EPA's Smart Growth INDEX
In 20 Pilot Communities:
Using GIS Sketch Modeling to Advance
Smart Growth
United States
Environmental Protection
Agency
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Acknowledgments
This report was prepared by the U.S. EPA's Development, Community, and Environment
Division (DCED). Division staff Carlton Eley, Christopher Forinash, Adhir Kackar, Amber
Levofsky, liana Preuss, Mary Kay Santore, and Eric Sprague contributed to this report.
Thanks are extended to the communities who have participated in EPA's Smart Growth
INDEX pilot program; their experiences have paved the way for future users.
For additional copies of this report, please call 202 566-2878 and ask for EPA's Smart
Growth INDEX in 20 Pilot Communities (EPA 231 -R-03-001). A PDF version of this report
will be available online at »http://www.epa.gov/smartgrowth/«.
Office of Policy, Economics, and Innovation (1808T)
Development, Community, and Environment Division
EPA 231 -R-03-001
www.epa.gov/smartgrowth
February 2003
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Table of Contents
Section I: Introduction 1
About EPA's Smart Growth INDEX (SGI) Pilot Program 2
About the Pilot Communities 3
Section II: The Smart Growth INDEX (SGI) Model 4
The Forecast and Snapshot Modes of SGI Version 1 4
Data and Resource Requirements for SGI Version 1 5
SGI Indicators 6
Section III: Smart Growth INDEX Pilot Projects 10
Overview Statistics 10
Profiles of Selected SGI Pilot Projects 11
Pilot Case #1: Fells Point-Baltimore (Digital) Harbor, Baltimore, Maryland 12
Pilot Case #2: Broadway Corridor, San Antonio, Texas 14
Pilot Case #3: New Castle County, Delaware, and Cecil County, Maryland 17
(Wilmington, Delaware, area)
Pilot Case #4: Berkeley-Charleston-Dorchester Counties, Charleston, .... 19
South Carolina region
Pilot Case #5: Two Harbors, Minnesota 22
Pilot Case #6: Hudson, Massachusetts (Boston Area) 24
Pilot Case #7: Indianapolis and Marion County, Indiana 26
Section IV: Performance Highlights and Lessons Learned: Launching SGI Version 2 27
Strengths of SGI Version 1 27
Recommended Improvements to SGI Version 1 27
SGI Version 2: Program Improvements and Phase 2 Partner Selection 30
List of Tables
Table 1: SGI Version 1 Snapshot Sketch Indicators 7
Table 2: SGI Version 1 Forecast Sketch Indicators 8
List of Figures
Figure 1: Location of SGI Pilots 10
Figure 2: Pilot Sites, by Recipient Type 10
Figures: SGI Pilots, by Project Type 11
Figure4: SGI Modes Used by Pilots 11
Figure 5: Sample Readout from Fells Point Pilot Project (Baltimore, Maryland) 13
Figure 6: SGI Indicator Scores: Broadway Corridor Project (San Antonio, Texas) 16
Figure 7: "Use Mix" Indicator Readout for Base Scenario: New Castle and Cecil 17
Counties Project
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Figure 8: Map Illustrating Existing Housing Density: Berkeley-Charleston-Dorchester 20
Counties, South Carolina, Project
Figure 9: Map Showing Future Housing Density Scenario: Berkeley-Charleston- 21
Dorchester Counties, South Carolina, Project
Figure 10: Map of Existing Land Uses in Two Harbors, Minnesota 23
Figure 11: Map Illustrating Existing Pedestrian Route Directness in Hudson, 25
Massachusetts
Appendices
Appendix A: Sample SGI Readouts 33
Appendix B: Short Summaries of Additional 13 SGI Pilot Projects 36
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Section I: Introduction
/ " \ \ mart growth offers localities a wide range of options for handling growth and
J~ ...i development in ways that make positive contributions to communities. By making
L..X '.-'development decisions that reflect smart growth principles (see box) and community
priorities, localities can approach growth-related problems with solutions that serve the
economy, community quality of life, and the environment. Smart growth strategies make it
possible to address problems such as traffic congestion and air pollution, maintain a sense
of place, and minimize expenditures for infrastructure such as water and sewer lines,
roads, and new schools in previously undeveloped locations.
As more communities seek smart growth solutions to growth-
related problems, they need tools that can help them evaluate
their options. Local officials and residents often find themselves
trying to determine how different proposed developments and
transportation improvements might meet their quality-of-life
goals. They may ask themselves —
• "Does this project achieve our goal of increasing transit
ridership?"
• "Will this development create more jobs in the downtown
core?"
• "How will these changes affect our air and water quality?"
Local governments rely on past experience, case studies of
similar places, professional judgement, and sometimes techni-
cal software tools to help answer these questions and to
engage the public in planning discussions.
To help communities answer these kinds of questions, the U.S.
Environmental Protection Agency (EPA) has developed the
Smart Growth INDEX (SGI) model. SGI is a software tool that
allows the user to benchmark existing environmental and com-
munity conditions, compare the impacts of multiple develop-
ment and transportation scenarios, and monitor changes over
time. The program provides clear graphics so that the public
can understand comparable impacts. It allows the public
visioning process to be integrated into the development plan-
ning and environmental protection process. Since July 2000,
more than 35 communities have used SGI in various ways to
enhance their planning processes.
This report describes the first version of the SGI model (in Section II) and summarizes the
experiences of those pilot communities during the initial pilot phase of SGI. Seven are pre-
sented as case studies in Section III; another 13 pilot projects are briefly synopsized in
Appendix B. Section IV discusses the successes achieved and lessons learned through
the first 20 pilot applications, sums up conclusions, briefly discusses the Version 2 update
of SGI, and names new partners who have signed on to use SGI Version 2 in 2003.
Smart Growth Principles
1. Mix land uses
2.
Take advantage of compact building
design
3.
4.
5.
6.
7.
8.
9.
Create a range of housing opportuni-
ties and choices
Create walkable neighborhoods.
Foster distinctive, attractive
communities with a strong sense of
place
Preserve open space, farmland, natu-
ral beauty, and critical environmental
areas
Strengthen and direct development
towards existing communities
Provide a variety of transportation
choices
Make development decisions pre-
dictable, fair, and cost effective
10. Encourage community and stakehold-
er collaboration in development deci-
sions
Source: Smart Growth Network
(For more information, see
»http://www.smartgrowth.org/about/«.)
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About the Smart Growth INDEX Pilot Program
Since 1996, EPA's Development, Community, and Environment Division (DCED), has been
working with national organizations, state and local governments, universities, and the pri-
vate sector to support smart growth — defined as development that serves the economy,
the community, and the environment. EPA's initial approach focused on providing informa-
tion, outreach, and policy analysis to constituencies looking to learn more about the princi-
ples (see box on previous page) and concepts of smart growth. As this knowledge base
grew, so too did the demand for new tools to implement smart growth.
In conversations with stakeholders, many noted a need for analytic tools - tools that
would quantitatively demonstrate the environmental, transportation, and quality-of-life ben-
efits of smart growth projects. In particular, communities stressed a need for a quick-
response tool that would allow them to estimate the impacts of different development sce-
narios and compare them to one another. The communities also sought a quantitative
method to display information and to engage the public regarding future land use trade-
offs. To respond to this interest, EPA developed the Smart Growth INDEX model. The
Agency contracted with Criterion Planners and Engineers in Portland, Oregon, to develop
the software.
In July 2000, after a limited beta-test (trial use) distribution of SGI, EPA's Development,
Community, and Environment Division (DCED) initiated a 20-community pilot program to
work with and evaluate the tool. DCED capped the number at 20 in order to provide sub-
stantial on-demand technical assistance to all pilot users.
Starting in February 2000, DCED gave presentations and sent written announcements to
alert organizations about the SGI pilot program. To apply, candidates were asked to sub-
mit a 1 - to 2-page proposal detailing their intended use for the SGI model, expected
results, staff commitment to the project, and data availability to run SGI.
For this first phase of the pilot program, DCED looked for proposals that demonstrated:
• Strong prospects for better environmental, economic, and community outcomes as a
result of using SGI
• Potential to achieve environmental benefits through smart growth approaches
• Significant contribution of staff time/resources by the local partner
• Potential to improve plans for federal facilities and/or lands through application of SGI
• Potential for SGI to facilitate win-win development outcomes (i.e., less controversial
projects generally preferred)
• Adequate existing GIS coverages — i.e., key areas with the data and resources avail-
able to run SGI, including GIS coverage in shapefile format (ArcView and Arclnfo for-
mat for storing data)
• A strong public participation process for review of SGI output and development of
planning alternatives
1 GIS stands for Geographic Information System. A GIS is an organized collection of computer software and
geographic data designed to display, analyze, and manipulate all forms of geographically referenced data.
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More than 40 proposals were submitted. Working in partnership with EPA Regional offices,
DCED selected pilot communities sequentially on a monthly basis until 20 were chosen. In
evaluating proposals, headquarters and regional staff gave particular emphasis to the fol-
lowing criteria:
• Availability of data to input to SGI
• Commitment of staff time/resources to the project
• Demonstration that planned project could achieve environmental benefits through
smart growth approaches
• Potential of SGI to inform a near-term decision
• Potential for a "win-win" development outcome
The pilot communities first received SGI at the end of July 2000. Each was provided with a
license for the software, an initial training seminar, and ongoing remote and on-site techni-
cal support. No funding was provided to the pilots through this agreement, although many
sites used the model to leverage monetary support elsewhere. Since then, the software
has been modified to correct errors and improve user-friendliness. Updated versions and
service packs have been posted to a secure website that all pilot users can access.
About the Pilot Communities
The 20 communities who took part in the first-phase pilot testing of SGI deserve recogni-
tion as bona fide pioneers on the frontier of smart growth. All new software tools, including
SGI, have initial technical issues and bugs which need to be worked out through user
experience. The SGI pilot users contributed extensive time and effort to this necessary
process. Thanks to their participation, SGI is well on its way to becoming a highly practical
tool for helping communities evaluate their development options and make informed,
strategic decisions.
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Section II: The Smart Growth INDEX Model
I f | he purpose of ERA'S Smart Growth INDEX (SGI) is to enable communities to do
| quick analyses of multiple "what if?" scenarios before running more sophisticated,
J LJ data- and labor-intensive transportation and land use models. Additionally, SGI is
intended to be a public visualization tool. Its easily understood readouts can help the gen-
eral public visualize and compare alternative development scenarios during the decision-
making process.
SGI is a GIS-based sketch model for analyzing alternative land-use and transportation
scenarios and evaluating their outcomes, using environmental and quality-of-life indicators.
As a sketch planning tool, SGI can demonstrate the direction and magnitude of change
and calculate rough estimates of relative impacts; it is not intended to replace more techni-
cal and sophisticated tools used for regulatory purposes. SGI can be used to analyze:
• Regional growth management plans
• Environmental impact changes
• Comprehensive land-use plans
• Transportation plans
• Neighborhood plans
• Land development proposals
• Environmental impact reports
• Special projects — e.g., brownfields redevelopment, annexation proposals
• Proposed indicators of community quality of life and environmental assessment
For any analysis it performs, the SGI software provides a variety of readouts, including
maps, bar charts, and tables that illustrate contrasting scenarios for easy public
understanding.
The Forecast and Snapshot Modes of SGI
SGI operates in two distinct modes: the forecast sketch and the snapshot sketch.
Although the software platform and several required data items are common to both
modes, they function quite differently and are meant to answer very different questions.
The Forecast Mode:
The forecast mode addresses questions like, "Where might growth go in the future, given
these conditions?" It applies population and employment projections provided by the user
to spatially allocate total growth over a decided time horizon. Users, applying given popu-
lation projections, can estimate how and where a community might grow over time by
varying any of the following:
• The community's land use plan
• Environmental constraint areas
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• Infrastructure service areas
• Development incentive areas
• Transportation system capabilities
The user sets various SGI parameters including: allowable densities for various land use
categories; vehicle ownership rates; trip generation rates; transportation level of service
standards, etc. Community-specific data can be used, or if unknown, SGI provides nation-
al-level defaults. All of the elements just mentioned influence the "growth attractiveness" of
a given part of the sketch area. The base attractiveness of a "cell" (an area of 10 to 200
acres) is calculated by its travel accessibility and is increased or diminished according to
development incentives or constraints and the presence or absence of infrastructure
(water and sewer services). Given the relative attractiveness of each cell in the sketch area,
SGI allocates housing and employment growth to appropriate locations.
In the forecast mode, SGI is unique in that it has an internal travel demand "submodel"
that can estimate transportation outcomes from land-use changes without the use of a
traditional four-step transportation model. This feature has made SGI a particularly valuable
tool for communities because it fills an important niche - it allows the user to see rough
estimates of transportation impacts from projects without needing to run a labor-intensive
travel demand model. In addition, SGI in forecast mode can run in tandem with popular
four-step transportation models, instead of relying on its internal travel submodel.
The Snapshot Mode:
The snapshot mode estimates the impacts of known, detailed alternative development
plans. It takes a "snapshot" at a moment in time, whether current or future, generally
applying the analysis to a smaller scale area than the forecast sketch. The "base case"
scenario usually represents existing conditions on a site. The user can examine potential
impacts by varying any of the following:
• Land use designations and densities
• Mix of housing types and job types
• Transportation system characteristics
The user enters various input parameters concerning land use, transportation, and other
conditions. When future snapshot sketches are prepared, the user-specified baseline esti-
mates of per-capita vehicle trips and vehicle miles of travel are adjusted to reflect impacts
of changes in density, jobs/housing balance, and pedestrian design.
Data and Resource Requirements for SGI
Although SGI is less data-intensive than many models, the data requirements are nonethe-
less significant. In a setting where land and travel data are not routinely maintained in a
GIS environment, first-time development of needed data can consume considerable
resources. In localities which have already invested in such basic data, the accumulation of
files and development of other inputs can and does occur quite quickly.
SGI Version 1 requires GIS coverages, including the following data, in "ESRI shapefile" for-
mat (an industry standard for data exchange):
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• Existing land-use designations by class (parcel-based, for snapshot mode only)
• Planned land use designations by class (typically from comprehensive plans, not par-
cel level of detail)
• Existing housing (single family or multifamily) locations by point (note: point implies the
address-specific location of a single residential structure) (forecast mode only)
• Existing employment by type (retail, service, or other) and job count in point format
• Existing and future street centerlines attributed by functional class, number of lanes,
and sidewalk presence
• Transit routes (bus or rail) and transit stops for snapshot sketches
Once installed, SGI snapshot is suitable for non-technical users with moderate computer
skills. Use of the forecast mode requires higher skill levels, both in model comprehension
and GIS. Installation and maintenance require an experienced model steward with GIS and
transportation modeling experience. At a minimum, SGI requires a 300- MHZ or higher PC
with 128 MB of RAM, and Microsoft Windows 95 or later operating systems.
SGI Indicators
SGI helps communities assess development scenarios by scoring projects with indicators
that measure a host of prospective impacts. These include: land consumption, pollutant
emissions and other environmental consequences, housing and employment density,
proximity to transit, and travel costs, among other things. The software can also produce
maps to illustrate geographical variations in indicator outcomes; such maps are often use-
ful as visualization aids for public forums. SGI Version 1 scores sketches using either 26
indicators (forecast sketch mode) or 29 indicators (snapshot sketch mode). Land alloca-
tions can be tabulated for land-use classes and local jurisdictions. The forecast and snap-
shot mode indicators are respectively given in Tables 1 and 2 below. Additional details
regarding the indicators are available from the SGI Reference Guide at
»http://www.epa.gov/smartgrowth/« (click on "Browse Smart Growth Topics," then on
"Smart Growth INDEX").
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Table 1. SGI Version 1 Forecast Sketch Indicators
Indicator
1. Population density
2. Growth compactness
3. Incentive area use for housing
4. Incentive area use for employment
5. Housing density
6. Housing transit proximity
7. Residential energy use
8. Residential water use
9. Employment density
10. Employment transit proximity
11. Jobs/housed workers balance
12. Vehicle trips
13. Vehicle miles traveled
14. Vehicle hours traveled
15. Vehicle hours of delay
16. Single-occupant vehicle mode share
17. Auto passenger mode share
18. Transit mode share
19. Walk/bike mode share
20. Auto travel cost
21. Nitrogen oxides vehicle emissions
22. Sulphur oxides vehicle emissions
23. Hydrocarbon vehicle emissions
24. Carbon monoxide vehicle emissions
25. Particulate matter vehicle emissions
26. Carbon dioxide vehicle emissions
Expressed As
Persons/square mile
Persons/square mile
Percent of total housing capacity
utilized in incentive areas (as entered by
user)
Percent of total employment capacity uti-
lized in designated incentive areas (as
entered by user)
Dwelling units (DU)/gross acre
Percent of all DU within 1/4 mile of transit
route
MMBtu/year/capita for housing and auto
travel
Gallons/day/capita
Employees/gross acre
Percent of all employees within 1/4 mile of
transit route
Ratio of total jobs to total housed work-
ers, assuming a constant 1.4 workers per
household
Vehicle trips taken/day/capita
Miles driven/day/capita
Hours driving time/day/capita
Hours delayed in traff ic/day/capita
Percent of total daily person trips by auto
driver
Percent of total daily person trips by auto
passengers
Percent of total daily person trips by tran-
sit
Percent of total daily person trips on foot
or bike
Dollars/year/capita
Pounds/year/capita
Pounds/year/capita
Pounds/year/capita
Pounds/year/capita
Pounds/year/capita
Tons/year/capita
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Table 2. SGI Version 1 Snapshot Sketch Indicators
Indicator
1. Population density
2. Use mix
3. Land-use diversity
4. Residential density
5. Single-family housing share
6. Multi-family housing share
7. Housing proximity to transit
8. Housing proximity to recreation
9. Residential energy consumption
10. Residential water consumption
11. Jobs/housed workers balance
12. Employment density
13. Employment proximity to transit
14. Park space availability
15. Open space
16. Sidewalk completeness
17. Pedestrian route directness
18. Street network density
19. Street connectivity
20. Pedestrian environment design
21. Vehicle trips
22. Vehicle miles traveled
23. Auto travel cost
24. Carbon monoxide vehicle emissions
25. Hydrocarbon vehicle emissions
26. Sulphur oxides vehicle emissions
27. Particulate matter vehicle emissions
28. Carbon dioxide vehicle emissions
29. Nitrogen oxides vehicle emissions
Expressed As
Persons/square mile
INDEX of use dissimilarity among one-acre
grid cells
INDEX of sketch area population/
employment mix in relation to region mix
Dwellings per net acre of residential land
Percent of single-family units/total
dwellings
Percent of multi-family units/total
dwellings
Percent of dwellings within 1/4 mile of
transit stop
Percent of dwellings within 1/4 mile of
park
MMBtu/year/capita for housing and auto
travel
Gallons/day/capita
Ratio of total jobs to total housed
workers, assuming a constant 1.4 workers
per household
Employees/net acre of employment land
Percent of employees within 1/4 mi. of
transit stop
Park acres/1,000 persons
Percent of total sketch area in open space
land-use classes (as designated by user)
Percent street frontage with sidewalks
Average ratio of walking distance from
point of origin to central node, compared
to straight line distance
Street centerline miles/square mile
Ratio of intersections to total intersections
plus cul-de-sacs
Composite index of street network densi-
ty, sidewalk completeness, and pedestrian
route directness
Vehicle trips/day/capita
Miles driven/day/capita
Dollars/year/capita
Pounds/year/capita
Pounds/year/capita
Pounds/year/capita
Pounds/year/capita
Tons/year/capita
Pounds/year/capita
8
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The SGI indicators can be displayed in multiple formats. SGI produces tables that list each
indicator and its score for each modeled scenario. The indicator scores can be compared
among scenarios to provide a direct contrast of outcomes for public facilitators and partic-
ipants. Most indicators can also be mapped, as noted above, to illustrate geographical
variations. Lastly, SGI produces bar charts that demonstrate, for a given scenario, how a
set of indicators changes relative to the baseline score. Samples of these various readouts
are given in Appendix A.
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Section III: Smart Growth INDEX Pilot Projects
F
or the first pilot applications of the Smart Growth INDEX (SGI), EPA selected 20
pilot projects from the more than 40 pilot project proposals submitted, using the
criteria discussed in the Introduction (Section I) of this report.
Overview Statistics
In the map shown in Figure 1, the states with shading have an SGI pilot. Of the shaded
states, Maryland, North Carolina, and Texas each have 2 pilots.
Figure 1. Location of SGI Pilots, Phase 1
Interest in the pilot program has come from a variety of government entities and other
organizations. Figure 2 below shows how the 20 pilot project sponsors are distributed
among six categories: one transit authority, two non-governmental organizations, three
counties, four cities, four state agencies, and six metropolitan planning organization/coun-
cil of government entities.
Figure 2. Pibt Sites By Recipients Type
Abbreviations:
NGO: Non-Governmental
Organization
MPO: Metropolitan
Planning Organization
COG: Council of Governments
c
0
'5.
I
Transit Authority
NGO
County
City
State Agency
MPO/COG
246
Number of Pilots
10
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Pilot communities are using SGI for a variety of purposes. The bar graph in Figure 3 gives
an overview of the basic types of projects that SGI is supporting.
Figure 3. SGI Pilots by Project Type
0.
•5
Comprehensive Transit Oriented Infill/Urban Corridor
Planning Development Revitalization Planning
Project Type
During the initial pilot testing of SGI, most pilot communities chose to work with the snap-
shot mode, primarily because it is simpler to run and has been subject to more testing
than the forecast mode. (The differences between SGI's snapshot and forecast modes are
discussed early in Section II.) As shown in Figure 4, 16 pilot communities used the snap-
shot mode; four used the forecast mode of SGI.
Figure 4. SGI Mode Used By Pilots
Forecast Snapshot
Profiles of Selected SGI Pilot Projects
Seven pilot projects are discussed below to illustrate applications of the model in different
contexts. These examples represent state, regional, and local uses, and varying circum-
stances of resources and analysis. A number of sites used SGI output to develop public
support for more extensive application in the locality's comprehensive plan. Others used
the model to illustrate the potential air quality improvements with smart growth develop-
ment. Nearly all of the pilot sites used SGI to instigate the planning process and/or to
sketch — for public discussion — alternatives for future development. SGI makes it possi-
ble to increase community understanding of the effects of development alternatives on the
local and regional quality of life, as well as to enhance public participation in the process of
evaluating new development alternatives. Each of these pilot communities will continue its
work with Smart Growth INDEX beyond the initial pilot phase reported here.
11
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Pilot Case #1
Site: Fells Point - Baltimore (Digital) Harbor, Baltimore,
Maryland
Sponsor: Maryland Department of Planning
Features: The Maryland Department of Planning (MDP) decided to use SGI in its study
regarding redevelopment in the Baltimore Inner Harbor, also known as Digital Harbor. The
redevelopment involves a series of large-scale, mixed-use developments surrounding an
ongoing hi-tech business expansion. The state wanted to quantify air quality benefits for
the Digital Harbor project at two levels:
• At the macro level, a regional transportation model was used to evaluate travel and
air quality impacts of the concentration of projects at a central location
• At the micro level, the Smart Growth INDEX model was used to quantify travel effects
and air quality benefits associated with density, diversity, and design for a sample
sub-area
Context: In 2001, the EPA released guidance on how states and regions could incorporate
land-use activities in developing state implementation plans (SIPs) for meeting federal air
quality standards. The state of Maryland has been an innovator in employing land-use
activity to improve air quality and chose to apply SGI to help illustrate the potential benefits
of redevelopment in the Digital Harbor. In aggregate, development of the Digital Harbor
area is expected to result in an additional 26,400 jobs and 5,900 households by 2005,
and 37,600 jobs and 10,400 households by 2020 in the City of Baltimore.
Approach: Given resource challenges associated with the quantification of all projects
within Digital Harbor, MDP officials chose to focus on a representative study area: greater
Fells Point. Projects within greater Fells Point area include residential, office/commercial,
retail/entertainment, and hotel developments. Maryland officials estimated that these proj-
ects would generate 14,800 new jobs and 1,100 new housing units.
Using SGI's snapshot mode, planning officials were able to estimate air quality impacts
resulting from different mixes of housing and employment in the greater Fells Point area.
EPA's Development, Community, and Environment Division (DCED) staff provided assis-
tance through on-site visits, on-call phone and e-mail support, and review of data quality.
EPA staff also assisted the users in the process of focusing the project on a smaller site
area within Digital Harbor for estimate analysis.
Results and Future plans: Several SGI indicators of environmental performance stood out
as especially germane to travel behavior and air pollutant emissions. Using SGI, Planning
Department officials estimated that smart growth development in the Fells Point area
would reduce daily vehicle miles travelled (VMT) per capita, and annual per capita emis-
sions of nitrogen oxides, volatile organic compounds, and greenhouse gases by 14 per-
cent, as compared to the baseline/no build scenario for the site. As shown in Figure 5, the
study compared indicators of "ideal values" for the future with the planned scenario for
Fells Point.
12 EPA's Smart Growth INDEX in 20 Pilot Communities
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Figure 5. Sample Readout from Fells Point SGI Pilot Project (Baltimore, Maryland)
Indicator
Persons/sq. mi
Jobs/housing
balance
Land use mix
Street network
density
Sidewalk
completeness
Route directness
Street
connectivity
Average distance
to transit stop
Housing near
transit
Description
Persons (residents and
employees) persq. mi.
Ratio of total jobs to total housed
workers
Proportion of dissimilar land uses
among a grid of one-acre cells.
Length of street in miles divided
by areas of neighborhood in
square miles (miles persq. mile.)
Percent street frontage with
sidewalks
Ratio of shortest walking distance
from outlying nodes to
neighborhood center vs. straight-
line distance
Ratio of intersections vs.
intersections and cul-de-sacs
Average distance from dwellings
to closest transit stop in feet
Percent of dwellings within 1/4 mi.
of transit stops
Ideal
Scenario*
100,000
1.0
1.0
10
100
1.3
1.0
600 **
100
Fells
Point Scenario
75,570
6.93
0.63
34.6
100
0.9
0.67
229
90
* Ideal scenario generated by MDP from SGI support information and other published sources.
** Maximum ideal distance
Results from these analyses have been included in Maryland's current SIP revision, as an
illustration of the potential effects of smart growth on regional air quality. In addition,
Maryland officials have used the results from SGI to communicate with other public offi-
cials in the Baltimore region and colleagues nationwide on the prospective advantages
associated with smart growth and infill development. These communications included
briefings to the American Planning Association and the Baltimore Metropolitan Council.
For Maryland officials, Smart Growth INDEX filled a gap in their tool box by helping to
determine the emissions reductions associated with micro-scale improvements in develop-
ment design. The tool also contributed to the department's capacity to share its experi-
ences with public officials in a quantified and illustrated manner. In the future, the Maryland
Department of Planning intends to increase its use of Smart Growth INDEX. Having
employed the snapshot mode of SGI, officials anticipate employing the forecast mode to
examine future development in the state.
13
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Pilot Case #2
Site: Broadway Corridor, San Antonio, Texas
Sponsor: VIA Metropolitan Transit
Features: San Antonio planning groups worked with a broad coalition of stakeholders to
analyze future development opportunities in the Broadway corridor. The group identified
existing assets to leverage in conjunction with new redevelopment patterns, as part of a
smart growth strategy for the neighborhood.
Context: The San Antonio City Planning Department worked with EPA regional and head-
quarters staff to bring together representatives from the City Public Works Department, the
Alamo Area Council of Governments, and local residents and business leaders to discuss
a new vision for the future development of the Broadway corridor. The corridor is a prime
reuse area with numerous historical buildings and community assets such as a zoo, gar-
dens, museums, and a university.
The group recognized four major goals for their discussions of redevelopment alternatives
in this corridor:
• Fully identify all of the reuse capacity in the area
• Test new provisions of San Antonio's Uniform Development Code, which encourages
redevelopment of existing neighborhoods
• Look at the potential transit uses for underused roadway capacity along Broadway
Street
• Improve the stakeholders' understanding of the connection between land use, trans-
portation, and the environment
Approach: The coalition of community members came together to envision development
alternatives for the corridor, given existing assets and the four major goals described
above. The group applied SGI to evaluate the effects of different scenarios on the local
environment and quality of life.
Three main alternatives were modeled:
• The existing conditions along Broadway Street
• A compilation of the existing proposed land plans that predominantly focused on
auto- oriented development
• A new development plan, created by the coalition of stakeholders involved in this
process, that focused on mixed use, reuse, and transit-oriented development
A sequence of public forums was convened to review and discuss SGI indicator outputs
from all three alternatives. Thereafter, the group repeatedly improved upon the third alter-
native — a new development plan — which enabled them to develop a final proposal with
"optimum" indicator outputs.
Staff from EPA's Development, Community, and Environment Division provided extensive
assistance throughout the modeling and stakeholder participation process for San
14 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Antonio. EPA staff helped plan and convene the charrette for stakeholders to develop the
alternative scenarios. The staff met with other partners to describe the software and pres-
ent its capacity to help local planning. Technical support was provided to integrate the
alternatives into the model as well as convert necessary data for model inputs. Criterion
Planners and Engineers — the Portland, Oregon, contractor involved in developing SGI —
delivered a post-charrette presentation of the indicator outputs for the three alternatives.
EPA provided further assistance in reviewing the locality's development code for existing
policies that would help the area reach its pre-defined goals of improved air quality,
reduced water consumption, and enhanced community livability.
Results and Future Plans: The team compared outputs from the three scenarios to exam-
ine the necessary steps for corridor revitalization and improvement. The land use indica-
tors that differed most significantly between the modeled scenarios included:
• Land use balance
• Developed footprint
• Multi-family dwelling density
• Employment density
• Transit-oriented residential development
These land use changes were reflected in a number of environmental outcome measures
as well. For instance, the community-recommended plan showed a six percent decrease
in greenhouse gases and air pollutant emissions. This same plan also forecasted a 55 per-
cent decrease in imperviousness (in terms of impervious acres per capita). Additional indi-
cator outcomes are shown in Figure 6.
Results such as those shown in Figure 6 were used in the consensus-building community
process to create a new vision for San Antonio's Broadway Corridor. SGI readouts were
also used for agency coordination purposes during the redevelopment efforts for the corri-
dor. Findings were published in the ESRI magazine as well as numerous local newspaper
articles. The City and stakeholder team expect to continue to use SGI to refine the devel-
opment plan for the corridor. Additionally, the city's VIA Metropolitan Transit Agency will
use the SGI forecast mode to examine how the existing transit system assists (or hinders)
smart growth development. City planners are considering use of SGI for the city's neigh-
borhood planning process as well.
15 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Figure 6. SGI Indicator Scores: Broadway Coridor Project (San Antonio, Texas)
Element
Demographics
Land- Use
Housing
Employment
Recreation
Travel
Environment
Indicator
Population
Employment
Land Area
Block Size
Parcel Size
Parking Lot Size
Use Mix
Use Balance
Developed Footprint
Vacant Land
Redevelopable Land
Single-Family Parcel Size
Single-Family Dwelling Density
Multi -Family Dwelling Density
Single-Family Dwelling Share
Multi-Family Dwelling Share
Amenities Proximity
Transit Proximity
Water Consumption
Jobs to Mousing Balance
Employment Density
Transit Proximity
Commercial Building Density
Commercial Building Setback
Park Space Supply
Park Proximity
Internal Street Connectivity
External Street Connectivity
Street Network Density
Street Network Extent
Transit-Oriented Residential Density
Transit-Oriented Employment Density
Transit Service Coverage
Transit Service Density
Pedestrian Network Coverage
Pedestrian Crossing Distance
Pedestrian Route Directress
Bicycle Network Coverage
Vehicle Miles Traveled
Air Pollutant Emissions
Greenhouse Gas Emissions
Open Space Share
Open Space Connectivity
Imperviousness
Units
residents
employees
acres
acres
sqft
acres
0 to 1 index
0 to 1 index
acres/1 000 residents
acres
acres
sqft.
DU/acre
DU/acre
% total DU
% total DU
walk ft to closest grocery
walk ft. to closest stop
gal/day/capita
jobs/DU
emps/acre
walk ft to closest stop
floor area/land area ratio
ft.
acres/1 000 res.
walk ft. to closest park
intersections/node ratio
ft. between access points
miles/sq. mi.
miles/1000 residents
DU/acre w/i 1/4 mi.
emps/acre w/i 1/4 mi.
stops/sq mi
tran.veii-ini/day/acre
% tot. centerline dist.
curb to curb ft.
route ft./direct ft. ratio
% tot. centerline dist.
veh- mi/day/capita
Ibs/capita/year
Ibs/capita/year
% total area
0 to 1 index
acres/capita
Existing
Conditions
11,934
13,274
2,364
4.76
22,536
no data
0.42
0.62
97
135
no data
9,116
4.78
16.96
43
57
1,702
988
129
3.56
17.97
550
no data
no data
24
2,422
0.93
545
17.53
5.54
8.41
16.40
44
9.50
100
30
1.29
9
19
254
6,935
18
0.8
0.09
CoSA
Future
18,027
15,644
2,364
4.76
22,373
0.38
0.66
66
48
9,692
4.49
16.78
26
74
1,700
876
132
285
21 31
502
16
2,371
0.93
545
17.53
3.59
9.69
18.10
44
1252
100
30
1.29
9
18.54
250
6,833
18
0.89
0.06
Scheme 4
Future
27,117
30,173
2,364
4.76
22,356
0.43
0.69
44
45
9,537
4.57
22.77
17
83
1,613
745
131
3.49
36.98
495
11
2,325
0.93
545
17.53
2.39
13.10
33.62
44
12.52
100
30
1.29
12
17.92
239
6,534
18
0.89
0.04
Favorable
Marginal
Unfavorable
* Ratings were determined by the stakeholder group
** CoSA Future: Existing Future Land Use Plans
*** Scheme 4 Future: Charrette-developed scenario
Source: Smart Growth Indicators Modeling of the Broadway Corridor, 2001
16
EPA's Smart Growth INDEX in 20 Pilot Communities
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Project Case #3
Site: New Castle County, Delaware, and Cecil County,
Maryland
Sponsor: The Wilmington Area Planning Council
Features: The Wilmington Area Planning Council (WILMAPCO) used SGI in the snapshot
mode to evaluate alternative brownfield redevelopment scenarios and in the forecast mode
to help inform its long-range planning activities. The results helped to garner public sup-
port for future uses of the model.
Context: WILMAPCO is the metropolitan planning organization for New Castle County,
Delaware, and Cecil County, Maryland. The region is a diverse mix of an older industrial
port city (Wilmington), fast growing suburban areas, and traditionally rural land.
In 2001, WILMAPCO used SGI's snapshot mode to compare alternative brownfield rede-
velopment scenarios for the City of Wilmington, Delaware, the region's most populous city
at 72,000 people. In addition to those snapshot applications, WILMAPCO is using the
forecast mode option to help inform its long-range planning activities. The long range
transportation plan looks forward at least 20 years and lays out policy, action, and trans-
portation investment initiatives.
Approach: The snapshot mode was used to compare three brownfield redevelopment
scenarios. The "base condition" focused on a vacant property targeted by the City and
State for future economic development activity. The alternative scenarios tested a variety
of options at each site, including high density residential, intense commercial, and
industrial. Each of these options was modeled as a separate scenario. The results were
compared with the base case to determine the relative impacts of each option. (See Figure
7 for a "use mix" readout for the base scenario.)
The forecast mode is being applied to
the area's long-range plan. This mode
will help the partners engage the pub-
lic on issues of environmental protec-
tion, land use, and transportation.
They engaged diverse partners,
including the City's Planning and
Economic Development departments,
the Delaware Department of Natural
Resources and Environmental
Control, the State Office of Planning
Coordination, and EPA to select spe-
cific brownfield sites in the City to
model with SGI.
Staff from EPA's Development,
Community, and Environment Division
(DCED) provided support initially
through a two-day onsite working
group to help prepare existing region-
al data for the model. Staff provided
Figure 7. "Use-Mix" Indicator Readout for Base Scenario*: Wilmington
Area Project (New Castle and Cecil Counties)
P USND-llSENOKLUHkl
Ltasthfr-,11125
* i. .-
1125 0.375
onre-o
"Note on color code: Green indicates greater mix of uses; red indicates less mix.
17
EPA's Smart Growth INDEX in 20 Pilot Communities
-------
regular on-call support via phone and email. Additionally, EPA provided the users with
presentation materials for the MPO Technical Committee and public forums. Staff assisted
in a panel presentation for the Transaction 2002 conference that highlighted SGI applica-
tion in WILMAPCO's planning process.
Results and Future Plans: WILMAPCO staff made presentations about the SGI model to
the following groups: the WILMAPCO Technical Advisory
Committee, Delaware Geographic Data Committee, a designat-
ed SGI model development stakeholder group, and the New
Castle County Council. In each case, SGI received very favorable
comments. In an upcoming series of public outreach meetings
for its long range planning process, WILMACO will use SGI to
illustrate how environmental impacts relate to issues of land use
and transportation. By visually illustrating the impacts of the land
use and transportation changes and quantifying the effects of
the long-range plan at public meetings, SGI can help build
stronger public participation.
"The EPA Smart Growth INDEX model
provides an extremely useful tool to
allow a small agency with limited
resources... to more effectively engage
the public and decision makers in an
informed debate regarding transporta-
tion and land use options for the
future."
- Mr. Ted M. Matley
Executive Director, WILMAPCO
Dan Blevins, the model steward at WILMAPCO, has commented
that SGI is very useful because it allows metropolitan planning
organizations to test many scenarios quickly. For instance, Blevins used SGI to run various
scenarios involving transit service and fare changes in relatively little time compared to
other model options. What's more, SGI is easy to understand, he said, and therefore use-
ful when communicating to the public.
WILMAPCO plans to continue using SGI to inform its long-range planning process
the future.
in
18
EPA's Smart Growth INDEX in 20 Pilot Communities
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Pilot Case #4
Site: Berkeley-Charleston-Dorchester Counties, Charleston,
South Carolina region
Sponsor: Berkeley-Charleston-Dorchester Council of
Governments
Features: The Berkeley-Charleston-Dorchester Council of Governments (BCDCOG) used
SGI to project future growth patterns based on current policies and planned infrastructure
improvements. The Council then compared these to alternative investment patterns and
development choices, and examined the environmental quality and transportation capacity
impacts of each scenario.
Context: The Berkeley-Charleston-Dorchester region is home to more than 500,000 resi-
dents, and its growth patterns indicate that land development is outpacing population
growth by six to one. In response to this challenge, the region's Council of Governments
has begun to develop regional strategies and models to link
transportation and infrastructure planning to land use planning.
Approach: The BCDCOG is using the SGI model to analyze
development options for the region's future land use and zoning
plans. The model is particularly valuable for the region's Growth
Options Program because of its capacity to illustrate how differ- _ ,
"[SGI] has expanded our potential land
use modeling capacity far beyond what
is typically feasible for a smaller metro-
politan area such as ours."
ent land use, transportation, and infrastructure policies can affect
growth patterns. Data were collected for the three-county area,
including the regional municipalities. The staff is currently revising
the zoning data for Charleston and Berkeley Counties for their recently updated zoning
ordinances. BCDCOG staff has run the model for Berkeley County's future land use and
zoning data for preliminary results. Currently, the users are concentrating on the analysis of
three primary indicators: housing, population, and employment densities.
EPA's Development, Community, and Environment Division staff provided assistance
through on-call support and technical data review. Staff also helped the Council of
Governments create consistent land use definitions across the county jurisdictions and
improve the street network coverage used in the model.
Results and Future Plans: The results from the model will be used to aid regional discus-
sions concerning growth patterns. It is expected that the SGI forecast module will provide
a sophisticated analysis and projection of future growth patterns that will lead to increased
interest by policy makers about the impacts of regional land use policies. It is also expect-
ed that the SGI model will increase understanding about the link between land use and
infrastructure demands for the public.
The BCDCOG hopes that the results can be used by their economic and environmental
consultants on the Growth Options project to feed into fiscal and environmental cost mod-
els. This in turn can provide the region with estimated costs associated with growth alter-
natives. Considered together, these outputs will help the region more fully understand the
long-term impacts of different development and infrastructure scenarios on its economy
and environment.
Executive Director
19 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Housing Density Map Outputs: The housing density maps reproduced below illustrate
growth being redirected in the study area by pursuing more compact building design,
more housing types, and higher densities. Note how under existing conditions (Figure 8),
most of the region is scattered with lower density housing at less than 3 dwelling units per
acre (red areas). Then compare Figure 9, which shows a scenario in which more compact
design and higher densities are redirecting the region's housing growth.
Figure 8. Map Illustrating Existing Housing Density: Berkeley-Charleston-Dorchester
Counties, Charleston, South Carolina, Project
Jfr Smart Growth Index
HHE3
File
Shapefifes S ketch A.rea Eafameters Mo.de! Indicators M^P Help
(_f Forecast Sketch
Fl ^P Sketch Shaeefites
Fl s^ Sketch Area
lj EJ^ User Defined Parameters
Fl > Apply Model
Fl cj}^ Indicators
•• Results Table
Land Area Allocatioi
;»! i;^ Database
[Base
HOUSING DEN.(DUS/ACRI
Less than 3
3-5
6-3
10-14
15-19
23 -:4
35-49
>=50.
GRID
D
Ird oator | Bass
3i -,-, -..I---, c :>r p jc tr L- ;!;: 146
Population density 146
Incentive area use for housing
Incentive area use for employ.. .
Jobs/housed workers balance
H ousrig density 0 1
Housing transit proximity
Residential energy use
Residential water use
Employment density 0.3
Employment transit proximity
vr1-1 -"'-- -- *- i
2000 | Map ^
1 81 Yos
181 Yes
Kn
No _
r.o
0.1 Yes
Mo
No
No
0.7 Yes
No
"" _._P
Note on color code: Red/pink indicates low density; deepening shades of green indicate higher densities.
20
EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Figure 9. Map Showing Future Housing Density Scenario: Berkeley-Charleston-Dorchester
Project
4f- Smart Growth Index
Fie Sketch S lapef1 ss S--etchA'ea Farame'ets Mo.de! Jndicafc>rs rap n?
:l€'IOl
^ Forecast Sketch |Base
g.. ... j3p Sketch Ehapsflle*
H £^ Sketch Area
••'9? Usei Defined Parameters
Efl- Jf- Appfei Model
- =}= Indicators
Results Table
Land Area Allocations
- £3 Database
^,' Indicator Scores
Indicator I Base
IT HOUSINB DEN.(CU5/ACRE)
• .ess (Kan 3
1 " ' S
1 10-14
1 15-19
I 20-3-
I 35-43
• --EC
P GRID
n
^^^^^•"xl
2CHO 1 Map _±
Growth compactness Yes
Population density 147 I:- Yes
nccm \'c ores use tcr housing No
incentive area use for emptoy,,. No
Jobs/housed workers balance No
Housing denwl.v 0.1 0.1 YES
Housing transit proximity No
Residential energy use No
Residential water use No
E mployment density 1 0,3 Yes
Employment transit proximity No »|
• i -r
-------
Pilot Case #5
Site: Two Harbors, Minnesota
Sponsor: Minnesota Department of Planning
Features: The Minnesota town of Two Harbors is using SGI for a twofold purpose: (1) to
analyze how its comprehensive plan supports smart growth principles and (2) to check for
any internal inconsistencies within the plan.
Context: A town of approximately 4,000 people, Two Harbors is in the northeastern part of
the state, along the northern shores of Lake Superior. The town itself is located on two
natural bays along the North Shore of Lake Superior; it is bisected by Highway 61.
Highway 61 is known as the North Shore Scenic drive, long recognized as an extraordi-
nary scenic drive.
Two Harbors lost 18 percent of its population between 1970 and 2000. In an effort to spur
revitalization, the city and its residents have been exploring and discussing planning direc-
tions and overall city improvements. Projects under consideration include a new high
school, a trail head for the North Shore Trail, a safe harbor, a context-sensitive design for
the reconstruction of Highway 61, and a new focus on the historic downtown area in
conjunction with Lake Superior.
Approach: The modeling effort is being carried out through a collaboration of the State of
Minnesota Planning Department's Local Planning Assistance Team with the Arrowhead
Regional Development Commission and the town of Two Harbors. Since mid-2000, this
team has put together a comprehensive collection of geo-spatial data for both the town of
Two Harbors and Lake County. Data developed for the town include: sewer and water
lines, home locations by type, employment points, park locations, a zoning map, and an
existing land use map. A town- and county-wide planning GIS support system for plan-
ners and the community has been developed using these sources and other current state
datasets. (See Figure 10, which illustrates existing land uses in Two Harbors.)
Results and Future Plans: Initial meetings on the use of the Smart Growth INDEX and how
it relates to comprehensive planning and various city opportunities took place on March
11, 2002. Smart Growth INDEX is being used to create a benchmark of conditions for
evaluating future alternatives for city projects and build-out scenarios. The next step:
Forecasting will be used to address several viable options related to housing development,
recreational access, and opportunities, downtown revitalization, and the redesign of
Highway 61. The City Council will then examine the Smart Growth INDEX results for
planning consideration and general information about various spatial relationships in
the community.
The cooperation between the state, region, and locality to evaluate development alterna-
tives endows this process with enhanced capacity. By working with the state, Two
Harbors puts concentrated effort into the development of the local and county GIS data-
bases, and it will have greater long-term ability to examine future land use changes.
22 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Figure 10. Existing Land Uses in Study Area: Two Harbors, Minnesota
File Sketch Shapeffcs SketchArea Parameters Model Jndcatas Map Help
±
a-
\s
;
.
i ..
Tiavel
Trans*
ri2005
i- Constraints
nccflood
Rural
NCC Ag
b Incentives
Comrn_TIA
IflfrMMdun
SewMnd
Olher
NCC_Bnd
F ^-'F-ni-i
SketchBd
RriiH
UNO
;=• Sketch Area
1% User Defined Parameters
17 COMM_TIA
|7 NCC AG
•
T RURAL
D
17 NCCFLOOD
D
T EMP1T21
T TRANSIT
r
17 N1?39
^
T HH1121
r< Mrnn runner 1111
^^•^^^•••••••I^^^^^B:
Population -?
Land Use
Tianspoitaton Filename: Comm_TIA
Resorjces&Emrssions Desoiptiori: none
§Appl* Model
Indicators Incentive Field: COUNTY
Existing Land-use
IT Ftanned Land-Use
'.one I Werahtma
'J New Castle 150
f+ Housing
E-- •- Employment
m Travel
-• Constraints
b Incentwes N
M
Jf L e ; TIA
^ Center Weight Adfjstment
Ince-itwes: -cmnl TIA
Shape'ileType:
Description:
23
EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Pilot Case #6
Site: Hudson, Massachusetts (Boston Area)
Sponsor: Boston Metropolitan Area Planning Council
Features: Located 40 miles from Boston, along the 1-495 corridor, Hudson,
Massachusetts, has recently experienced rapid residential and high-tech commercial
growth, along with increased traffic congestion. SGI was used to evaluate impacts of fur-
ther growth in jobs and housing on the region's worsening traffic congestion and air quali-
ty.
Context: Through an FY 1999 Transportation and Community and System Preservation
(TCSP) grant from the US Department of Transportation, the Metropolitan Area Planning
Council (MAPC) and a large number of area stakeholders launched a project called the "I-
495 Technology Corridor Initiative/Campaign for Shared Solutions." Desiring to balance
economic vitality and community preservation, MAPC is working with residents of the
community to compare their goals for the future with a build-out scenario that would be
allowed under current zoning practices. As part of this process, SGI is being applied to the
town of Hudson along this corridor.
Approach: In addition to depicting the impacts of the build-out case, SGI will be used with
community residents to develop alternative visions of transportation investments and land
use scenarios. In Hudson, SGI was used to analyze two potential development projects.
The first was an expansion of an Intel Corporation campus, slated to the number of
employees from 2,000 to 2,500. The second was a proposed condominium development
of 150 units that required changing the current zoning from commercial to residential. The
snapshot sketch provided a useful level of analysis of the impacts of each of these proj-
ects. During the project development phase, staff from EPA's Development, Community,
and Environment Division helped the planning council determine the best application of the
model for regional goals and select an initial site.
Results and Future Plans: Addition of the proposed jobs and housing showed slight incre-
mental effects on the indicators modeled in SGI. These relatively small impacts may be the
result of the locations of the developments within the pattern seen in Hudson, or may
reflect the comparative size of the projects in relation to overall development in the region.
However, a series of developments of this type and size would likely have a much larger
effect on the environment.
Although the initial SGI analysis results were not dramatic, the public gained a greater
understanding of land use and transportation interplay through this analysis. Visual repre-
sentation of these interrelationships, such as pedestrian route directness seen in Figure 11,
allowed the community to participate more fully in the long-term corridor analysis than
would otherwise be the case. This information is now being considered by County and
planning officials, who will decide whether to allow these projects to proceed. Within the
scope of the TCSP grant, SGI will be used for impact review of other development pro-
posals as well.
24 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Figure 11. Map Illustrating Pedestrian Route Directness in Hudson, Massachusetts
''•.IfcyjiT
Pedestrian Route Directness
-All Parcels within a Hnlf-Milc Distance from Schools-
To>vn of Hudson
Pedestrian Route Directness-
ratio of walking distance to schools
destination vs. straight line distance
between same points — 1.6
1.2 is relatively favorable;
1.9 is relatively unfavorable
tOCO 0 SOW 12000 1KOO 24000 Fsrf
^ High School
£ Schools
!• Water
Parcds Withina Ds stance o f Schools
Wishtfi E/?trdle
p 1/8 • 1/4 mile
^ l/4-1/2nale
PatLtiiOulsideaKalf-y.ile
25
EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Pilot Case #7
Site: Indianapolis and Marion County, Indiana
Sponsor: City of Indianapolis
Features: The city of Indianapolis is using the SGI model in its comprehensive planning
process. It will also integrate the model into its natural resources management program as
a tool to assess the impacts of development and other land use changes on the city's nat-
ural environment. In addition, Marion County will also be using SGI in its county-wide com-
prehensive planning process, although that process has been temporarily postponed. The
intent is to use SGI to forecast changes in large areas of the county where significant
growth and development are anticipated.
Context: Two main Indianapolis city agencies are using the model: the Department of
Metropolitan Development (in particular, its Division of Planning), and the Department of
Public Works (specifically, its Environmental Resources Management Division).
The two agencies are working collaboratively. The Planning Division of the Department of
Metropolitan Development devoted staff to ensure the model would be installed correctly
and run effectively. The Department of Public Works dedicated staff members from a vari-
ety of disciplines to participate in the project.
Approach: For Indianapolis' comprehensive plan, the first application of the model was
running scenarios of land use options. These included applying various growth boundaries
in and around the city, preserving significant open spaces, making adjustments in develop-
ment density in certain areas of the city, framing policies promoting infill development, and
other options. Subsequent applications include sub-area planning, and comparing trans-
portation-related alternatives in the context of long-range planning.
The city also intends to use the SGI program in conjunction with the Long-Term
Hydrologic Impact Analysis (L-THIA) model, developed by Purdue University and EPA's
Region 5 (Chicago) office, to examine changes in forest cover, watershed impacts, air
quality impacts, brownfield redevelopment issues, development impacts in wellhead pro-
tection areas, and habitat changes under various land use scenarios.
Results and Future Plans: Indianapolis has arranged three "town hall" meetings for citizen
participation in Franklin Township, which has a good deal of undeveloped land. The meet-
ings consist of citizen participation workshops to plan land use scenarios in their areas.
These proposed land use maps will then be incorporated into the model. At follow-up
meetings, the results of the land use scenarios in SGI will be publicly presented.
EPA was pleased to learn that planning staff from Louisville, Kentucky - eager to learn
about SGI's capabilities - contacted Indianapolis' SGI project manager, who was able to
run the model for them. EPA strongly supports the sharing of information and experiences
among current and potential future SGI users, and was impressed to see this kind of col-
laboration across state boundaries.
26 EPA's Smart Growth INDEX in 20 Pilot Communities
-------
Section IV: Performance Highlights and
Lessons Learned: Launching SGI Version 2
j Irom the experiences of the first 20 Smart Growth INDEX pilot projects, ERA has
I documented the performance strengths of SGI Version 1 together with difficulties
c zi users encountered, for the purpose of strategically improving the program in Version
2. Highlighted below are the demonstrated strengths of Version 1, followed by a discus-
sion of user-recommended areas for improvement. Taking this feedback into account, EPA
has already moved forward with Version 2 of SGI, as described toward the end of this
section. In the future, EPA will work to continually improve subsequent versions of SGI -
both the technical performance of the software and the support provided to users.
Strengths of SGI Version 1
In field use, many pilot sponsors found SGI a strong tool for illustrating the environmental
and community benefits of smart growth development patterns because it vividly illustrates
the impacts of development alternatives and quickly presents scenario comparisons.
Wherever it was used — in public forums, in city council discussions, to illustrate State
Implementation Plans - SGI effectively helped both the public and local decision-makers
understand land use impacts. It helped decision-makers engage the public in discussing
links between land use and transportation alternatives, and overall implications for com-
munity quality of life. For many pilot sites, the resulting process provided a new opportunity
for the public to contribute to local and regional land use planning decisions.
As a case in point, the Executive Director of WILMAPCO (Pilot Case #3), Ted Matley, noted
that SGI helped educate a wide audience about the effects of transportation and land use
options, while his organization was working with limited resources. Ron Mitchum, the
Executive Director of the Berkeley-Charleston-Dorchester Council of Government, South
Carolina (Pilot Case #4), recognized the success of SGI in engaging the public in policy
decision-making. The pilot project in San Antonio, Texas (Pilot Case #2) used SGI to insti-
gate a public forum discussion about the future redevelopment of the Broadway corridor.
From this collaboration of community members, city staff, and local businesses developed
an alternative plan deemed a great improvement over existing plans, much more closely
reflecting the environmental goals and ideas of smart growth.
In addition to helping people understand planning decisions before investing major capital
into any particular plan, SGI expands the GIS and modeling capacity of the user organiza-
tion without extensive monetary investment (other than development of necessary GIS
data). Through its use of specific indicators, it provides quantifiable measures of smart
growth advantages that can be used in day-to-day planning decisions. The bottom line:
SGI provided users with valuable information to help inform planning decisions that better
protect the environment and community quality of life.
Recommended Improvements to SGI Version 1
The pilot users of SGI Version 1 suggested that improvements in the following categories
could enhance the efficiency and effectiveness of SGI applications in the field: software
ease of use, data availability, personnel resources required, and technical assistance, as
discussed below.
2 7 EPA's Smart Growth INDEX in 20 Pilot Communities
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Software Upgrades
After the initial release of SGI to the pilot groups in July 2000, a number of revisions to the
software were posted on the website of the software developer (Criterion Planners and
Engineers) for downloading. Pilot users were able to download these changes at their con-
venience and load them into the existing software. Some upgrades were necessary
because of modeling errors found by the users (and by Criterion) as the program was test-
ed in the field. Other program changes were made in order to provide specific new capa-
bilities requested by users.
Many users understandably found the interaction with error messages frustrating during
this revision process. Some pilots did not successfully run the model because of glitches
they encountered with these early versions and/or because of incompatible data formats
(discussed below). Also understandably, users experienced the frequent updating of the
program as setbacks to their attempts to get a project initiated.
To help minimize software problems, EPA's Development, Community, and Environment
Division agreed to all of the following improvements, which are reflected in Version 2 of
SGI:
• Work with Criterion on minimizing software glitches in the updated version of SGI (i.e.,
SGI Version 2)
• Discuss in detail the nature of the modeling software with new pilot users and explain
that it will regularly be upgraded and improved
• Develop a more complete list of frequently asked questions (FAQs) and answers for
SGI users to quickly access known remedies to previously experienced problems
• Maintain frequent technical support communications with users to supplement the
"Frequently Asked Questions" list - e.g., conduct "kick-off" user training and site vis-
its, hold periodic technical support conference calls, and set up an e-mail listserver.
Data Availability
Many pilot groups had difficulties finding or creating the necessary data layers to run SGI.
In instances where the local or regional planning organization did not have employment or
housing point data readily available, pilots were slowed or stopped for that reason. Also
frequently difficult data for pilot users to find: sewer and utility infrastructure information
and road network data. Some pilot sponsors found that their available databases were not
in the format required by SGI and needed extensive investments of time to be converted.
Pilot users made the following recommendations for alleviating data availability problems:
• Provide a universal database for selected layers needed in SGI. In particular, these
may include housing, employment, or street centerline data. Package this data with
SGI when distributing to users
• Present precise data structure and fields necessary for SGI application
• Provide a list of external data sources for necessary layers to facilitate community
database compilation
• Review pilot sponsor databases before model use
28 EPA's Smart Growth INDEX in 20 Pilot Communities
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In launching Version 2 of SGI, EPA accommodated all but one of the above suggestions.
The Agency was not able to provide a universal database for packaging with SGI — main-
ly because of the cost to provide each locality with detailed data.
Resources—Personnel and Hardware
A number of pilot sponsors cited limited local staff resources as a problem that hindered
the application of SGI to the proposed project. In some instances, local staff turnover was
the primary issue. In others, pilot users noted that they did not adequately anticipate the
demands of using the product. Another SGI Version 1 pilot was limited by the age and
speed of hardware available in the office. A related problem: Different iterations of the soft-
ware encountered errors with the Windows environment and other software present on the
user's somewhat older computer.
Acting on pilot users' suggestions on resource issues, EPA did all of the following before
launching SGI Version 2:
• Provide more detailed explanations of the time commitment and staff knowledge
needed to install and maintain SGI during the launch of SGI Version 2
• Discuss staff responsibilities required for participation in SGI Version 2 pilot projects
• Solve compatibility issues of SGI with other software
• Provide detailed requirements for users regarding necessary computer systems, and
software that may conflict
• Install Version 2 software on the user's computer to ensure compatibility
Technical Assistance
Pilot groups working to successfully run the software and install the necessary databases
faced numerous technical difficulties along the way. Assistance was provided both by EPA
staff and by Criterion personnel. Some pilots received on-site assistance, while most oth-
ers received help by phone or e-mail. Even with these various kinds of support, many pilot
users found that they needed more focused help, especially at the start of their SGI use.
To improve technical assistance for SGI Version 2, EPA accommodated all of the following
suggestions from Version 1 pilot users:
• Provide a more detailed guidebook, along with the Version 2 software, that discusses
technical issues faced by SGI Version 1 users
• Prepare guidebook appendices regarding the database requirements for each data
layer, with visual examples of necessary fields
• Integrate more screen "captures" into the guidebook to help on-site users with first
time scenario creation
• Provide several regional training programs, and train interested EPA regional staff on
SGI so that they may provide assistance to pilot users in their region
• Support a SGI listserver where users may request help from other users
• Establish a monthly conference call where users may ask technical questions of other
users or EPA staff
29 EPA's Smart Growth INDEX in 20 Pilot Communities
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• Distribute contact information for all the pilot leads so that groups may contact each
other more easily
• Develop an interactive database of technical errors and corresponding solutions
found to date to review with pilots who call for assistance
SGI Version 2: Program Improvements and Phase 2 Partner
Selection
Based on valuable feedback from pilot users of SGI Version 1, Version 2 of SGI has
been upgraded and expanded to take into account user needs and recommendations,
which are spelled out above. In fact, all recommendations were implemented prior to
launching Version 2, with the exception of providing a universal database for selected SGI
data layers.
The strengths of SGI Version 1 as a tool for vividly depicting comparative development
scenarios and their impacts, and thereby illustrating the environmental and community
benefits of smart growth, have been preserved and enhanced in Version 2. To sum up, the
key changes to SGI Version 2 are the following:
• Snapshot mode provided only
• More GIS data layers to integrate additional planning and environmental analysis
• Nearly 30 additional indicators for public review
• A rating and weighting indicator interface that allows audiences to combine selected
indicators into one major comparative index
• An expanded user manual
• Data preparation tools and review services to smooth upfront setup
• Onsite software installation and user training to ensure immediate productivity
• Increased contact between new (Phase 2) and continuing partner communities to
exchange experiences and successes
The Next Round of SGI Partner Projects
In mid-2002, EPA requested applications from communities interested in using Smart
Growth INDEX, Version 2, to evaluate local development alternatives. Each of 32 appli-
cants provided detailed information on local issues, data availability, and staff capabilities,
to help identify potentially successful local projects.
In August 2002, EPA announced the selection of fourteen new partner communities to
receive the SGI Version 2 software, documentation, technical assistance, and other
support. These new partner sponsors are using an improved SGI Version 2 tool, as
described above and will benefit from a new community process workshop. The workshop
will help new partners learn how to apply SGI in several typical settings, including public
forums, with lessons from experienced contractor and continuing partner staff.
30 EPA's Smart Growth INDEX in 20 Pilot Communities
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The new partners for 2003 are: Montgomery Township, New York; Cornell University;
Municipality of Murrysville, Pennsylvania; Prince George's County Planning Department,
Maryland; Voices and Choices of the Central Carolinas; Newton County Planning & Zoning
Department, Georgia; Austin Transportation Planning and Sustainability Department,
Texas; Twin Cities Metropolitan Council, Minnesota; Hamilton County Regional Planning
Commission, Ohio; North Central Wisconsin Regional Planning Commission, Wisconsin;
Ohio-Kentucky-Indiana Regional Council of Governments, Ohio; Hawaii State Office of
Planning, Hawaii; Community Planning Association of Idaho, Idaho; Maui County Planning
Department, Hawaii.
For More Information on SGI
For more detailed information about the SGI model (Version 2) and how it works, please
visit: (click on "Browse Smart Growth Topics," then
on "Smart Growth INDEX"). In the future, EPA plans to continually improve material avail-
able at this Web site includes:
• The SGI Reference Guide (a lengthy technical manual that documents the model's
methodologies)
• The SGI Getting Started Guide (a user manual)
• The SGI Process Guide (guidance on using SGI in the planning process)
31 EPA's Smart Growth INDEX in 20 Pilot Communities
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Appendices
Appendix A: Sample SGI Readouts
Example of Indicator "Score" Table
Example of Bar Chart
Example of Indicator Mapping
Appendix B: Summaries of Additional 13 SGI Pilot Projects
Burlington-Essex, Vermont
Concord, New Hampshire
Burlington County, New Jersey
St. Mary's County, Maryland
Wilmington, North Carolina
Hendersonville, North Carolina
Gainesville, Florida
Milwaukee, Wisconsin
St. Tammany Parish, Louisiana
Houston, Texas
Wildwood, Missouri
Clark County, Nevada
Merced, California
32
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Appendix A: Sample SGI Readouts
Example of Indicator "Score" Table: Illustrates how a scenario scores on each SGI indicator
1 fff Indicator Scores
Indicator 1 Score 1 Ivlap
Ml Population density 354 Yes
Use mi* 0.20 Yes
Jobs/housed workers balance 0.44 No
Land-use diversity 0.52 No
Residential density 4.8 Yes
Single -family housing share 30 No
Multi-family housing share 70 No
HousingproKirnity to transit 911 Yes
HousingproKinriit_Y to recreation 328 Yes
Residential water consumption 88 No
Employment density 8.2 Yes
Employment proximity to transit 1,149 Yes
Sidewalk completeness 39.3 Yes:
Pedestrian route directness 1 .5 Yes
Pedestrian design indes 3.0 No
Street network density 7.7 No
Street connectivity 0.72 No
Vehicle miles travel 21.0 No
Vehicle trips 4.5 No
Auto travel coste 3,S03 No
Residential energy consumption 98 No
Open space 1.2 Yes
Park space availability 5.5 Yes
Carbon morion! de [CO 1 432.5 No
Hydrocarbon (HC) 55.8 No
0 Hides of sulphu r f 9 OX) 11.9 No
Oxides of nitrogen (NOX) 4-1.1 No
Participate m at ter(PM) 1.3 No
Carbon dioxide (C02) 7.0 No
•I
Units
persons per sq.rni. ;
proportion of dissimilar uses among 1 acre grid cells
jobsy'workers (1.4 workers per household]
population and employment balance
dwelllngs/'acre
percent SF
percent MF
ave. distance to a stop (ft.)
ave. distance to a park (ft.)
gal/day ^capita
employee s/acre
ave. distance to a stop (ft.)
percent of street frontage w/ sidewalk
network distance/ airline distance
pedestrian walkability
street centerline mi. J sq. mi.
ratio cf intersections vs. intersections plus cul-de-sacs
VMT /day /capita
VT /day ^capita
S/jir/eapita
MMBtiVyr/capita [housing & travel)
percent total land area
acres/1 ,000 persons
Ibs/yr/capita
Ibs/yr/capita
Ibs/yr/capita
lbs:/yr/capita
Ibs/yr/capita
tons^iir/capita
.Return
33
EPA's Smart Growth INDEX in 20 Pilot Communities
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Example of Bar Chart: Depicts how much a scenario varies from its environmental baseline
score, in either a positive or negative percentage value
if- Index Inriicaln II«-
-------
Example of Indicator Mapping: Map depicting a single indicator (pedestrian route direct-
ness) for a given scenario (existing conditions, Hudson, MA)
Pedestrian Route Directness
-All Parcels within a Half-Mile Distance from Scliools-
Town of Hudson
L
Pedestrian Route Dirertness-
ratio oi'wuEkirjg disunite la schools
destination vs. straight line distance
between same points = 1.6
1.2 is relatively favorable;
1.9 is relatively uiifavunililc
MA PC *f ~-
£DM L1003 ISXC 2(000
S;hui:is
Within ],''3mile
1/4- Lfi 'Cult
. i^c .Is Du-.afe a Hslf-Milc
35
EPA's Smart Growth INDEX in 20 Pilot Communities
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Appendix B: Short Summaries of Additional 13
SGI Pilot Projects
In EPA Region 1 (Connecticut, Maine, Massachusetts,
New Hampshire, Rhode Island, and Vermont):
Burlington-Essex, Vermont
The Chittenden County Metropolitan Planning Organization (CCMPO) is planning an 8-mile
transit corridor between Burlington and Essex under the Federal Transit Administration's
New Starts program. Under this program, participants are required to develop plans for
transit-supportive land use that include measures to contain sprawl and supportive zoning
regulations. SGI's role in this project is to analyze various transit-oriented developments
and design scenarios, and to provide a much needed quantitative estimate of the effects
of these different options.
Concord, New Hampshire
Responding to a controversial widening of Interstate 93, a group of concerned Concord
citizens launched "the Initiative for a 20/20 Concord." Their goal: to create a long-range
plan for growth and development that will preserve the thriving downtown area, revitalize
old rail yards, and improve the region's air quality and water quality in the Merrimack River
watershed, using smart growth strategies. The initiative involves an extensive public partic-
ipation process that includes residents; state, regional and local government entities; and
business interests. As various vision and policy scenarios are considered, SGI helps
advance the public process by depicting the environmental and transportation impacts of
those different scenarios.
In EPA Region 2 (New Jersey, New York, Puerto Rico, and the Virgin
Islands):
Burlington County, New Jersey
The Delaware Valley Regional Planning Commission and the New Jersey Office of State
Planning are taking a community-driven approach to examining how transit-oriented devel-
opment scenarios will impact municipalities within the boundaries of the Delaware Estuary
Project, along a major transportation corridor. SGI was chosen as the tool to be used to
compare the effects of three scenarios: developing the corridor in an auto-oriented fash-
ion; developing improved transit service without supportive land use changes; and devel-
oping the corridor with improved transit and transit-oriented developments. This compara-
tive analysis will lead to further investigation of redeveloping abandoned shopping centers
based on new-urbanist principles and creating improved linkages to transit for bicyclists,
pedestrians, and feeder buses.
In EPA Region 3 (Delaware, Maryland, Pennsylvania, Virginia, West
Virginia, and Washington, District of Columbia):
St. Mary's County, Maryland
Southern Maryland is the fastest growing area in the state. In order to guide growth into
priority areas, St. Mary's County has developed a comprehensive plan that seeks to pro-
tect sensitive areas and the Chesapeake Bay watershed, diversify economic growth, and
reduce resource consumption. SGI is being used to assist the county in assessing the
36 EPA's Smart Growth INDEX in 20 Pilot Communities
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effectiveness of various policies such as density bonuses, agricultural preservation strate-
gies, mixed use planning districts, and infill development, which may ultimately be incorpo-
rated in the updated comprehensive plan.
In EPA Region 4 (Alabama, Florida, Georgia, Kentucky, Mississippi,
North Carolina, South Carolina, and Tennessee):
Wilmington, North Carolina
In 1999, the City of Wilmington and New Hanover County developed a Comprehensive
Plan that will be implemented through a "Unified Development Ordinance" (UDO). The
UDO provides the county with an opportunity to implement various Smart Growth initia-
tives, such as open space preservation and increased housing densities. Working with the
Wilmington Urban Area Metropolitan Planning Organization, the city and county are using
SGI to model alternative land use scenarios to inform the implementation of the UDO and
examining the impacts of an expanded transit network in the region. The project involves
students at the University of North Carolina at Wilmington in the modeling effort.
Hendersonville, North Carolina
A rapidly growing community in western North Carolina, Hendersonville is looking to create
a more walkable and livable downtown. The North Carolina Department of Transportation
and the City of Hendersonville are working jointly on developing the city's land use plan
and the state's Transportation Improvement Plan. As part of this working partnership, both
entities are using SGI to examine the impacts of proposed land use designations and
transportation improvements.
Gainesville, Florida
The City of Gainesville and Alachua County are working with the University of Florida,
Center for Construction and Environment, to examine a series of revitalization strategies for
the Depot Avenue corridor, located in Gainesville's downtown core. SGI is being used to
model locations for a multi-modal transportation hub and an urban rail-trail network, and
to examine options for the redevelopment of Depot Avenue and the surrounding commer-
cial and industrial areas. This work helps support EPA Brownfields and Sustainable
Development Challenge Grant efforts underway in the corridor.
In EPA Region 5 (Illinois, Indiana, Michigan, Minnesota, Ohio, and
Wisconsin):
Milwaukee, Wisconsin
The Menomonee Valley, a 1500-acre industrial brownfield area, is undergoing large-scale
redevelopment spearheaded by the City of Milwaukee. The site provides a significant
opportunity for urban manufacturing infill in the Milwaukee area, which has been experi-
encing significant development pressures on surrounding open space and farmland. SGI is
being used to analyze the effects of various transportation options within the site. The
Valley stakeholders, representing over 30 public, private, and non-profit organizations, are
examining scenarios to assess how site improvements affect vehicle miles traveled, trans-
portation access for the workforce, and air quality impacts.
37 EPA's Smart Growth INDEX in 20 Pilot Communities
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In EPA Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma,
and Texas):
St. Tammany Parish, Louisiana
St. Tammany Parish is developing a 10-element Comprehensive Plan, consistent with the
American Planning Association's "Growing Smart" guidance, to manage growth through-
out the parish. As part of the comprehensive planning process, the parish is examining a
range of densities, development types (linear patterns, radial corridors, spread cities, and
clusters), and transportation options, using SGI to visualize the impacts of these various
land use and transportation scenarios.
Houston, Texas
The Gulf Coast Institute (GCI), a non-profit organization serving as the principal leader of
smart growth in Houston, is using SGI as a bridge between community organizations and
planning agencies in the Houston region. Working with the City of Houston Planning
Department, GCI is examining development codes for ways to reduce air emissions; in
addition, the institute is assisting with scenarios for a Master Plan for a 10-block area in a
low-income neighborhood in the City's Third Ward. Assisted by the GIS Program Manager
at Houston-Galveston Area Council and the Scarcella Science and Technology Center of
Houston Community College, GCI is using SGI to evaluate the redevelopment of the
neighborhood with mixed-use development and pedestrian-enhanced transit access and
resulting effects on vehicle miles of travel and air quality.
In EPA Region 7 (Iowa, Kansas, Missouri, and Nebraska):
Wildwood, Missouri
Located in the path of St. Louis' impending development, the City of Wildwood has
adopted a Master Plan and various development ordinances to advance compact and
sustainable development. In this context, SGI is being used to compare alternative park
locations in terms of pedestrian access, vehicle miles traveled, land use mix, and open
space needs. In addition, SGI results will inform a pending decision on amending the
Master Plan to shift two parcels that are zoned for suburban-area densities and designs to
town-center level densities and designs.
In EPA Region 9 (Arizona, California, Hawaii, Nevada, Guam, and
American Samoa):
Clark County, Nevada
Under the Southern Nevada Public Lands Management Act, more than 27,000 acres of
Federal land within Clark County will be privatized by auction. In order to determine which
properties to nominate for disposal, the county and affected city governments are using
the SGI model to examine the impacts from alternative development scenarios and assist
with potential re-zonings. The county's goal is to support developments that will provide
environmental benefits through smart growth approaches.
Merced, California
The Merced County Association of Governments (MCAG), Caltrans, the Federal Highway
Administration, and EPA have formed the "Partnership for Integrated Planning"—a multi-
agency approach for improving regional planning efforts by integrating land use planning
with environmental and transportation planning. The steering committee has developed
an extensive workplan detailing specific tasks. All stakeholders will participate in
38 EPA's Smart Growth INDEX in 20 Pilot Communities
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developing population and housing projections, land use and transportation scenarios,
and impact evaluations as steps toward selecting a preferred transportation system, along
with financial plans and environmental mitigation strategies. SGI is part of a compendium
of tools to help MCAG and Caltrans evaluate the impacts of alternative transportation
system scenarios.
39 EPA's Smart Growth INDEX in 20 Pilot Communities
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United States
Environmental Protection Agency
(1808T)
1200 Pennsylvania Avenue, NW.
Washington, DC 20460
Official Business
Penalty for Private Use $300
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