant
0.543
0.829
0.235
0.154
0.533
0.283
Greeley
1.044
0.618
0.329
0.257
0.601
0.281
Hall
3.229
0.719
0.295
0.439
0.691
0.325
Hamilton
4.716
0.675
0.246
0.379
0.747
0.503
Harlan
1.736
0.744
0.279
0.239
0.532
0.395
269
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Hayes
-1.461
0.661
0.196
0.121
0.284
0.279
Hitchcock
0.529
0.805
0.185
0.241
0.400
0.282
Holt
3.921
0.732
0.274
0.388
0.889
0.278
Hooker
0.468
0.662
0.242
0.214
0.415
0.319
Howard
2.695
0.781
0.181
0.256
0.571
0.334
Jefferson
2.609
0.743
0.289
0.293
0.681
0.365
Johnson
5.632
0.735
0.116
0.298
0.673
0.312
Kearney
3.510
0.785
0.300
0.323
0.708
0.443
Keith
4.476
0.727
0.164
0.375
0.686
0.269
Keya Paha
-0.180
0.761
0.288
0.179
0.372
0.294
Kimball
0.305
0.617
0.219
0.136
0.509
0.304
Knox
4.803
0.754
0.230
0.418
0.673
0.419
Lancaster
2.165
0.654
0.594
0.636
0.631
0.419
Lincoln
4.963
0.663
0.198
0.551
0.663
0.289
Logan
0.160
0.769
0.318
0.194
0.551
0.190
Loup
-0.262
0.650
0.258
0.177
0.433
0.233
Madison
7.830
0.635
0.114
0.382
0.763
0.361
McPherson
-0.238
0.796
0.279
0.149
0.483
0.233
Merrick
4.054
0.700
0.172
0.285
0.679
0.367
Morrill
3.522
0.709
0.115
0.370
0.502
0.243
Nance
3.205
0.646
0.152
0.293
0.595
0.328
Nemaha
5.621
0.766
0.081
0.226
0.569
0.357
Nuckolls
2.071
0.651
0.213
0.199
0.630
0.370
Otoe
4.180
0.689
0.255
0.391
0.795
0.384
Pawnee
8.506
0.777
0.058
0.266
0.567
0.331
Perkins
7.398
0.778
0.128
0.330
0.763
0.340
Phelps
4.864
0.635
0.155
0.262
0.725
0.432
Pierce
18.224
0.697
0.053
0.299
0.826
0.388
Platte
6.302
0.620
0.145
0.431
0.736
0.359
Polk
3.854
0.763
0.238
0.317
0.690
0.407
Red Willow
6.064
0.691
0.104
0.313
0.677
0.305
Richardson
11.722
0.703
0.074
0.310
0.719
0.408
Rock
1.764
0.763
0.183
0.242
0.520
0.308
Saline
2.502
0.640
0.272
0.346
0.598
0.392
Sarpy
1.376
0.699
0.509
0.353
0.595
0.363
Saunders
8.828
0.753
0.140
0.453
0.708
0.423
Scotts Bluff
2.511
0.694
0.392
0.470
0.716
0.309
Seward
7.622
0.748
0.160
0.417
0.772
0.408
Sheridan
1.008
0.620
0.319
0.279
0.595
0.247
270
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Sherman
1.705
0.845
0.283
0.240
0.611
0.297
Sioux
-0.141
0.765
0.139
0.185
0.334
0.335
Stanton
3.498
0.593
0.192
0.370
0.651
0.352
Thayer
2.359
0.754
0.411
0.338
0.750
0.370
Thomas
1.301
0.716
0.293
0.216
0.559
0.349
Thurston
7.605
0.791
0.147
0.377
0.217
0.805
Valley
2.106
0.624
0.289
0.279
0.803
0.266
Washington
4.788
0.663
0.215
0.398
0.800
0.374
Wayne
3.875
0.659
0.175
0.329
0.590
0.403
Webster
0.645
0.671
0.286
0.207
0.498
0.302
Wheeler
-0.951
0.762
0.233
0.165
0.429
0.173
York
5.016
0.672
0.227
0.353
0.854
0.436
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 8
6.477
0.685
0.162
0.398
0.617
0.395
Colorado
5.776
0.673
0.203
0.453
0.555
0.396
Adams
1.306
0.677
0.592
0.606
0.520
0.205
Alamosa
5.334
0.600
0.121
0.389
0.601
0.347
Arapahoe
0.672
0.662
0.591
0.436
0.508
0.179
Archuleta
8.764
0.666
0.117
0.440
0.580
0.500
Baca
1.656
0.697
0.128
0.188
0.472
0.357
Bent
0.310
0.533
0.203
0.164
0.402
0.363
Boulder
2.194
0.659
0.556
0.645
0.539
0.436
Broomfield
0.246
0.720
0.702
0.227
0.524
0.246
Chaffee
12.822
0.598
0.091
0.472
0.744
0.504
Cheyenne
3.201
0.824
0.106
0.294
0.497
0.265
Clear Creek
5.586
0.693
0.202
0.499
0.619
0.438
Conejos
6.227
0.778
0.082
0.339
0.410
0.385
Costilla
-0.810
0.584
0.122
0.328
0.206
0.224
Crowley
-2.960
0.719
0.125
0.150
0.167
0.307
Custer
4.198
0.634
0.179
0.388
0.573
0.418
Delta
9.362
0.623
0.121
0.542
0.562
0.517
Denver
0.231
0.642
0.551
0.403
0.487
0.068
Dolores
5.398
0.602
0.097
0.222
0.438
0.577
Douglas
2.516
0.703
0.467
0.565
0.588
0.411
Eagle
12.194
0.663
0.124
0.627
0.650
0.543
El Paso
2.655
0.620
0.490
271
0.875
0.500
0.318
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Elbert
7.716
0.682
0.132
0.579
0.657
0.269
Fremont
6.408
0.712
0.155
0.498
0.527
0.418
Garfield
13.115
0.730
0.131
0.707
0.688
0.454
Gilpin
2.399
0.642
0.274
0.387
0.528
0.388
Grand
10.719
0.678
0.139
0.612
0.779
0.419
Gunnison
14.460
0.698
0.102
0.515
0.786
0.484
Hinsdale
5.888
0.658
0.092
0.273
0.428
0.509
Huerfano
2.542
0.614
0.168
0.352
0.455
0.351
Jackson
1.676
0.747
0.148
0.257
0.369
0.375
Jefferson
2.420
0.710
0.553
0.647
0.625
0.379
Kiowa
1.755
0.764
0.168
0.276
0.449
0.312
Kit Carson
6.676
0.741
0.125
0.382
0.709
0.290
La Plata
10.010
0.632
0.151
0.642
0.705
0.526
Lake
5.463
0.585
0.101
0.179
0.585
0.535
Larimer
4.329
0.686
0.380
0.856
0.591
0.388
Las Animas
4.434
0.667
0.154
0.402
0.588
0.324
Lincoln
4.704
0.610
0.133
0.416
0.485
0.391
Logan
5.218
0.652
0.154
0.541
0.569
0.275
Mesa
10.071
0.659
0.169
0.729
0.615
0.582
Mineral
5.089
0.772
0.119
0.294
0.335
0.546
Moffat
7.379
0.650
0.125
0.406
0.612
0.460
Montezuma
8.637
0.713
0.134
0.488
0.550
0.506
Montrose
8.539
0.645
0.163
0.605
0.636
0.524
Morgan
2.492
0.655
0.234
0.474
0.541
0.230
Otero
5.376
0.627
0.133
0.373
0.506
0.468
Ouray
15.737
0.755
0.073
0.411
0.681
0.442
Park
8.188
0.767
0.171
0.511
0.708
0.436
Phillips
3.286
0.743
0.130
0.293
0.528
0.307
Pitkin
14.512
0.643
0.076
0.498
0.604
0.485
Prowers
2.250
0.718
0.229
0.308
0.582
0.306
Pueblo
4.690
0.612
0.199
0.631
0.516
0.337
Rio Blanco
4.625
0.526
0.127
0.323
0.530
0.495
Rio Grande
2.079
0.645
0.313
0.404
0.542
0.353
Routt
12.139
0.640
0.124
0.663
0.771
0.435
Saguache
6.258
0.655
0.109
0.415
0.461
0.419
San Juan
9.960
0.686
0.079
0.296
0.387
0.647
San Miguel
14.215
0.714
0.093
0.475
0.668
0.517
Sedgwick
2.380
0.794
0.126
0.249
0.478
0.316
Summit
11.825
0.705
0.126
0.561
0.764
0.453
Teller
6.861
0.663
0.168
0.505
0.650
0.452
Washington
1.819
0.657
0.264
0.334
0.545
0.313
Ill
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Weld
5.504
0.703
0.302
0.971
0.523
0.305
Yuma
4.707
0.619
0.162
0.447
0.649
0.312
Montana
7.261
0.676
0.135
0.381
0.638
0.402
Beaverhead
13.121
0.613
0.083
0.412
0.798
0.450
Big Horn
3.627
0.628
0.213
0.351
0.439
0.583
Blaine
5.237
0.585
0.132
0.380
0.539
0.450
Broadwater
9.538
0.722
0.091
0.323
0.626
0.451
Carbon
10.193
0.682
0.135
0.482
0.831
0.448
Carter
2.584
0.745
0.128
0.247
0.488
0.337
Cascade
10.850
0.695
0.120
0.648
0.602
0.391
Chouteau
8.263
0.773
0.140
0.399
0.809
0.343
Custer
7.608
0.719
0.132
0.383
0.820
0.311
Daniels
17.054
0.668
0.056
0.292
0.790
0.443
Dawson
6.029
0.697
0.130
0.352
0.741
0.296
Deer Lodge
7.240
0.604
0.080
0.233
0.516
0.541
Fallon
6.854
0.610
0.083
0.311
0.729
0.274
Fergus
6.782
0.653
0.129
0.407
0.767
0.307
Flathead
17.476
0.681
0.114
0.827
0.697
0.545
Gallatin
4.544
0.661
0.354
0.657
0.776
0.469
Garfield
1.114
0.711
0.375
0.271
0.540
0.327
Glacier
5.527
0.671
0.113
0.335
0.420
0.494
Golden Valley
-2.188
0.755
0.084
0.216
0.249
0.281
Granite
12.836
0.684
0.060
0.321
0.560
0.476
Hill
5.675
0.696
0.114
0.330
0.683
0.288
Jefferson
12.768
0.647
0.093
0.523
0.756
0.387
Judith Basin
3.355
0.614
0.129
0.240
0.565
0.388
Lake
10.369
0.704
0.116
0.481
0.590
0.515
Lewis and Clark
8.309
0.519
0.133
0.591
0.725
0.458
Liberty
3.548
0.576
0.073
0.271
0.503
0.299
Lincoln
15.805
0.686
0.078
0.507
0.645
0.478
Madison
9.159
0.593
0.126
0.485
0.690
0.525
McCone
2.899
0.470
0.215
0.310
0.833
0.351
Meagher
3.488
0.590
0.101
0.289
0.390
0.432
Mineral
11.915
0.828
0.108
0.297
0.589
0.654
Missoula
11.079
0.632
0.114
0.616
0.642
0.442
Musselshell
2.098
0.589
0.136
0.224
0.586
0.299
Park
7.051
0.696
0.151
0.420
0.756
0.375
Petroleum
0.730
0.937
0.311
0.177
0.354
0.437
Phillips
5.272
0.589
0.190
0.369
0.735
0.516
Pondera
7.994
0.797
0.098
273
0.319
0.576
0.409
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Powder River
4.390
0.709
0.123
0.270
0.622
0.332
Powell
8.571
0.665
0.086
0.276
0.622
0.468
Prairie
1.788
0.829
0.206
0.206
0.510
0.363
Ravalli
15.261
0.671
0.074
0.488
0.600
0.493
Richland
6.808
0.713
0.106
0.383
0.600
0.331
Roosevelt
7.104
0.643
0.153
0.455
0.514
0.595
Rosebud
4.977
0.684
0.163
0.500
0.558
0.308
Sanders
12.309
0.648
0.103
0.542
0.691
0.473
Sheridan
8.163
0.776
0.121
0.290
0.867
0.323
Silver Bow
9.403
0.593
0.079
0.348
0.682
0.401
Stillwater
5.569
0.721
0.154
0.419
0.634
0.337
Sweet Grass
6.610
0.774
0.125
0.315
0.723
0.330
Teton
12.979
0.766
0.106
0.386
0.865
0.432
Toole
11.510
0.575
0.048
0.299
0.773
0.263
Treasure
0.578
0.752
0.093
0.232
0.405
0.267
Valley
5.150
0.688
0.266
0.450
0.912
0.407
Wheatland
3.603
0.657
0.109
0.268
0.574
0.308
Wibaux
1.488
0.661
0.168
0.197
0.573
0.296
Yellowstone
4.539
0.631
0.254
0.696
0.635
0.294
North Dakota
6.297
0.704
0.150
0.374
0.662
0.354
Adams
3.390
0.692
0.183
0.311
0.743
0.248
Barnes
8.808
0.744
0.121
0.445
0.711
0.345
Benson
3.761
0.714
0.223
0.408
0.484
0.461
Billings
3.551
0.704
0.171
0.256
0.470
0.511
Bottineau
8.025
0.636
0.121
0.391
0.743
0.417
Bowman
11.045
0.773
0.107
0.327
0.990
0.291
Burke
4.497
0.679
0.108
0.307
0.637
0.262
Burleigh
2.759
0.632
0.361
0.530
0.727
0.300
Cass
5.270
0.676
0.268
0.707
0.623
0.400
Cavalier
10.329
0.718
0.122
0.363
0.867
0.442
Dickey
6.871
0.665
0.123
0.363
0.716
0.367
Divide
6.620
0.509
0.082
0.330
0.814
0.238
Dunn
7.432
0.854
0.139
0.304
0.771
0.358
Eddy
3.112
0.655
0.103
0.216
0.496
0.382
Emmons
7.810
0.715
0.117
0.348
0.843
0.282
Foster
4.951
0.665
0.154
0.303
0.674
0.413
Golden Valley
5.864
0.726
0.095
0.251
0.568
0.400
Grand Forks
4.824
0.687
0.210
0.525
0.581
0.379
Grant
1.695
0.792
0.184
0.276
0.496
0.277
Griggs
3.420
0.773
0.157
0.304
0.620
0.271
274
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Hettinger
5.211
0.799
0.122
0.279
0.719
0.261
Kidder
2.434
0.674
0.197
0.301
0.641
0.265
LaMoure
7.631
0.750
0.116
0.405
0.655
0.333
Logan
4.278
0.869
0.163
0.308
0.695
0.250
McHenry
10.658
0.777
0.092
0.405
0.681
0.343
Mcintosh
7.004
0.768
0.113
0.318
0.736
0.300
McKenzie
8.078
0.740
0.139
0.418
0.671
0.443
McLean
12.784
0.736
0.115
0.510
0.843
0.399
Mercer
7.241
0.645
0.125
0.487
0.676
0.314
Morton
4.452
0.723
0.270
0.585
0.757
0.252
Mountrail
5.548
0.666
0.138
0.412
0.575
0.376
Nelson
5.221
0.769
0.209
0.384
0.735
0.386
Oliver
2.851
0.777
0.107
0.279
0.490
0.279
Pembina
14.560
0.741
0.110
0.483
0.900
0.445
Pierce
6.007
0.676
0.087
0.257
0.657
0.323
Ramsey
6.282
0.669
0.174
0.344
0.873
0.408
Ransom
5.189
0.729
0.168
0.335
0.767
0.324
Renville
2.631
0.645
0.170
0.266
0.609
0.321
Richland
5.391
0.502
0.139
0.469
0.689
0.360
Rolette
10.493
0.660
0.090
0.376
0.646
0.472
Sargent
2.723
0.725
0.303
0.330
0.729
0.336
Sheridan
1.439
0.717
0.105
0.225
0.371
0.359
Sioux
0.750
0.757
0.261
0.276
0.065
0.567
Slope
1.008
0.691
0.172
0.183
0.378
0.415
Stark
9.522
0.626
0.100
0.510
0.742
0.289
Steele
0.736
0.582
0.305
0.296
0.381
0.345
Stutsman
10.604
0.667
0.100
0.496
0.723
0.345
Towner
7.883
0.701
0.093
0.339
0.618
0.377
Traill
8.261
0.776
0.135
0.387
0.744
0.383
Walsh
10.240
0.726
0.102
0.416
0.709
0.383
Ward
13.230
0.652
0.093
0.631
0.656
0.373
Wells
9.729
0.713
0.092
0.345
0.727
0.371
Williams
9.614
0.645
0.098
0.520
0.665
0.313
South Dakota
5.682
0.703
0.142
0.314
0.608
0.377
Aurora
6.860
0.724
0.090
0.252
0.824
0.229
Beadle
4.584
0.632
0.145
0.360
0.753
0.248
Bennett
2.133
0.557
0.151
0.212
0.330
0.555
Bon Homme
10.296
0.759
0.097
0.341
0.776
0.358
Brookings
8.765
0.613
0.103
0.463
0.634
0.403
Brown
7.951
0.746
0.143
0.462
0.819
0.278
275
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Brule
5.575
0.730
0.144
0.267
0.767
0.358
Buffalo
-1.278
0.766
0.120
0.248
0.011
0.454
Butte
3.198
0.675
0.160
0.270
0.625
0.330
Campbell
-1.077
0.674
0.078
0.204
0.306
0.297
Charles Mix
7.444
0.739
0.129
0.383
0.574
0.466
Clark
7.588
0.802
0.089
0.246
0.640
0.377
Clay
6.398
0.630
0.067
0.325
0.508
0.333
Codington
12.077
0.732
0.086
0.380
0.739
0.387
Corson
2.178
0.737
0.162
0.287
0.135
0.590
Custer
5.075
0.598
0.198
0.395
0.713
0.497
Davison
4.673
0.622
0.155
0.365
0.772
0.275
Day
17.070
0.776
0.079
0.333
0.886
0.456
Deuel
15.918
0.807
0.055
0.335
0.636
0.382
Dewey
2.902
0.594
0.178
0.311
0.326
0.569
Douglas
3.224
0.782
0.176
0.272
0.711
0.246
Edmunds
9.820
0.649
0.088
0.332
0.849
0.319
Fall River
3.314
0.642
0.199
0.270
0.648
0.419
Faulk
5.685
0.741
0.080
0.226
0.611
0.329
Grant
14.782
0.756
0.086
0.372
0.793
0.460
Gregory
3.235
0.772
0.151
0.258
0.588
0.322
Haakon
1.770
0.603
0.275
0.254
0.721
0.290
Hamlin
12.392
0.691
0.061
0.331
0.696
0.341
Hand
4.960
0.725
0.117
0.238
0.674
0.341
Hanson
5.115
0.858
0.089
0.291
0.462
0.344
Harding
1.408
0.610
0.212
0.234
0.519
0.345
Hughes
10.621
0.754
0.120
0.394
0.855
0.391
Hutchinson
5.296
0.686
0.156
0.355
0.756
0.314
Hyde
2.385
0.671
0.076
0.223
0.511
0.273
Jackson
1.875
0.667
0.229
0.281
0.384
0.464
Jerauld
1.664
0.703
0.221
0.226
0.593
0.308
Jones
3.866
0.760
0.113
0.255
0.612
0.282
Kingsbury
7.736
0.753
0.144
0.334
0.840
0.381
Lake
8.827
0.623
0.089
0.315
0.701
0.427
Lawrence
4.237
0.665
0.256
0.431
0.639
0.498
Lincoln
4.209
0.677
0.252
0.491
0.691
0.366
Lyman
3.852
0.770
0.153
0.338
0.488
0.369
Marshall
4.970
0.767
0.159
0.302
0.549
0.468
McCook
6.654
0.750
0.158
0.356
0.830
0.334
McPherson
3.915
0.777
0.092
0.257
0.421
0.387
Meade
4.353
0.652
0.212
0.443
0.732
0.323
Mellette
0.276
0.494
0.189
0.226
0.300
0.373
276
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Miner
2.501
0.612
0.077
0.249
0.528
0.250
Minnehaha
3.177
0.655
0.324
0.597
0.647
0.289
Moody
12.228
0.847
0.076
0.269
0.618
0.474
Oglala Lakota
2.567
0.581
0.153
0.394
0.131
0.557
Pennington
4.935
0.648
0.264
0.649
0.649
0.410
Perkins
2.156
0.801
0.208
0.178
0.741
0.257
Potter
0.795
0.728
0.081
0.069
0.617
0.282
Roberts
20.901
0.830
0.084
0.380
0.784
0.638
Sanborn
0.296
0.652
0.140
0.200
0.415
0.291
Spink
9.850
0.722
0.086
0.339
0.754
0.320
Stanley
6.205
0.818
0.137
0.303
0.691
0.355
Sully
4.497
0.615
0.094
0.216
0.654
0.337
Todd
3.055
0.582
0.142
0.336
0.173
0.613
Tripp
2.502
0.648
0.150
0.241
0.633
0.282
Turner
6.513
0.799
0.185
0.345
0.841
0.381
Union
8.561
0.703
0.110
0.427
0.634
0.392
Walworth
7.781
0.672
0.087
0.240
0.777
0.340
Yankton
6.984
0.730
0.133
0.319
0.771
0.368
Ziebach
0.753
0.828
0.231
0.261
0.134
0.508
Utah
7.259
0.670
0.211
0.495
0.617
0.463
Beaver
10.117
0.727
0.135
0.552
0.590
0.507
Box Elder
7.366
0.755
0.167
0.599
0.635
0.319
Cache
4.753
0.664
0.267
0.541
0.638
0.494
Carbon
6.668
0.584
0.120
0.438
0.564
0.444
Daggett
6.685
0.761
0.128
0.311
0.404
0.610
Davis
1.357
0.639
0.711
0.424
0.655
0.445
Duchesne
14.008
0.761
0.075
0.423
0.589
0.449
Emery
9.925
0.631
0.125
0.540
0.663
0.495
Garfield
7.351
0.657
0.146
0.493
0.615
0.454
Grand
5.433
0.523
0.147
0.372
0.606
0.547
Iron
8.863
0.631
0.136
0.610
0.592
0.457
Juab
8.609
0.780
0.148
0.436
0.783
0.380
Kane
9.396
0.620
0.107
0.412
0.688
0.479
Millard
11.180
0.698
0.112
0.576
0.578
0.455
Morgan
6.623
0.673
0.155
0.462
0.701
0.371
Piute
6.188
0.678
0.103
0.252
0.399
0.605
Rich
6.337
0.541
0.110
0.393
0.604
0.427
Salt Lake
1.573
0.667
0.775
0.768
0.612
0.236
San Juan
8.578
0.642
0.127
0.481
0.617
0.492
Sanpete
8.578
0.712
0.137
0.446
0.681
0.460
Ill
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Sevier
9.782
0.729
0.134
0.463
0.683
0.496
Summit
7.613
0.671
0.156
0.570
0.606
0.429
Tooele
6.933
0.595
0.141
0.615
0.565
0.365
Uintah
12.073
0.741
0.117
0.518
0.601
0.546
Utah
3.145
0.678
0.462
0.706
0.591
0.446
Wasatch
7.740
0.587
0.147
0.513
0.637
0.539
Washington
6.026
0.701
0.266
0.612
0.619
0.585
Wayne
5.941
0.711
0.204
0.367
0.750
0.511
Weber
1.680
0.669
0.569
0.459
0.630
0.393
Wyoming
8.236
0.659
0.142
0.464
0.658
0.433
Albany
7.714
0.625
0.110
0.545
0.502
0.372
Big Horn
7.683
0.770
0.115
0.313
0.681
0.394
Campbell
5.632
0.675
0.227
0.683
0.634
0.335
Carbon
16.706
0.676
0.094
0.626
0.685
0.533
Converse
10.506
0.639
0.101
0.516
0.677
0.389
Crook
3.565
0.616
0.201
0.376
0.709
0.311
Fremont
9.572
0.670
0.132
0.589
0.602
0.464
Goshen
4.186
0.630
0.119
0.366
0.621
0.242
Hot Springs
3.438
0.668
0.189
0.218
0.622
0.476
Johnson
7.968
0.654
0.143
0.395
0.873
0.395
Laramie
4.868
0.619
0.171
0.566
0.599
0.268
Lincoln
10.437
0.685
0.155
0.573
0.755
0.549
Natrona
9.356
0.613
0.129
0.566
0.639
0.489
Niobrara
1.165
0.689
0.257
0.250
0.505
0.316
Park
10.395
0.676
0.145
0.557
0.717
0.542
Platte
6.378
0.600
0.123
0.409
0.658
0.375
Sheridan
8.432
0.576
0.125
0.468
0.706
0.484
Sublette
8.421
0.596
0.153
0.499
0.705
0.590
Sweetwater
12.194
0.666
0.107
0.573
0.602
0.505
Teton
10.915
0.719
0.154
0.573
0.721
0.567
Uinta
16.507
0.678
0.063
0.472
0.586
0.458
Washakie
9.653
0.682
0.099
0.260
0.725
0.520
Weston
3.737
0.743
0.168
0.268
0.610
0.378
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 9
5.524
0.551
0.235
0.620
0.480
0.469
Arizona
7.359
0.613
0.183
278
0.710
0.710
0.410
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Apache
10.466
0.650
0.125
0.741
0.443
0.473
Cochise
8.158
0.586
0.123
0.726
0.470
0.348
Coconino
13.333
0.650
0.132
0.906
0.573
0.472
Gila
7.544
0.556
0.112
0.528
0.495
0.463
Graham
7.635
0.625
0.108
0.427
0.540
0.457
Greenlee
6.192
0.572
0.130
0.378
0.481
0.590
La Paz
3.008
0.549
0.187
0.547
0.363
0.346
Maricopa
2.111
0.660
0.662
0.942
0.471
0.276
Mohave
8.288
0.632
0.122
0.830
0.365
0.275
Navajo
11.310
0.602
0.131
0.804
0.513
0.534
Pima
7.001
0.649
0.225
0.895
0.454
0.465
Pinal
4.616
0.594
0.249
0.873
0.389
0.343
Santa Cruz
5.316
0.640
0.123
0.420
0.399
0.456
Yavapai
9.728
0.612
0.131
0.896
0.504
0.286
Yuma
5.677
0.610
0.179
0.731
0.415
0.368
California
3.897
0.498
0.279
0.641
0.485
0.461
Alameda
1.436
0.394
0.501
0.720
0.549
0.338
Alpine
2.560
0.749
0.318
0.370
0.276
0.633
Amador
2.698
0.523
0.191
0.427
0.489
0.360
Butte
3.938
0.484
0.211
0.721
0.397
0.414
Calaveras
4.227
0.464
0.131
0.494
0.553
0.320
Colusa
2.453
0.482
0.147
0.390
0.359
0.410
Contra Costa
1.175
0.403
0.651
0.734
0.525
0.371
Del Norte
3.443
0.521
0.191
0.407
0.374
0.586
El Dorado
4.823
0.557
0.209
0.623
0.538
0.444
Fresno
5.014
0.470
0.241
0.960
0.478
0.435
Glenn
3.272
0.552
0.149
0.367
0.379
0.471
Humboldt
8.059
0.462
0.148
0.805
0.511
0.580
Imperial
9.285
0.654
0.155
0.894
0.379
0.438
Inyo
5.004
0.571
0.253
0.605
0.668
0.525
Kern
4.615
0.433
0.213
0.990
0.389
0.350
Kings
2.265
0.580
0.201
0.632
0.345
0.172
Lake
1.676
0.195
0.160
0.490
0.360
0.552
Lassen
11.003
0.632
0.117
0.630
0.529
0.535
Los Angeles
1.936
0.486
0.576
0.881
0.535
0.363
Madera
4.165
0.437
0.182
0.695
0.451
0.403
Marin
5.032
0.447
0.159
0.414
0.545
0.656
Mariposa
1.296
0.229
0.272
0.476
0.515
0.512
Mendocino
6.181
0.466
0.157
0.723
0.533
0.452
Merced
2.967
0.583
0.250
279
0.688
0.362
0.296
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Modoc
4.731
0.534
0.139
0.435
0.434
0.498
Mono
12.921
0.525
0.089
0.586
0.644
0.553
Monterey
2.231
0.175
0.187
0.809
0.493
0.513
Napa
1.240
0.307
0.370
0.458
0.518
0.512
Nevada
5.594
0.573
0.198
0.596
0.536
0.525
Orange
1.317
0.543
0.763
0.736
0.570
0.312
Placer
2.994
0.618
0.424
0.711
0.575
0.418
Plumas
3.901
0.539
0.291
0.635
0.527
0.561
Riverside
3.496
0.591
0.453
0.934
0.488
0.500
Sacramento
1.907
0.568
0.626
0.763
0.590
0.370
San Benito
4.763
0.583
0.124
0.427
0.453
0.402
San Bernandino
7.519
0.589
0.215
0.985
0.480
0.479
San Diego
3.767
0.542
0.386
0.907
0.520
0.507
San Francisco
5.591
0.792
0.240
0.429
0.530
0.614
San Joaquin
1.624
0.590
0.564
0.724
0.447
0.304
San Luis Obispo
2.721
0.452
0.439
0.861
0.606
0.471
San Mateo
3.954
0.423
0.186
0.557
0.539
0.485
Santa Barbara
7.179
0.556
0.224
0.830
0.559
0.645
Santa Clara
2.166
0.406
0.363
0.682
0.552
0.420
Santa Cruz
4.203
0.454
0.173
0.382
0.532
0.625
Shasta
6.492
0.416
0.134
0.799
0.462
0.429
Sierra
4.434
0.556
0.157
0.366
0.406
0.600
Siskiyou
2.107
0.395
0.398
0.740
0.541
0.440
Solano
1.509
0.433
0.455
0.607
0.522
0.383
Sonoma
3.670
0.381
0.251
0.783
0.564
0.497
Stanislaus
1.623
0.503
0.477
0.717
0.432
0.317
Sutter
2.546
0.571
0.192
0.470
0.437
0.303
Tehama
2.773
0.560
0.299
0.550
0.406
0.495
Trinity
2.548
0.489
0.271
0.428
0.405
0.600
Tulare
4.284
0.351
0.186
0.895
0.440
0.415
Tuolumne
3.730
0.417
0.171
0.570
0.476
0.438
Ventura
3.766
0.534
0.384
0.751
0.550
0.660
Yolo
2.664
0.538
0.317
0.564
0.514
0.429
Yuba
1.534
0.576
0.233
0.372
0.362
0.374
Hawaii
16.531
0.739
0.092
0.570
0.589
0.479
Hawaii
17.909
0.781
0.107
0.740
0.626
0.508
Honolulu
15.570
0.860
0.147
0.698
0.639
0.631
Kalawao
1.492
0.421
0.068
0.260
0.308
0.383
Kauai
20.062
0.789
0.074
0.559
0.733
0.383
Maui
27.620
0.846
0.063
280
0.595
0.640
0.492
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Nevada
6.218
0.623
0.172
0.485
0.446
0.548
Carson City
1.959
0.576
0.331
0.224
0.594
0.554
Churchill
10.613
0.717
0.116
0.544
0.519
0.505
Clark
5.389
0.631
0.316
0.901
0.458
0.569
Douglas
6.272
0.644
0.175
0.490
0.567
0.522
Elko
9.414
0.546
0.130
0.675
0.515
0.583
Esmeralda
1.220
0.579
0.101
0.282
0.010
0.586
Eureka
5.199
0.597
0.109
0.407
0.269
0.544
Humboldt
10.544
0.665
0.108
0.522
0.481
0.561
Lander
3.997
0.600
0.119
0.245
0.397
0.554
Lincoln
4.109
0.635
0.252
0.447
0.529
0.569
Lyon
5.634
0.712
0.207
0.527
0.434
0.562
Mineral
6.312
0.628
0.144
0.454
0.477
0.530
Nye
10.078
0.630
0.129
0.657
0.427
0.598
Pershing
3.517
0.567
0.154
0.389
0.321
0.524
Storey
1.695
0.688
0.266
0.309
0.498
0.347
Washoe
9.208
0.643
0.183
0.841
0.517
0.552
White Pine
10.550
0.528
0.082
0.333
0.575
0.658
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Region 10
15.395
0.660
0.137
0.478
0.492
0.531
Alaska
57.225
0.736
0.038
0.475
0.479
0.627
Aleutians East
122.319
0.762
0.012
0.342
0.305
0.990
Aleutians West
Anchorage
28.143
0.795
0.039
0.479
0.424
0.523
Municipality
25.800
0.990
0.078
0.693
0.557
0.443
Bristol Bay
34.581
0.948
0.034
0.307
0.234
0.795
Denali
3.871
0.706
0.116
0.413
0.309
0.377
Dillingham
41.835
0.649
0.022
0.489
0.428
0.508
Fairbanks North Star
31.851
0.778
0.050
0.665
0.579
0.455
Haines
65.906
0.779
0.021
0.371
0.596
0.664
Hoonah-Angoon
66.480
0.638
0.010
0.416
0.357
0.502
Juneau City and
154.022
0.777
0.013
0.561
0.731
0.686
Kenai Peninsula
34.426
0.781
0.053
0.713
0.630
0.482
Ketchikan Gateway
145.556
0.743
0.010
281
0.464
0.617
0.614
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Kodiak Island
189.172
0.702
0.010
0.557
0.474
0.928
Lake and Peninsula
20.715
0.606
0.045
0.488
0.247
0.718
North Slope
51.674
0.638
0.038
0.747
0.365
0.959
Petersburg
Prince of Wales-
39.241
0.762
0.026
0.318
0.604
0.538
Hyder
55.456
0.608
0.018
0.573
0.443
0.487
Sitka City and
Skagway
45.338
0.767
0.036
0.398
0.681
0.709
Municipality
30.968
0.944
0.028
0.245
0.520
0.501
Valdez-Cordova
11.163
0.641
0.126
0.689
0.603
0.475
Wrangell City and
33.133
0.523
0.011
0.269
0.373
0.520
Yakutat City and
27.305
0.662
0.044
0.256
0.449
0.924
Idaho
8.774
0.666
0.137
0.420
0.545
0.537
Ada
3.770
0.625
0.345
0.562
0.580
0.586
Adams
12.908
0.720
0.093
0.376
0.573
0.627
Bannock
6.919
0.645
0.190
0.519
0.615
0.587
Bear Lake
3.113
0.269
0.102
0.284
0.632
0.480
Benewah
16.565
0.790
0.062
0.326
0.476
0.612
Bingham
7.274
0.595
0.146
0.492
0.626
0.507
Blaine
16.076
0.744
0.101
0.550
0.683
0.563
Boise
8.875
0.624
0.106
0.500
0.313
0.637
Bonner
20.557
0.616
0.064
0.636
0.582
0.531
Bonneville
4.295
0.623
0.282
0.574
0.647
0.465
Boundary
17.607
0.662
0.072
0.443
0.666
0.576
Butte
3.197
0.677
0.141
0.256
0.403
0.485
Camas
2.270
0.894
0.084
0.213
0.171
0.532
Canyon
1.917
0.651
0.431
0.507
0.518
0.366
Caribou
6.409
0.783
0.142
0.332
0.568
0.473
Cassia
11.750
0.660
0.101
0.452
0.644
0.542
Clark
0.944
0.766
0.313
0.243
0.254
0.503
Clearwater
8.102
0.623
0.144
0.417
0.609
0.637
Custer
7.015
0.659
0.173
0.419
0.592
0.636
Elmore
12.465
0.723
0.116
0.549
0.508
0.624
Franklin
6.364
0.616
0.130
0.411
0.529
0.493
Fremont
9.271
0.753
0.131
0.414
0.612
0.531
Gem
8.001
0.697
0.106
0.344
0.514
0.526
Gooding
8.570
0.552
0.102
0.424
0.566
0.542
Idaho
12.923
0.720
0.108
0.531
0.666
0.496
Jefferson
11.000
0.615
0.095
0.412
0.673
0.524
Jerome
7.811
0.667
0.133
0.514
0.485
0.504
282
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Kootenai
10.106
0.640
0.151
0.725
0.573
0.538
Latah
21.018
0.634
0.046
0.488
0.538
0.473
Lemhi
7.246
0.618
0.117
0.347
0.694
0.439
Lewis
6.810
0.857
0.177
0.285
0.458
0.710
Lincoln
5.931
0.623
0.151
0.270
0.536
0.676
Madison
6.932
0.799
0.157
0.430
0.542
0.469
Minidoka
8.853
0.659
0.093
0.333
0.487
0.571
Nez Perce
12.597
0.689
0.105
0.480
0.612
0.582
Oneida
4.510
0.669
0.146
0.246
0.546
0.510
Owyhee
7.633
0.675
0.106
0.367
0.430
0.559
Payette
4.066
0.568
0.135
0.359
0.440
0.465
Power
5.356
0.610
0.115
0.349
0.476
0.465
Shoshone
6.408
0.718
0.085
0.261
0.490
0.449
Teton
9.913
0.782
0.124
0.427
0.622
0.492
Twin Falls
8.948
0.562
0.125
0.592
0.599
0.501
Valley
17.870
0.652
0.088
0.506
0.812
0.602
Washington
5.882
0.568
0.101
0.321
0.434
0.542
Oregon
7.145
0.618
0.149
0.499
0.465
0.517
Baker
11.961
0.600
0.089
0.475
0.554
0.580
Benton
5.144
0.669
0.176
0.468
0.432
0.511
Clackamas
7.105
0.576
0.189
0.698
0.562
0.554
Clatsop
6.299
0.604
0.098
0.412
0.461
0.415
Columbia
3.875
0.686
0.167
0.400
0.408
0.438
Coos
5.082
0.511
0.124
0.509
0.411
0.433
Crook
6.593
0.622
0.130
0.416
0.517
0.509
Curry
2.809
0.568
0.253
0.353
0.489
0.550
Deschutes
13.496
0.745
0.123
0.661
0.561
0.553
Douglas
6.993
0.515
0.163
0.837
0.429
0.461
Gilliam
6.547
0.690
0.066
0.287
0.370
0.461
Grant
11.587
0.814
0.108
0.372
0.521
0.616
Harney
8.136
0.584
0.100
0.373
0.489
0.584
Hood River
5.421
0.495
0.147
0.392
0.552
0.601
Jackson
7.281
0.559
0.159
0.727
0.428
0.526
Jefferson
6.224
0.618
0.125
0.485
0.391
0.487
Josephine
4.082
0.437
0.142
0.477
0.399
0.518
Klamath
8.713
0.588
0.121
0.703
0.435
0.435
Lake
6.540
0.591
0.117
0.431
0.437
0.524
Lane
8.488
0.648
0.180
0.871
0.485
0.440
Lincoln
5.446
0.601
0.138
0.470
0.421
0.477
Linn
6.444
0.556
0.167
283
0.646
0.486
0.510
-------
Area
, Built _ . Natural
CRSI Governance Risk Society
Environment Environment
Malheur
13.153
0.728
0.098
0.506
0.450
0.625
Marion
5.282
0.514
0.184
0.628
0.531
0.477
Morrow
5.703
0.644
0.099
0.428
0.396
0.390
Multnomah
2.196
0.545
0.419
0.559
0.476
0.514
Polk
5.243
0.647
0.161
0.440
0.467
0.492
Sherman
8.020
0.782
0.055
0.269
0.279
0.529
Tillamook
6.278
0.631
0.155
0.487
0.500
0.515
Umatilla
12.495
0.675
0.112
0.697
0.501
0.500
Union
12.628
0.686
0.096
0.468
0.552
0.586
Wallowa
10.140
0.658
0.120
0.322
0.698
0.659
Wasco
12.989
0.640
0.078
0.496
0.465
0.548
Washington
1.983
0.562
0.405
0.419
0.501
0.540
Wheeler
2.760
0.761
0.081
0.210
0.232
0.519
Yamhill
4.073
0.510
0.207
0.561
0.445
0.522
Washington
6.883
0.648
0.182
0.524
0.465
0.485
Adams
5.951
0.684
0.080
0.422
0.329
0.375
Asotin
13.269
0.635
0.054
0.332
0.475
0.533
Benton
6.182
0.567
0.170
0.613
0.477
0.521
Chelan
10.756
0.701
0.128
0.590
0.550
0.530
Clallam
10.133
0.584
0.085
0.508
0.514
0.451
Clark
1.590
0.550
0.447
0.460
0.478
0.458
Columbia
2.910
0.526
0.165
0.235
0.510
0.524
Cowlitz
6.855
0.655
0.120
0.501
0.412
0.450
Douglas
3.731
0.693
0.254
0.490
0.416
0.506
Ferry
6.396
0.691
0.122
0.338
0.351
0.626
Franklin
4.886
0.668
0.158
0.461
0.486
0.396
Garfield
3.381
0.646
0.101
0.226
0.397
0.466
Grant
8.958
0.657
0.155
0.699
0.458
0.551
Grays Harbor
9.313
0.616
0.096
0.597
0.400
0.436
Island
4.087
0.820
0.192
0.374
0.456
0.432
Jefferson
4.164
0.437
0.152
0.468
0.518
0.479
King
3.328
0.686
0.527
0.864
0.521
0.501
Kitsap
2.375
0.619
0.266
0.499
0.443
0.335
Kittitas
6.399
0.735
0.200
0.575
0.499
0.498
Klickitat
9.349
0.654
0.119
0.558
0.503
0.497
Lewis
7.340
0.641
0.144
0.613
0.451
0.460
Lincoln
9.452
0.687
0.114
0.524
0.624
0.385
Mason
7.566
0.563
0.101
0.499
0.406
0.500
Okanogan
8.339
0.665
0.138
0.609
0.503
0.454
Pacific
5.751
0.594
0.092
0.448
0.387
0.382
284
-------
Area
CRSI
Governance
Risk
Built
Environment
Society
Natural
Environment
Pend Oreille
13.128
0.697
0.081
0.447
0.430
0.603
Pierce
2.392
0.659
0.571
0.727
0.489
0.476
San Juan
15.937
0.716
0.095
0.432
0.638
0.694
Skagit
9.140
0.675
0.141
0.569
0.507
0.570
Skamania
6.999
0.668
0.124
0.391
0.406
0.593
Snohomish
6.181
0.634
0.263
0.722
0.541
0.641
Spokane
4.593
0.650
0.272
0.715
0.513
0.408
Stevens
10.228
0.659
0.108
0.581
0.500
0.466
Thurston
2.187
0.696
0.531
0.564
0.522
0.464
Wahkiakum
-0.727
0.595
0.179
0.153
0.202
0.396
Walla Walla
7.648
0.657
0.103
0.543
0.432
0.369
Whatcom
7.693
0.638
0.188
0.694
0.533
0.557
Whitman
12.743
0.677
0.086
0.703
0.402
0.391
Yakima
7.823
0.661
0.174
0.705
0.438
0.538
285
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oEPA
United States
Environment;
Agency
PRESORTED STANDARD
POSTAGE and FEES
PAID EPA
PERMIT NO. G-35
Office of Research and
Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
286
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