Smart Location Database
Technical Documentation
and User Guide
Version 3.0
Updated: June 2021
Authors:
Jim Chapman, MSCE, Managing Principal, Urban Design 4 Health, Inc. (UD4H)
Eric H. Fox, MScP, Senior Planner, UD4H
William Bachman, Ph.D., Senior Analyst, UD4H
Lawrence D. Frank, Ph.D., President, UD4H
John Thomas, Ph.D., U.S. EPA Office of Community Revitalization
Alexis Rourk Reyes, MSCRP, U.S. EPA Office of Community Revitalization
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About This Report
The Smart Location Database is a publicly available data product and service provided by the U.S. EPA
Smart Growth Program. This version 3.0 documentation builds on, and updates where needed, the version
2.0 document.1 Urban Design 4 Health, Inc. updated this guide for the project called Updating the EPA GSA
Smart Location Database.
Acknowledgements
Urban Design 4 Health was contracted by the U.S. EPA with support from the General Services
Administration's Center for Urban Development to update the Smart Location Database and this User
Guide. As the Project Manager for this study, Jim Chapman supervised the data development and authored
this updated user guide. Mr. Eric Fox and Dr. William Bachman led all data acquisition, geoprocessing, and
spatial analyses undertaken in the development of version 3.0 of the Smart Location Database and co-
authored the user guide through substantive contributions to the methods and information provided. Dr.
Larry Frank provided data development input and reviewed the report providing critical input and feedback.
The authors would like to acknowledge the guidance, review, and support provided by:
Ruth Kroeger, U.S. General Services Administration
Frank Giblin, U.S. General Services Administration
^{amart Location Database: Version 2.0 User Guide. U.S. EPA, 2014.
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Table of Contents
About This Report ii
Table of Contents iii
Figures iv
Tables iv
Background 1
Accessing the Smart Location Database 1
Smart Location Database Measures 2
Data Sources 7
Block Group Boundaries 7
Census American Community Survey 7
Longitudinal Employer-Household Dynamics 8
HERE 9
TravelTime API 9
Protected Areas Database 9
General Transit Feed Specification 10
Transit-Oriented Development Database 10
National Transit Database 10
Technical Approach 12
Geographic Coordinate System & Projection 12
Administrative 12
Demographics 12
Employment 13
Area 15
Density (Dl) 16
Employment & Housing Diversity (D2) 16
Urban Design (D3) 19
Transit Accessibility (D4) 22
Distance to Nearest Transit (D4a) 22
Access to Fixed-Guideway Transit (D4b) 23
Aggregate Frequency of Peak Hour Transit Service (D4c) 23
Aggregate Frequency of Peak Hour Transit Service Density (D4d) 24
Aggregate Frequency of Peak Hour Transit Service per Capita (D4e) 24
Destination Accessibility (D5) 24
Destination Accessibility via Automobile Travel (D5a) 24
Destination Accessibility via Transit (D5b) 28
iii
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Proportional Regional Accessibility (D5c) 31
Relative Regional Accessibility (D5d) 31
National Walkability Index 31
Appendix A: Regions with transit service data reflected in SLD metrics 33
Appendix B: Transit Service Data: GTFS Transit Agencies 35
Appendix C: Transit Service Data: GTFS Data Coverage by Ridership 0
Figures
Figure 1: 2017 NHTS travel time distance decay based on reported commute travel times 26
Figure 2: Graph demonstrating the various time decay functions by source 29
Tables
Table 1: Description, data source and geographic coverage of all SLD measures 2
Table 2: Summary of LEHD LODES WAC and RAC variables 8
Table 3: Groups of LODES WAC characteristics to support five-tier employment entropy 14
Table 4: Groups of LODES WAC characteristics to support eight-tier employment entropy 14
Table 5: Detailed description of employment and housing diversity (D2) variables 17
Table 6: Summary of intersection density measures by type groupings and corresponding urban design
variables 21
Table 7: Attributes and Parameters of Transit Accessibility Analysis 30
Table 8: Summary of metropolitan regions with fixed-guideway transit service incorporated into SLD
variables 33
Table 9: Summary of transit agencies that have GTFS data reflected in SLD measures 35
Table 10: GTFS transit data coverage summarized by total 2019 ridership by metropolitan area 0
iv
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Background
The U.S. Environmental Protection Agency's (EPA) and U.S. General Services Administration
(GSA) Smart Location Database (SLD) addresses the growing demand for data products and tools
that consistently compare the location efficiency of various places. The SLD summarizes several
demographic, employment, and built environment variables for every Census block group (CBG) in
the United States.2 The database includes indicators of the commonly cited "D"3 variables shown in
the transportation research literature to be related to travel behavior.4 The Ds include residential and
employment density, land use diversity, design of the built environment, access to destinations, and
distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for
scenario planning studies, and combined into composite indicators characterizing the relative location
efficiency of CBG within U.S. metropolitan regions.
Previous versions of the SLD (version 1.0) were released by the EPA in early 2012 and again in 2014
(version 2.0). This guide describes anew version of the SLD (version 3.0, herein referred to as the
SLD). The 2021 update features the most recent geographic boundaries (2019 CBGs) and new and
expanded sources of data used to calculate variables. Entirely new variables have been added and the
methods used to calculate some of the SLD variables have changed. Although the majority of SLD
variables are consistent in the data source and calculation method to previous versions, it may not be
appropriate to compare all variables with version 2.0 directly. Changes in data sources and methods
are explained in detail in this guide.
Version 3.0 of the SLD was developed by Urban Design 4 Health for the EPA Office of Community
Revitalization and the GSA Center for Urban Development. This guide contains a detailed
description of the data sources and methodologies used to calculate each of the variables included in
the SLD. It also reviews any known geographic or data limitations associated with variables in the
SLD.
Accessing the Smart Location Database
The SLD is a free resource available to the public for download, web service, or viewing online.
Download:
The SLD can be downloaded as a file geodatabase from this page:
https://www.epa.gOv/smartgrowth/smart-location-mapping#sld
Web service:
The SLD is available as a map service, JSON, SOAP, and KML. See the SLD web service5 for
details: https://geodata.epa.gov/ArcGIS/rest/services/OA/SmartLocationDatabase/MapServer
Viewing online:
Visit https://www.epa.gOv/smartgrowth/smart-location-mapping#sld to open the map viewer.
2 SLD version 3.0 uses 2018 Census TIGER/Line polygons for defining block group boundaries.
3 Cervero, R. & Kockelman. 1997. Travel Demand and the 3Ds: Density, Diversity, and Design. Transportation
Research PartD. 2 (3): 199-219.
4 Ewing, R. & Cervero, R. 2001. Travel and the Built Environment: A Synthesis. Transportation Research Record,
1780(1), 87-114; Ewing, R & Cervero, R. 2010. Travel and the Built Environment: A Meta-Analysis. Journal of
the American Planning Association, 76(3), 265-294; Kuzmyak, J.R., Pratt, R.H., Douglas, G.B., Spielberg, F.
(2003). Land Use and Site Design - Traveler Response to Transportation System Changes. Transit Cooperative
Research Program (TCRP) Report 95: Chapter 15, published by Transportation Research Board, Washington.
1
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Smart Location Database Measures
Table 1 lists all of the variables available in the SLD. SLD variables are sorted by topic areas and start with
administrative identifiers, geometric area characteristics and demographic and employment base data
gathered from the U.S. Census. There are five main domains for calculated measures in the SLD: 1)
Density (Dl), 2) Diversity (D2), 3) Design (D3), 4) Transit Accessibility (D4) and 5) Destination
Accessibility (D5). Identical field names from version 2.0 were retained for consistency. The few variables
new to version 3.0 maintain the same naming convention. SLD variable names are identified using square
brackets (e.g. [D3b]), except when referred to in formula text, tables, header titles, discussing prefix or
suffix components, or field name prefixes are used to relate to multiple variables. The sections that follow
describe the data sources and the technical approach used to calculate the measures in further detail.
Table 1: Description, data source and geographic coverage of all SLD measures.
Field Name
Description
Data Source
Geographic
Coverage*
Administrative
GEOIDIO
Census block group 12-digit FIPS code (2010)
2010 Census TIGER/Line
50 States, PR, OT
GEOID20**
Census block group 12-digit FIPS code (2018)
2019 Census TIGER/Line
50 States, PR, OT
STATEFP
State FIPS code
2019 Census TIGER/Line
50 States, PR, OT
COUNTYFP
County FIPS code
2019 Census TIGER/Line
50 States, PR, OT
TRACTCE
Census tract FIPS code in which CBG resides
2019 Census TIGER/Line
50 States, PR, OT
BLKGRPCE
Census block group FIPS code in which CBG resides
2019 Census TIGER/Line
50 States, PR, OT
CSA
Combined Statistical Area (CSA) Code
US Census
50 States, PR, OT
CSA Name
Name of CSA in which CBG resides
US Census
50 States, PR, OT
CBS A
FIPS for Core-Based Statistical Area (CBSA) in which
CBG resides
US Census
50 States, PR, OT
CBS A Name
Name of CBSA in which CBG resides
US Census
50 States, PR, OT
Core-Based Statistical Area Measures
CBSA_Pop
Total population in CBSA
2018 US Census ACS (5-Year
Estimate)
50 States, PR
CBS A Emp
Total employment in CBSA
2017 Census LEHD,
50 States, PR
CBS A Wrk
Total number of workers that live in CBSA
2017 Census LEHD,
50 States, PR
Area
Ac Total
Total geometric area (acres) of the CBG
2019 Census TIGER/Line
50 States, PR, OT
Ac_Water
Total water area (acres)
Census, 2018 HERE Maps
NAVTREETS, HERE Maps
Water & Oceans, 2018 USGS
PAD-US, USGS National
Hydrography Data Plus
50 States, PR, OT
Ac_Land
Total land area (acres)
Census, 2018 HERE Maps
NAVTREETS, HERE Maps
Water & Oceans, 2018 USGS
PAD-US, USGS National
Hydrography Data Plus
50 States, PR, OT
Ac_Unpr
Total land area (acres) that is not protected from
development (i.e., not a park, natural area or conservation
area)
Census, 2018 HERE Maps
NAVTREETS, HERE Maps
Parks, 2018 USGS PAD-US,
USGS National Hydrography
Data Plus
50 States, PR, OT
I )emo«raphics
Toll'op
Population, 2018
2018 Census ACS (5-Year
Estimate), 2010 Decennial
Census (OT only)
50 Stales. PR. OT
CountHU
Housing units, 2018
2018 Census ACS (5-Year
Estimate), 2010 Decennial
Census (OT only)
50 States, PR, OT
HH
Households (occupied housing units), 2018
2018 Census ACS (5-Year
Estimate), 2010 Decennial
Census (OT only)
50 States, PR, OT
P WrkAge
Percent of population that is working aged 18 to 64 years,
2018 Census ACS (5-Year
50 States, PR
2
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Field Name
Description
Data Source
Geographic
Coverage*
2018
Estimate)
AutoOwnO
Number of households in CBG that own zero automobiles,
2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
Pct_AOO
Percent of zero-car households in CBG, 2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
AutoOwnl
Number of households in CBG that own one automobile,
2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
Pct_A01
Percent of one-car households in CBG, 2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
AutoOwn2p
Number of households in CBG that own two or more
automobiles, 2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
Pct_A02p
Percent of two-plus-car households in CBG, 2018
2018 Census ACS (5-Year
Estimate)
50 States, PR
Workers
Count of workers in CBG (home location), 2017
2017 Census LEHD RAC
50 States
R_LowWageWk
Count of workers earning $1250/month or less (home
location), 2017
2017 Census LEHD RAC
50 States
R_MedWageWk
Count of workers earning more than $1250/month but less
than $3333/month (home location), 2017
2017 Census LEHD RAC
50 States
R_HiWageWk
Count of workers earning $3333/month or more (home
location), 2017
2017 Census LEHD RAC
50 States
R_PctLowWage
Percent of low wage workers in a CBG (home location),
2017
2017 Census LEHD RAC
50 States
Employment
TotEmp
Total employment, 2017
2017 Census LEHD WAC
50 States
E5_Ret
Retail jobs within a 5-tier employment classification
scheme (LEHD: CNS07), 2017
2017 Census LEHD WAC
50 States
E5_Off
Office jobs within a 5-tier employment classification
scheme (LEHD: CNS09 + CNS10 + CNS11 + CNS13 +
CNS20) ,2017
2017 Census LEHD WAC
50 States
E5_Ind
Industrial jobs within a 5-tier employment classification
scheme (LEHD: CNS01 + CNS02 + CNS03 + CNS04 +
CNS05 + CNS06 + CNS08) ,2017
2017 Census LEHD WAC
50 States
E5_Svc
Service jobs within a 5-tier employment classification
scheme (LEHD: CNS12 + CNS14 + CNS15 + CNS16 +
CNS19), 2017
2017 Census LEHD WAC
50 States
E5_Ent
Entertainment jobs within a 5-tier employment
classification scheme (LEHD: CNS17 + CNS18), 2017
2017 Census LEHD WAC
50 States
E8_Ret
Retail jobs within an 8-tier employment classification
scheme (LEHD: CNS07), 2017
2017 Census LEHD WAC
50 States
E8_Off
Office jobs within an 8-tier employment classification
scheme (LEHD: CNS09 + CNS10 + CNS11 + CNS13),
2017
2017 Census LEHD WAC
50 States
E8_Ind
Industrial jobs within an 8-tier employment classification
scheme (LEHD: CNS01 + CNS02 + CNS03 + CNS04 +
CNS05 + CNS06 + CNS08), 2017
2017 Census LEHD WAC
50 States
E8_Svc
Service jobs within an 8-tier employment classification
scheme (LEHD: CNS12 + CNS14 + CNS19), 2017
2017 Census LEHD WAC
50 States
E8_Ent
Entertainment jobs within an 8-tier employment
classification scheme (LEHD: CNS17 + CNS18), 2017
2017 Census LEHD WAC
50 States
E8_Ed
Education jobs within an 8-tier employment classification
scheme (LEHD: CNS15), 2017
2017 Census LEHD WAC
50 States
E8_Hlth
Health care jobs within an 8-tier employment
classification scheme (LEHD: CNS16), 2017
2017 Census LEHD WAC
50 States
E8_Pub
Public administration jobs within an 8-tier employment
classification scheme (LEHD: CNS20), 2017
2017 Census LEHD WAC
50 States
E_LowWageWk
# of workers earning $1250/month or less (work location),
2017
2017 Census LEHD WAC
50 States
E_MedWageWk
# of workers earning more than $1250/month but less than
$3333/month (work location), 2017
2017 Census LEHD WAC
50 States
E HiWageWk
# of workers earning $3333/month or more (work
2017 Census LEHD WAC
50 States
3
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Field Name
Description
Data Source
Geographic
Coverage*
location), 2017
E_PctLowWage
% LowWageWk of total #workers in a CBG (work
location), 2017
2017 Census LEHD WAC
50 States
Density (1)1)
Dla
Gross residential density (HU/acre) on unprotected land
Derived from other SLD
variables
50 States, PR, OT
Dlb
Gross population density (people/acre) on unprotected
land
Derived from other SLD
variables
50 States, PR, OT
Die
Gross employment density (jobs/acre) on unprotected land
Derived from other SLD
variables
50 States
Dlc5_Ret
Gross retail (5-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc5_Off
Gross office (5-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc5_Ind
Gross industrial (5-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc5_Svc
Gross service (5-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc5_Ent
Gross entertainment (5-tier) employment density
(iobs/acre) on unprotected land
Derived from other SLD
variables
50 States
Dlc8_Ret
Gross retail (8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc8_Off
Gross office (8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc8_Ind
Gross industrial (8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc8_Svc
Gross service (8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc8_Ent
Gross entertainment (8-tier) employment density
(jobs/acre) on unprotected land
Derived from other SLD
variables
50 States
Dlc8_Ed
Gross education(8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Dlc8_Hlth
Gross health care (8-tier) employment density (jobs/acre)
on unprotected land
Derived from other SLD
variables
50 States
Dlc8_Pub
Gross retail (8-tier) employment density (jobs/acre) on
unprotected land
Derived from other SLD
variables
50 States
Did
Gross activity density (employment + HUs) on
unprotected land
Derived from other SLD
variables
50 States
(employment and
housing), PR
(housing only),
OT (housing only)
Dl_Flag
Flag indicating that density metrics are based on total
CBG land acreage rather than unprotected acreage
Derived from other SLD
variables
50 States, PR, OT
Diversity (D2)
D2a_JpHH
Jobs per household
Derived from other SLD
variables
50 States
D2b_E5Mix
5-tier employment entropy (denominator set to observed
employment types in the CBG)
Derived from other SLD
variables
50 States
D2b_E5MixA
5-tier employment entropy (denominator set to the static 5
employment types in the CBG)
Derived from other SLD
variables
50 States
D2b_E8Mix
8-tier employment entropy (denominator set to observed
employment types in the CBG)
Derived from other SLD
variables
50 States
D2b_E8MixA
8-tier employment entropy (denominator set to the static 8
employment types in the CBG)
Derived from other SLD
variables
50 States
D2a_EpHHm
Employment and household entropy
Derived from other SLD
variables
50 States, PR
(housing only),
OT (housing only)
D2c_TrpMxl
Employment and Elousehold entropy (based on vehicle trip
production and trip attractions including all 5 employment
categories)
Derived from other SLD
variables
50 States, PR
(housing only),
OT (housing only)
4
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Field Name
Description
Data Source
Geographic
Coverage*
D2c_TrpMx2
Employment and Household Entropy calculations, based
on trips production and trip attractions including 4 of the 5
employment categories (excluding industrial)
Derived from other SLD
variables
50 States, PR
(housing only),
OT (housing only)
D2c_TripEq
Trip productions and trip attractions equilibrium index; the
closer to one, the more balanced the trip making
Derived from other SLD
variables
50 States
D2r_JobPop
Regional Diversity. Standard calculation based on
population and total employment: Deviation of CBG ratio
of iobs/pop from the regional average ratio of iobs/pop
Derived from other SLD
variables
50 States, PR
(housing only)
D2r_WrkEmp
Household Workers per Job, as compared to the region:
Deviation of CBG ratio of household workers/job from
regional average ratio of household workers/job
Derived from other SLD
variables
50 States
D2a_WrkEmp
Household Workers per Job, by CBG
Derived from other SLD
variables
50 States
D2c_WrEmIx
Household Workers per Job Equilibrium Index; the closer
to one the more balanced the resident workers and jobs in
the CBG.
Derived from other SLD
variables
50 States
Desimi (1)3)
D3a
Total road network density
2018 HERE Maps
NAVSTREETS
50 States. PR. VI
D3aao
Network density in terms of facility miles of auto-oriented
links per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3amm
Network density in terms of facility miles of multi-modal
links per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3apo
Network density in terms of facility miles of pedestrian-
oriented links per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3b
Street intersection density (weighted, auto-oriented
intersections eliminated)
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3bao
Intersection density in terms of auto-oriented intersections
per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3bmm3
Intersection density in terms of multi-modal intersections
having three legs per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3bmm4
Intersection density in terms of multi-modal intersections
having four or more legs per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3bpo3
Intersection density in terms of pedestrian-oriented
intersections having three legs per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
D3bpo4
Intersection density in terms of pedestrian-oriented
intersections having four or more legs per square mile
2018 HERE Maps
NAVSTREETS
50 States, PR, VI
Transit Access (D4)
D4a
Distance from the population-weighted centroid to nearest
transit stop (meters)
2020 GTFS, 2020 CTOD
50 States
(participating
GTFS transit
service areas), PR
D4b025
Proportion of CBG employment within % mile of fixed-
guideway transit stop
2020 GTFS, 2020 CTOD, 2018
USGS PAD-US, SLD
unprotected area polygons
50 States, PR
D4b050
Proportion of CBG employment within Vi mile of fixed-
guideway transit stop
2020 GTFS, 2020 CTOD, 2018
USGS PAD-US
50 States, PR
D4c
Aggregate frequency of transit service within 0.25 miles of
CBG boundary per hour during evening peak period
2020 GTFS
50 States
(participating
GTFS transit
service areas)
D4d
Aggregate frequency of transit service [D4c] per square
mile
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
D4e
Aggregate frequency of transit service [D4c] per capita
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
Destination Accessibility (1)5)
5
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Field Name
Description
Data Source
Geographic
Coverage*
D5ar
Jobs within 45 minutes auto travel time, time- decay
(network travel time) weighted
2020 TravelTime API, 2017
Census LEUD
50 States
D5ae
Working age population within 45 minutes auto travel
time, time-decay (network travel time) weighted
2020 TravelTime API, 2018
Census ACS
50 States
D5br
Jobs within 45-minute transit commute, distance decay
2020 TravelTime API, 2017
50 States
(walk network travel time, GTFS schedules) weighted
Census LEUD, 2020 GTFS
(participating
GTFS transit
service areas)
D5be
Working age population within 45-minute transit
commute, time decay (walk network travel time, GTFS
schedules) weighted
2020 TravelTime API, 2018
Census ACS, 2020 GTFS
50 States
(participating
GTFS transit
service areas)
D5cr
Proportional Accessibility to Regional Destinations -
Auto: Employment accessibility expressed as a ratio of
total CBSA accessibility
Derived from other SLD
variables
50 States
D5cri
Regional Centrality Index - Auto: CBG [D5cr] score
relative to max CBSA [D5cr| score
Derived from other SLD
variables
50 States
D5ce
Proportional Accessibility to Regional Destinations -
Auto: Working age population accessibility expressed as a
ratio of total CBSA accessibility
Derived from other SLD
variables
50 States
D5cei
Regional Centrality Index - Auto: CBG [D5ce] score
relative to max CBSA fD5cel score
Derived from other SLD
variables
50 States
D5dr
Proportional Accessibility of Regional Destinations -
Transit: Employment accessibility expressed as a ratio of
total MSA accessibility
Derived from other SLD
variables
50 States
D5dri
Regional Centrality Index - Transit: CBG [D5dr] score
Derived from other SLD
50 States
relative to max CBSA [D5dr] score
variables
(participating
GTFS transit
service areas)
D5de
Proportional Accessibility of Regional Destinations -
Transit: Working age population accessibility expressed as
a ratio of total MSA accessibility
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
D5dei
Regional Centrality Index - Transit: CBG [D5de] score
relative to max CBSA [D5de] score
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
Walkability Index
D2A_Ranked
Quantile ranked order (1-20) of [D2a_EpUHm] from
lowest to highest
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
D2B_Ranked
Quantile ranked order (1-20) of [D2b_E8MixA] from
lowest to highest
Derived from other SLD
variables
50 States
(participating
GTFS transit
service areas)
D3B Ranked
Quantile ranked order (1-20) of [D3b] from lowest to
Derived from other SLD
50 States
highest
variables
(participating
GTFS transit
service areas)
D4A Ranked
Quantile ranked order (l,13-20)6 of [D4a] from lowest to
Derived from other SLD
50 States
highest
variables
(participating
GTFS transit
service areas)
NatWalklnd
Walkability index comprised of weighted sum of the
ranked values of fD2a EpUHml (D2A Ranked),
Derived from other SLD
variables
50 States
(participating
6 All CBGs with no transit access were assigned a rank of 1. The remaining CBGs were assigned a rank from 13-
2 0 (n=8 classes) following the same methodology used in the previous version except adding two additional
ranks due to an increase in the number of CBGs that now have access to transit.
6
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Field Name
Description
Data Source
Geographic
Coverage*
[D2b_E8MixA] (D2B_Ranked), [D3b] (D3B_Ranked)
and fD4al (D4A Ranked)
GTFS transit
service areas)
* Comprises, where stated, the 50 U.S. states including the District of Columbia, Puerto Rico (PR) and the U.S. overseas territories
(OT), which include Guam (GU), American Samoa (AS), the Commonwealth of the Northern Mariana Islands (MP) and the U.S.
Virgin Islands (VI) (unless otherwise mentioned).
** Two sets of FIPS CBG identifiers are provided for the SLD database. The first is the original 2010 FIPS CBG identifier
[GEOIDIO] and the second is an updated 2019 FIPS CBG identifier [GEOID20], A total of 74 (0.3%) of FIPS CBG identifiers
were changed. Both are required for the database because many base data sources continue to use the 2010 FIPS CBG identifier.
Data Sources
This section summarizes each of the data sources used to develop the SLD. These include:
Census datasets (TIGER/Line, 2010 Summary File 1, American Community Survey, and
Longitudinal Employer-Household Dynamics),
HERE Maps NAVSTREETS highway/streets, parks and water data,
U.S. Geological Survey Protected Areas Database of the United States,
U.S. Geological Survey National Hydrography Data Plus,
fixed-guideway transit station locations from the Center for Transit-Oriented Development
Transit-Oriented Development Database, and
transit service route and schedule data from various inventories, including TransitFeeds,
TransitLand and directly from individual transit authorities shared in the General Transit Feed
Specification format.
Block Group Boundaries
CBG polygon geography was acquired from 2019 Census TIGER/Line databases7 and combined them
into a single national ArcGIS (ESRI, Redlands, CA) feature class.8 EPA SLD Database V3 2021 is
the core geographic dataset to which all SLD variables were appended. It represents the 2019
geographic boundaries of all CBGs in the contiguous United States, District of Columbia, Alaska,
Hawaii, Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa and the Commonwealth of the
Northern Mariana Islands.9 The most recent, publicly available CBG "centers of population"10 are the
same as what was used for version 2.0 of the SLD. These 2010 points were used in geoprocessing
routines developed for spatially derived variables, notably the distance to the nearest transit stop and
regional accessibility measures.11 Lastly, tables containing county, Core-Based Statistical Areas and
Combined Statistical Areas information were also acquired from the U.S. Census Bureau. These tables
were used to associate CBGs with their respective metropolitan areas and micropolitan areas based on
county location and were also used to develop some regional diversity measures.
Census American Community Survey
American Community Survey (ACS) Five-Year Estimates (2014-2018) data furnished by the Census
Bureau were used for all population, demographic and housing information for the SLD for the 50
States and Puerto Rico.12 Due to the extended time since the release of version 2.0 of the SLD, ACS
7 These boundaries closely mirror the 2010 Decennial CBG boundaries used for version 1.0 and 2.0, however,
there are some minor changes in geography and CBG FIPS identifiers.
8 EPA_SLD_Database_GDB_V3_UD4H_Jan_2021_Final.gdb: EPA_SLD_Database_V3_UD4H_Jan_2021_Final.
9 Not all SLD variables are available for Puerto Rico and other overseas territories of the U.S. due to a lack of
base data to calculate the measures.
10 Centers of Population. U.S. Census Bureau, 2010.
11 No updated population centers were available in 2020, so the same data used for version 2.0 of the SLD was
used for version 3.0 of the SLD. This allows for improved comparability between the different versions of the
SLD metrics.
12 ACS block group-level data is not currently acquired for the U.S. overseas territories, thus 2010 Decennial
Census information was used, where available for these areas.
7
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data provided more recent socio-demographic estimates compared to the legacy 2010 Decennial
Census13 information used before.
Longitudinal Employer-Household Dynamics
Employment information was acquired from the US Census' Longitudinal Employer-Household
Dynamics (LEHD) Origin-Destination Employment Statistics (LODES)14 database at the CBG level for
all 50 states, the District of Columbia and Puerto Rico.15 LODES version 7 block-level data from 201716
were then aggregated to the CBG geography. LODES data is separated by Work Area Characteristics
(WAC) tables for employment tabulations and Residence Area Characteristics (RAC), which identifies
the home location of workers. LODES data categorizes a range of employment types using the North
American Industry Classification System (NAICS).17 The structures and field definitions of the RAC
and WAC datasets are identical and displayed for reference in Table 2.
Table 2: Summary of LEHD LODES WAC and RAC variables.
Position
Variable Name
Type
Length
Explanation
1
h geocode
Character
15
Residence/Workplace Census Block Code
2
cooo
Numeric
8
Total Number of Jobs
6
CE01
Numeric
8
Number of jobs with earnings $1250/month or less
7
CE02
Numeric
8
Number of jobs with earnings $1251/month to $3333/month
8
CE03
Numeric
8
Number of jobs with earnings greater than $3333/month
9
CNS01
Numeric
8
Number of jobs in NAICS sector 11 (Agriculture, Forestry, Fishing and
Elunting)
10
CNS02
Numeric
8
Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil and Gas
Extraction)
11
CNS03
Numeric
8
Number of jobs in NAICS sector 22 (Utilities)
12
CNS04
Numeric
8
Number of jobs in NAICS sector 23 (Construction)
13
CNS05
Numeric
8
Number of jobs in NAICS sector 31-33 (Manufacturing)
14
CNS06
Numeric
8
Number of jobs in NAICS sector 42 (Wholesale Trade)
15
CNS07
Numeric
8
Number of jobs in NAICS sector 44-45 (Retail Trade)
16
CNS08
Numeric
8
Number of jobs in NAICS sector 48-49 (Transportation and
Warehousing)
17
CNS09
Numeric
8
Number of jobs in NAICS sector 51 (Information)
18
CNS10
Numeric
8
Number of jobs in NAICS sector 52 (Finance and Insurance)
19
CNS11
Numeric
8
Number of jobs in NAICS sector 53 (Real Estate and Rental and Leasing)
20
CNS12
Numeric
8
Number of jobs in NAICS sector 54 (Professional, Scientific, and
Technical Services)
21
CNS13
Numeric
8
Number of jobs in NAICS sector 55 (Management of Companies and
Enterprises)
22
CNS14
Num
8
Number of jobs in NAICS sector 56 (Administrative and Support and
Waste Management and Remediation Services)
23
CNS15
Num
8
Number of jobs in NAICS sector 61 (Educational Services)
24
CNS16
Num
8
Number of jobs in NAICS sector 62 (Health Care and Social Assistance)
25
CNS17
Num
8
Number of jobs in NAICS sector 71 (Arts, Entertainment, and Recreation)
26
CNS18
Num
8
Number of jobs in NAICS sector 72 (Accommodation and Food Services)
Number of jobs in NAICS sector 81 (Other Services [except Public
13 2010 Decennial Census. U.S. Census Bureau, 2010.
14 LEHD LODES. U.S. Census Bureau, 2020.
15 Unlike for previous versions of the SLD, complete employment information is available for the
Commonwealth of Massachusetts, thus no additional data sources from InfoUSA on employment information
were required. Employment information used in SLD variables was comprehensively applied to all block
groups, which was not the case in previous version of the SLD.
16 LEHD LODES typically releases block-level employment information on an annual basis and is typically at
least one year behind the ACS data releases.
17 Introduction to the NAICS. North American Industry Classification System, U.S. Census Bureau, 2020.
8
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Position
Variable Name
Type
Length
Explanation
27
CNS19
Num
8
Administration!)
28
CNS20
Num
8
Number of iobs in NAICS sector 92 (Public Administration)
Source: LODES: WAC/RAC, LEHD, U.S. Census Bureau, 2018.
HERE
EPA maintains a license for the use of the HERE Maps (formerly NAVTEQ) NAVSTREETS18 road
networking data layers. The most recent available version (release date 2018 Q4) of the NAVSTREETS
database was utilized to develop network and intersection density measures as part of the Design (D3)
SLD metrics. NAVSTREETS is a detailed nationwide street network and road network node database
with comprehensive network attribute information. These attributes include network functional class and
speed categories, direction of travel restrictions, vehicular and pedestrian restrictions, tags for highway
ramps and other variables of interest for developing a multimodal travel network and characterizing
network design.
In addition to the NAVSTREETS layer, other HERE Maps North American databases were used to
support SLD data development. These supplementary databases include polygon features for water
bodies and a park layer, and both are used in area calculations. The HERE water bodies layer was
compared with the USGS National Hydrography Dataset and the Census TIGER/Line land and water
area measures. The parks layer was compared with the Protected Areas Database for use calculating
developable area measures.
TravelTime API
Auto and transit accessibility metrics were generated from a commercial application programming
interface (API) data source19 that maintains road and transit transportation networks for all 50 U.S. states.
This data, accessed through the API, provides time-of-day specific travel speeds and travel times, and
travel distances by mode or across modes. This data source was used extensively in the development of
the destination accessibility (D5) SLD measures, as well as with some of the transit accessibility (D4)
variables. This API was used to generate:
walking travel times between CBG population centers and
driving travel times between CBGs during the AM peak period
transit travel times between CBGs during the PM peak period
Protected Areas Database
The Protected Areas Database (PAD)20 developed by the U.S. Geological Survey (USGS) is an inventory
of public lands' protection status and voluntarily provided private conservation lands in the U.S. The PAD
version 2.0, with a public release date of 2018, was used. PAD coverage extends to the 50 states, as well as
Puerto Rico and all overseas territories. This database was used to develop the unprotected land area
measure which is used as the denominator for many SLD measures, including many Density (Dl) variables
and some Transit Accessibility (D4measures.
National Hydrography Data
The National Hydrography Dataset Plus Version 221 is a joint database developed by the USGS and EPA
to support geospatial analysis of water resources and catchment areas in the US. Among the various data
layers is a polygon dataset of surface water and coastal boundaries. This dataset was used in conjunction
with the PAD data and CBG data to determine the acreage of surface water and the unprotected land area.
18 HERE NAVSTREETS. Chicago, IL, 2019.
19 TravelTime API. 2020
20 Gap Analysis Project: Protected Areas Database. USGS, 2018.
21 National Hydrology Data. USGS, 2019.
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General Transit Feed Specification
Local transit agencies use a General Transit Feed Specification (GTFS)22 to share transit schedules and
associated geographic information about transit services in a common standardized format. GTFS files
contain stop locations, stop times, routes, route types and trips, and other transit network attributes. Since
its release in the mid-2000s, GTFS has become the most recognizable and common format for transit
service data in the U.S. and internationally. GTFS data were acquired for use in metrics summarizing
transit service availability, frequency, and accessibility to destinations via transit. These data were
gathered between July and September 2020. Data were downloaded with a targeted release date of early
2020.23 Not all transit agencies in the U.S. develop GTFS data for their systems, other agencies do but do
not share it with the public, and others make it available only upon individual request. However, the large
majority of large and medium-sized transit agencies regularly update their GTFS data. GTFS data
obtained for this version of SLD represents a substantial increase from version 2.0. Version 3.0 has over
double the number of transit agencies, increasing from 228 in 2014 to 573 agencies in 2020.24 Data were
acquired from a series of different sources, including TransitFeeds25, TransitLand26 and individual transit
agency websites. Table 9 in Appendix B provides an overview of the transit agencies included in the
inventory used to develop the SLD metrics.
Transit-Oriented Development Database
The Center for Transit Oriented Development (CTOD) developed an inventory of existing, planned, and
proposed fixed-guideway transit station locations in the United States.27 This transit oriented development
(TOD) database relies on information about existing and planned federal grants for the development of
future transit systems from the U.S. Federal Transit Authority (FTA). The status of planned and proposed
stations was updated to bring them to the most current status as of mid-2020.28 The database includes
fixed-guideway transit systems such as metro (heavy rail, subway, light metro), commuter rail, light rail,
streetcars (trams, interurbans),29 bus rapid transit (BRT)30, cable cars, funiculars and aerial trams, as well
as ferry and water taxis.31 The database also includes a selected set of intercity Amtrak stations that serve
commuters. These systems include portions of the Acela, Northeast Regional and Keystone Service among
others in the Northeast and the Cascades and Capital Corridor on the West Coast. Table 8 in Appendix A
summarizes the metropolitan areas served by fixed-guideway transit used to develop transit accessibility
measures in the SLD.
National Transit Database
Public transit ridership information was gathered from the National Transit Database (NTD) developed by
22 Overview of General Transit Feed Speciation (GTFS). 2020.
23 Transit service data were targeted for February, 2020 when possible before the onset of the COVID-19
pandemic. When possible, release dates in late 2019 were preferred over those after March 2020. Some transit
agencies only offer the most recent GTFS data for download, so in some cases obtaining GTFS data from early
2020 was not possible. Some transit operators have stopped updating their GTFS data, so only legacy versions
of the data are available, some being several years old. See Table 9 in Appendix A for more information.
24 Not all GTFS data contained sufficient information to identify schedule details required for some SLD
measures. See the methods used to develop the transit accessibility (D4) and destination accessibility (D5)
measures in the section that follows.
25 TransitFeeds. OpenMobilityData, 2020.
26 TransitLand. Interline Technologies, 2020.
27 Transit-Oriented Development Database. Center for Transit-Oriented Development, 2012.
28 Planned and proposed stations were reviewed to see which were now in operation as of mid-2020. The
methods used to do this are further explained in the transit accessibility (D4) section of this document.
29 Streetcar systems do not require a dedicated right-of-way (ROW] and may operate in mixed traffic.
30 Transit agencies have varying definitions and stylization for bus rapid transit (BRT) service. To meet the
requirement for the fixed-guideway inventory, bus service must have a dedicated ROW.
31 Ferry system comprise mainly coastal urban ferry systems and long-distance ferry routes and do not include
smaller in-land ferry systems.
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the FTA.32 Ridership information is calculated as total annual (FY 2019) unlinked passenger trips by
transit agency by transit mode. These data were then summarized for all transit agencies within Census
urbanized areas (UZAs) in the country. Although not directly used as an input for SLD variables, ridership
information was compared with GTFS data coverage to provide a relative percentage of transit service
coverage by region. Over 95% of total transit ridership in the U.S. was covered by the GTFS used in the
SLD, increasing from 88% coverage for version 2.0 of the SLD in 2014. See Table 10 in Appendix C for
more information on the urbanized areas covered by the available GTFS data.
32 National Transit Database. U.S. FTA, 2020.
11
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Technical Approach
This section summarizes the methods used to calculate all variables in the SLD, including
geoprocessing components and tabular calculations. The discussion is organized by variable category
(see Table 1 for category headings and a full list of variables).
Geographic Coordinate System & Projection
All spatial analysis techniques and geoprocessing required establishing a Geographic Coordinate System
(GCS) and a Projected Coordinate System (PCS) for all spatial layers used in the SLD. The GCS used was
the North American 1983 (NAD 83).33 Several different PCSs were used for analysis to distinguish
between the lower continuous 48 states and Alaska, Hawaii and some U.S. overseas territories. The U.S.
Geological Survey (USGS) USA Contiguous Albers Equal Area Conic34 PCS was used for the contiguous
48 states, as well as Puerto Rico and the U.S. Virgin Islands. In contrast, the Alaska Albers Equal Area
Conic35 was used for Alaska, and the Hawaii Albers Equal Area Conic36 was used for analysis in Hawaii.
The World Geodetic System (WGS) 19 8 4 37 GCS and Asia South Albers Equal Area Conic38 projection
were used for U.S. overseas territories to the west of the International Dateline including Guam, American
Samoa and the Northern Mariana Islands.39 The GCSs and PCSs used for version 3.0 of the SLD are
consistent with those used for version 2.0. All CBGs were eventually merged together and the SLD is
provided in the NAD 83 GCS and the USGS USA Contiguous Albers Equal Area Conic PCS. Some base
data sources provide geographic references in WGS 1984 for latitude and longitude coordinates (e.g.,
GTFS data), which were eventually converted into NAD 83 for geoprocessing.
Administrative
Administrative variables provide classification system information for each CBG using the Federal
Information Processing Standard (FIPS) system.40 Administrative variables were from the 2019 CBG
data. FIPS codes are provided for the state, county, tract and CBG for all database records.41 In addition,
information regarding metropolitan areas including Core-Based Statistical Areas (CBSA) and Combined
Statistical Areas (CSA) was acquired from the U.S. Census Bureau.42 Also, text descriptions of the
CBSAs and CSAs were added to the SLD database. CBSA information was utilized in the development
of some employment entropy variables.
Demographics
Demographic variables are from the most recent ACS five-year estimate (2014-2018) data at the block
group-level furnished by the U.S. Census. These include population and residential activity (dwelling
units and households). Variables related to worker earnings feature the prefix "R_" to reflect that they
summarize workers by residential location using LEHD RAC tables rather than a work location (LEHD
WAC tables). The methods outlined below were the same used in the development of version 2.0 of the
SLD for consistency.
Total population of all ages [TotPop] and housing units [CountHU] were tabulated. The
33 Lower 48-state geographic coordinate system: GCS_North_American_1983.
34 Lower 48-state projected coordinate system: USA_Contiguous_Albers_Equal_Area_Conic_USGS.
35 Alaska projected coordinate system: NAD_1983_Alaska_Albers.
36 Hawaii projected coordinate system: Hawaii_Albers_Equal_Area_Conic.
37 Pacific Ocean U.S. overseas territories geographic coordinate system: GCS_WGS_1984.
38 Pacific Ocean U.S. overseas territories projected coordinate system: Asia_South_Albers_Equal_Area_Conic.
39 This projection was limitedly used due to the lack of data availability for these areas.
40 Federal Information Processing Standard. U.S. Census Bureau, 2020.
41 Two sets of FIPS block group identifiers are provided for the SLD database. The first is the original 2010 FIPS
block group identifier [GEOID10] and the second is an updated 2019 FIPS block group identifier [GEOID20], A
total of 74 (0.3%) of FIPS block group identifiers were changed. Both are required for the database because
many base data sources continue to use the 2010 FIPS block group identifier.
42 Note that these CBSA and CSA identifiers are only applied to block groups in metropolitan areas.
12
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percentage of working age [P_WrkAge] population (between 18 years and 64 years of age) was
identified.43
Auto ownership fields were derived from the ACS table B08201 and were calculated using a
two-step process. First, percent auto ownership fields were calculated as the share of all
households having zero cars [PctAOO], one car [PctAOl], or two or more cars [Pct_A02p],
with respect to total households reported in the ACS table. These percent auto ownership rates
were then applied to the housing unit count [CountHU] field of the Demographics table to
ascertain the number of households estimated to own zero cars [AutoOwnO], one car
[AutoOwnl], or two or more cars [AutoOwn2p], The process was conducted in this order
because isolated discrepancies were observed between the total number of households reported
in the ACS table and the corresponding figure in the Demographics table. The Demographics
table was given precedence, and only the auto ownership rates were taken directly from the
ACS table.
The number of workers [WORKERS] was summarized from LEHD RAC tables, which report
employment based on worker residence.
The LEHD RAC tables were also referenced to produce wage stratification variables based on
worker residence. High wage workers [R_HighWageWk] earn more than $3,333 per month,
while low wage workers [R_LowWageWk] earn $1,250 or less per month. Medium wage
workers [R_MedWageWk] earned between $1,251 and $3,333 a month. The share of total
workers consisting of low wage workers [R_PctLowWage] was also computed.
Employment
Employment information is based on LEHD LODES data for all 50 states.44 These employment
variables report job activity and worker information for each CBG. Variables summarizing worker
earnings feature the prefix "E_" to reflect that they summarize workers by employment location rather
than home location. All other employment data are from LEHD. LEHD WAC and RAC tables at the
census block-level were consolidated state-by-state into a nationwide dataset and then summarized by
CBG.
A summary of employment variables from LEHD data is provided below.
Total employment [TotEmp] was summarized for each CBG from the LEHD WAC tables, using
the C000 field (total number ofjobs).
Two employment classification systems were developed: five-tier employment and eight-tier
employment. The five-tier classification summarizes jobs into the five employment sectors: 1)
retail, 2) office, 3) service, 4) industrial, and 5) entertainment. Five-tier employment
classifications were denoted by "E5_" prefix for each employment variable. The distribution of
individual employment sectors into the five-tier employment categories from the LEHD WAC
data are shown in Table 3.
The eight-tier classification summarizes jobs into the five employment sectors: 1) retail, 2)
office, 3) service, 4) industrial, 5) entertainment, 6) education, 7) healthcare and 8) public
administration. Eight-tier employment classifications were denoted by "E8_" prefix for each
employment variable. The distribution of individual employment sectors into the eight-tier
employment categories from the LEHD WAC data are shown in Table 4.
Lastly, wage stratification variables based on workplace location were developed for each CBG.
43 Version 2.0 of the SLD characterized this variable as the proportion of the population greater than 17 years
of age. The definition of this variable was changed for SLD version 3.0.
44 No employment information is currently available at the block or CBG-level for Puerto Rico or the U.S.
overseas territories. Although, where available, some employment-based variables that also utilize
demographics information were used to calculate SLD variables for these areas.
13
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High wage workers [E_HiWageWk] earned more than $3,333 per month, while low wage
workers [E_LowWageWk] earned $1,250 or less per month. Medium wage workers
[E_MedWageWk] earned between $1,251 and $3,333 a month. The total number of workers
comprised by each wage group was tabulated for each CBG. The share of total workers
comprised by low wage workers [EPctLowWage] was also computed.
Table 3: Groups of LODES WAC characteristics to support five-tier employment entropy.
Position
Variable
Name
Type
Length
Explanation
1
h aeocode
Character
15
Residence/Workplace Census Block Code
Office .lobs
17
CNS09
Numeric
8
Number of jobs in NAICS sector 51 (Information)
18
CNS10
Numeric
8
Number of jobs in NAICS sector 52 (Finance and Insurance)
19
CNS11
Numeric
8
Number of jobs in NAICS sector 53 (Real Estate and Rental and
Leasing)
21
CNS13
Numeric
8
Number of jobs in NAICS sector 55 (Management of Companies
and Enterprises)
28
CNS20
Numeric
8
Number of jobs in NAICS sector 92 (Public Administration)
Reliiil Jobs
15
CNS07
Numeric
8
Number of jobs in NAICS sector 44-45 (Retail Trade)
Industrial .lobs
9
CNS01
N umeric
8
Number of jobs in NAICS sector 11 (Agriculture, Forestry,
Fishing and Hunting)
10
CNS02
Numeric
8
Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil
and Gas Extraction)
11
CNS03
Numeric
8
Number of jobs in NAICS sector 22 (Utilities)
12
CNS04
Numeric
8
Number of jobs in NAICS sector 23 (Construction)
13
CNS05
Numeric
8
Number of jobs in NAICS sector 31-33 (Manufacturing)
14
CNS06
Numeric
8
Number of jobs in NAICS sector 42 (Wholesale Trade)
16
CNS08
Numeric
8
Number of jobs in NAICS sector 48-49 (Transportation and
Warehousing)
Service Jobs
20
CNS12
Numeric
8
Number of jobs in NAICS sector 54 (Professional, Scientific, and
Technical Services)
22
CNS14
Numeric
8
Number of jobs in NAICS sector 56 (Administrative and Support
and Waste Management and Remediation Services)
23
CNS15
Numeric
8
Number of jobs in NAICS sector 61 (Educational Services)
24
CNS16
Numeric
8
Number of jobs in NAICS sector 62 (Health Care and Social
Assistance)
27
CNS19
Numeric
8
Number of jobs in NAICS sector 81 (Other Services [except
Public Administration!)
Entertainment, Accommodation, Food Services Jobs
25
CNS17
Numeric
8
Number of jobs in NAICS sector 71 (Arts, Entertainment, and
Source: LODES: WAC, LEHD, U.S. Census Bureau, 2018.
Table 4: Groups of LODES WAC characteristics to support eight-tier employment entropy.
Position
Variable
Name
Type
Length
Explanation
1
h geocode
Character
15
Residence/W orkplace Census Block Code
Office Jobs
17
CNS09
Numeric
8
Number of jobs in NAICS sector 51 (Information)
18
CNS10
Numeric
8
Number of jobs in NAICS sector 52 (Finance and Insurance)
19
CNS11
Numeric
8
Number of jobs in NAICS sector 53 (Real Estate and Rental and
Leasing)
21
CNS13
Numeric
8
Number of jobs in NAICS sector 55 (Management of Companies
and Enterprises)
Retail Jobs
15
CNS07
Numeric
8
Number of jobs in NAICS sector 44-45 ( Retail Trade)
Industrial Jobs
14
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Position
Variable
Name
Type
Length
Explanation
9
CNS01
Numeric
8
Number of jobs in NAICS sector 11 (Agriculture, Forestry,
Fishing and Ehmting)
10
CNS02
Numeric
8
Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil
and Gas Extraction)
11
CNS03
Numeric
8
Number of jobs in NAICS sector 22 (Utilities)
12
CNS04
Numeric
8
Number of jobs in NAICS sector 23 (Construction)
13
CNS05
Numeric
8
Number of jobs in NAICS sector 31-33 (Manufacturing)
14
CNS06
Numeric
8
Number of jobs in NAICS sector 42 (Wholesale Trade)
16
CNS08
Numeric
8
Number of jobs in NAICS sector 48-49 (Transportation and
Warehousing)
Service Jobs
20
CNS12
Numeric
8
Number of jobs in NAICS sector 54 (Professional, Scientific, and
Technical Services)
22
CNS14
Numeric
8
Number of jobs in NAICS sector 56 (Administrative and Support
and Waste Management and Remediation Services)
27
CNS19
Numeric
8
Number of jobs in NAICS sector 81 (Other Services [except
Public Administration!)
Entertainment, Accommodation, Food Service Jobs
25
CNS17
Numeric
8
Number of jobs in NAICS sector 71 (Arts, Entertainment, and
Recreation)
26
CNS18
Numeric
8
Number of jobs in NAICS sector 72 (Accommodation and Food
Services)
Education Jobs
23
CNSI5
Numeric
8
Number of jobs in NAICS sector 61 (Educational Services)
I Iealthcare Jobs
Number of jobs in NAICS sector 62 (Health Care and Social
24
CNSI6
Numeric
8
Assistance)
Public Administration Jobs
28
CNS20
Numeric
8
Number of jobs in NAICS sector 92 (Public Administration)
Source: LODES: WAC, LEHD, U.S. Census Bureau, 2018.
Area
The total geometric area of each CBG [AcTotal], the unprotected area [Ac Unpr], and land area
[Ac Land] values were calculated. [Ac Unpr] represents the total land area in the CBG that is not
protected from development activity. The area not protected from development activity was identified
using the USGS PAD-US database and is referred to as "unprotected" area in this guide. The
unprotected area represents a portion or all of the land area of a CBG but may never be more than the
CBG land area and does not consider protected areas in water bodies. Unprotected area was used in the
calculation of all density metrics (Dl), proportional area metrics for fixed-guideway transit
accessibility (D4b), and informed intrazonal travel times used in calculating the destination
accessibility metrics (D5).
Calculating the area of unprotected land for each CBG required identifying CBG areas of protected land
and surface water. A polygon GIS database was created that is the intersection between the CBGs, the
USGS Protected Area Database (PAD)45, and the USGS surface water database.46 The resulting polygon
database includes protected land polygons with CBG identifiers from which total acreage is calculated.
Unprotected land area is simply the CBG land area minus the CBG protected land area.
The PAD (version 2.0) database was prepared before simple protected area polygons were established.
PAD contains many layers of information that overlap, not all of which are relevant for establishing
protected area. A Federal Geographic Data Committee (FGDC) guidance document47 provided an
45 Protected Areas Database. USGS, 2019.
46 National Hydrology Data. USGS, 2019.
47 How to Use Protected Areas Data in Base Maps. USGS, 2019.
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overview and clarification of various PAD data layers for establishing protected area base maps. Within
this document, the following guidance was identified and followed:
Exclude "Proclamation" areas as these do not represent ownership of legal control
Exclude "Easements" area overlap private land and has a status that is unreliable
Additionally, Federal, State, and Local "Resource Management Areas" are locations where active
development may occur with some restrictions related to mining, forestry, or other commercial harvesting.
These areas were also excluded.
The PAD database documentation also clearly states that many state and local parks are missing. For this
reason, park areas were included from the 2017 NAVTEQ database. The additional NAVTEQ features
included beaches, wildlife refuges, parks, and national forest. The polygons representing these areas were
merged and dissolved with the PAD polygons to generate the final protected land polygons used in the
analysis.
Density (Dl)
All density variables summarize population, housing, or employment within a CBG per unprotected
CBG acreage [Ac Unpr], The primary density variables examine residential (housing units [Dla],
population [Dl], employment (jobs) [Die] and activity (housing units and jobs) [Did] characteristics.
Employment density is also disaggregated by employment categories for both five-tier and eight-tier job
classifications. The definitions of employment categories parallel those specified in the Employment
table. Variables with the "Dlc5..." prefix summarize employment based on the 5-tier ("E5...")
employment classification scheme. Variables with the "Dlc8..." prefix summarize employment based
on the 8-tier ("E8...") employment classification scheme.
In a few cases, unexpectedly high densities were observed in known low density areas. This occurred in
CBGs in which nearly all the land area was classified as protected area. In such cases, it was clear that
population, housing, and/or employment were present in otherwise protected areas. To correct this
overestimation of protected areas, all CBGs in which the unprotected area represented less than one
half of one percent of its total area were identified. For only these CBGs, all density metrics were re-
calculated to be based on total land area, rather than unprotected area. CBGs to which this adjustment
applied were identified using a flag [D l_Flag] and were given a value of 1.
Employment & Housing Diversity (D2)
Employment and housing diversity refer to the relative mix of employment and residential development
within an analysis zone. These measures act as proxies for land use diversity by quantifying the relative
blend of the number of jobs in different employment sectors and residential housing types. Since there is
no uniformly measured, publicly available national land use parcel database that can be allocated to the
CBG, assumptions were made about the mixture of land uses based on counts of job by employment sector
and housing unit counts. Using these employment and housing characteristics, the SLD includes a variety
of alternative metrics to measure entropy. Base data used to derive the employment and housing diversity
variables are listed in Table 2.
Table 3 and Table 4 describe the five-tier and eight-tier employment categorization used to develop the
diversity measures. Detailed descriptions and methods used to calculate the diversity variables are
provided in Table 5.
The most simplistic of the measures characterize the jobs to household balance [D2a_JpHH] and the
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workers' residential location to the employment location balance [D2a_WrkEmp] by CBG. Three trip
generation measures were also developed to quantify the average number of trips produced and attracted
by job type and household. Trip generation rates by location type were derived from the same Institute
of Transportation Engineer (ITE) Trip Generation manual used for version 2.0 of the SLD. Lastly, two
regional diversity measures were developed to quantify population, jobs and workers within each CBG
relative to the regional average. The jobs to population balance [D2r_JobPop] and worker home
residence to job location balance [D2r_WrkEmp] were calculated by comparing CBG-level values to
average values for the CBS A.
It is important to keep in mind a few things when interpreting these metrics. First, the D2 variables say
nothing about how different uses or activities are spatially distributed within a CBG. A large CBG in an
area of low density development may include a variety of different activities. But those activities may be
spatially separated within the CBG area. As a result, any given part of the CBG might have little
diversity when examined in detail. Second, in some higher density urban areas CBGs may be quite small
in area. So, a uniformly residential CBG might be located next to a CBG with a greater diversity of land
uses. These metrics will assess the residential CBG to be low in diversity even though the diverse land
uses are just a short walk away. In other words, the analysis contributing to these metrics did not
consider activities that may be in just outside of CBG boundaries. Third, the entropy formulas assess the
evenness of the distribution across the types of employment and households without consideration of the
aggregate quantity of jobs or households. For example, a CBG may have a small number of jobs
(relative to another CBG), but if the mixture of these jobs is present in the same ratio as in a CBG with
more jobs, they will have the same mix score.
Table 5: Detailed description of employment and housing diversity (D2) variables.
Fieldname
Description
Method of calculation
D2a JpHH
Jobs to Household Balance per CBG
TotEmp/HH
D2b_E5Mix
This employment mix (or entropy) variable uses
the five-tier employment categories (Error!
Reference source not found.) to calculate
employment mix. The entropy denominator is
set to observed existing employment types
within each CBG.48
D2b_E5Mix = -E/(ln(N))
Where: E=(E5 Ret/TotEmp)*ln(E5 Ret/TotEmp) +
(E5_Off/TotEmp)*ln(E5_Off/TotEmp) +
(E5_Ind/TotEmp)*ln(E5_Ind/TotEmp) +
(E5_Svc/TotEmp)*ln(E5_Svc/TotEmp) +
(E5_Ent/T otEmp)*ln(E5_Ent/T otEmp)
N= number of employment types with employment > 0.
D2b_E5MixA
This entropy variable uses the five-tier
employment categories to calculate employment
mix. The entropy denominator is set to all five
employment types within each CBG.
D2b_E5MixA = -E/(ln(5))
Where: E=(E5 Ret/TotEmp)*ln(E5 Ret/TotEmp) +
(E5_Off/T otEmp)*ln(E5_Off/T otEmp) +
(E5_Ind/TotEmp)*ln(E5_Ind/TotEmp) +
(E5_Svc/TotEmp)*ln(E5_Svc/TotEmp) +
(E5 Ent/TotEmp)*ln(E5 Ent/TotEmp)
D2b_E8Mix
This entropy variable uses the eight-tier
employment categories (Error! Reference
source not found.) to calculate employment
mix. The entropy denominator is set to observed
existing employment types within each CBG.
D2b_E8Mix = -E/(ln(N))
Where: E=(E8 Ret/TotEmp)*ln(E8 Ret/TotEmp) +
(E8_Off/TotEmp)*ln(E8_Off/TotEmp) +
(E8_Ind/TotEmp)*ln(E8_Ind/TotEmp) +
(E8_Svc/TotEmp)*ln(E8_Svc/TotEmp) +
(E8_Ent/TotEmp)*ln(E8_Ent/TotEmp) +
(E8 Ed/TotEmp)*ln(E8 Ed/TotEmp) +
(E8_Hlth/TotEmp)*ln(E8_Hlth/TotEmp) +
(E8_Pub/T otEmp)*ln(E8_Pub/T otEmp)
48 This entropy equation was originally applied by Robert Cervero and has been used since then in different
land use entropy formulations. [Cervero, R. (1989). Land-Use Mixing and Suburban Mobility. UC Berkeley:
University of California Transportation Center. Retrieved from https: / /escholarship.org/uc/item/4nf7klv9]
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Fieldname
Description
Method of calculation
N= number of the employment types with employment > 0.
D2b_E8MixA
This entropy variable uses the eight-tier
employment categories to calculate employment
mix. The entropy denominator is set to all eight
employment types within each CBG.
D2b_E8MixA = -E/(ln(8))
Where: E=(E8 Ret/TotEmp)*ln(E8 Ret/TotEmp) +
(E8_Off/T otEmp)*ln(E8_Off/T otEmp) +
(E8_Ind/TotEmp)*ln(E8_Ind/TotEmp) +
(E8_Svc/TotEmp)*ln(E8_Svc/TotEmp) +
(E8_Ent/TotEmp)*ln(E8_Ent/TotEmp) +
(E8_Ed/TotEmp)*ln(E8_Ed/TotEmp) +
(E8_Hlth/TotEmp)*ln(E8_Hlth/TotEmp) +
(E8 Pub/TotEmp)*ln(E8 Pub/TotEmp)
D2a_EpHHm49
Employment and household entropy
calculations, where employment and occupied
housing are both included in the entropy
calculations. This measure uses the five-tier
employment categories.
D2a_EpHHm = -A/(ln(N))
Where:
A = (HH/T otAct)* ln(HH/T otAct) +
(E5 Ret/TotAct)*ln(E5 Ret/TotAct) +
(E5 Off/TotAct)*ln(E5 Off/TotAct) +
(E5_Ind/T otAct)*ln(E5_Ind/T otAct) +
(E5_Svc/TotAct)*ln(E5_Svc/TotAct) +
(E5_Ent/T otAct)*ln(E5_Ent/T otAct)
TotAct = TotEmp + HH
N= number of activity categories (employment or households)
with count >0.
D2c_TrpMxl50
Employment and household entropy
calculations, based on trip production and trip
attractions, including five-tier employment
categories. The vehicle trip productions and
attractions are derived by multiplying the
average Institute of Transportation Engineers
(ITE) vehicle trip generation rates by
employment types and households. The trip
generation rates were used as a proxy for trip
activity.
D2c_TrpMxl = - [H(VT) +E(VT)]/(ln(6))
Where:
H(VT) + E(VT) =
(HH* 11/ TotVT)*ln(HH* 11/ TotVT) + (E5 Ret*22/
TotVT)*ln(E5 Ret*22/TotVT) + (E5 OfP3/
TotVT)*ln(E5 Off*3/ TotVT) + (E5 Ind*2/
TotVT)*ln(E5 Ind*2/TotVT) + (E5 Svc*31/
TotVT)*ln(E5 Svc*31/TotVT) + (E5 Ent*43/
TotVT)*ln(E5_Ent*43/ TotVT)
TotVT = Total trips generated (production and attraction) for
all activity categories in the CBG based on ITE Trip
Generation Rates (rates shown in equation above).51
D2c_TrpMx252
Employment and household entropy
calculations, based on trip productions and trip
attractions, including 4 of the 5 employment
categories (excluding industrial). The vehicle
trip productions and attractions are derived by
multiplying the average Institute of ITE vehicle
trip generation rates by employment types and
households. The trip generation rates were used
as a proxy for trip activity.
Employment and Household Trips Mix = - [H(VT)
+E(VT)]/(ln(5))
Where:
H(VT) + E(VT) =
(HH* 11 /VT)*ln(HH* 11/VT) +
(E5 Ret*22/TotVT)*ln(E5 Ret*22/TotVT) + (E5 Off*3/
TotVT)*ln(E5 Off*3/TotVT) + (E5 Svc*31/
TotVT)*ln(E5 Svc*31/TotVT) + (E5 Ent*43/
TotVT)*ln(E5_Ent*43/ TotVT)
TotVT = Total trips generated (production and attraction) for
all activity categories (excluding industrial jobs) in the CBG
based on ITE Trip Generation Rates.
49 Only accounts for households in Puerto Rico and the U.S. overseas territories due to a lack of employment
data in these regions.
50 Only accounts for households in Puerto Rico and the U.S. overseas territories due to a lack of employment
data in these regions.
51 Trip generation rates used previously for version 2.0 of the SLD were used again for version 3.0.
52 Only accounts for households in Puerto Rico and the U.S. overseas territories due to a lack of employment
data in these regions.
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Fieldname
Description
Method of calculation
D2c_TripEq53
Trip Equilibrium Index. It is derived by
calculating trip productions and trip attractions
by CBG; the closer to one, the more balanced
the trip making at the CBG level. The vehicle
trip productions and attractions were derived by
multiplying average ITE vehicle trip generation
rates by employment types and households. The
trip generation rates were used as a proxy for
trip activity.
D2c_TripEq = exp{ - |[H(VT)/E(VT)]-1|)
Where:
HH(VT) = Productions: total occupied household units
in CBG * ITE Vehicle Trip (VT) Generation Rates.
J(VT) = Total trip attractions for the five-tier employment
(job) categories based on ITE Trip Generation Rates.
exp = the exponential function (e [approximately 2.718281828]
raised to the power of the number in parenthesis)
D2r_JobPop54
Regional diversity of employment to
population. Calculated based on total population
and total employment by CBG. It quantifies the
deviation of the CBG ratio of jobs/pop from the
regional average ratio of jobs/pop.
D2r_JobPop =1- |(b*(TotPop-
T otEmp))/(b*(T otPop+T otEmp))|
Where b=CBSA_Pop/CBSA_Emp
D2r_WrkEmp55
Regional diversity of household workers to
employment. Household Workers per Job, as
compared to the region. It quantifies the
deviation of CBG ratio of household
workers/job from regional average ratio of
household workers/job.
D2r_WrkEmp =1- |(b* (Workers -
TotEmp))/(b*(Workers +TotEmp))|
Where b=CBSA_Wrk/CBSA_Emp
D2a WrkEmp
Household Workers per Job, by CBG.
D2a WrkEmp = Workers/TotEmp
D2c_WrEmIx
Working population and actual jobs equilibrium
index. The closer to one, the more balanced the
resident workers and jobs are in a CBG.
D2c_WrEmIx = ex/>(-|(Workers/TotEmp ) -1|)
Where exp = the exponential function (e [approximately
2.7182818281 raised to the power of the number in parenthesis)
Urban Design (D3)
Urban design variables measure connectivity or the ability to traverse distances in many directions
along a street network. Areas with higher connectivity typically have a gridded street network with
shorter block lengths than more disconnected areas with fewer intersections and longer block lengths.
The urban design (D3) variables measure connectivity in terms of street network density and street
intersection density by facility orientation. The street network is categorized into three distinct facility
orientation types: 1) automobile, 2) multi-modal and 3) pedestrian. The denominator used in street
network density [D3a] and street intersection density [D3b] calculations was total land area [Ac Land].
Additionally, street intersection density [D3b] also summarizes total intersection density weighted to
emphasize pedestrian and bicycle travel connectivity. While intersection density is often used as an
indicator of more walkable urban design and the source network database includes pedestrian or non-
motorized pathways only in addition to streets which vehicles can traverse. However, it is important to
note that the source data provides no information regarding the presence or quality of sidewalks.
The urban design variables required substantial preparation of the HERE Maps NAVSTREETS
databases56 to assign each network feature's facility orientation. The Streets layer displayed network
links and includes a link-level attributes such as functional class, speed category, direction of travel
(one-way or two-way), auto or pedestrian restrictions, and identifiers for ramps, tunnels, and bridges.
The Zlevels layer displayed all points of articulation on the network (node junctions) and included
node-level attributes such as intersections, node identifiers, link identifiers, and relative elevation fields
to govern connectivity at coincident grade separated nodes.
53 Only accounts for households in Puerto Rico and the U.S. overseas territories due to a lack of employment
data in these regions.
54 This measure is not calculated for block groups in rural areas or small towns that are not part of CBSA.
55 This measure is not calculated for block groups in rural areas or small towns that are not part of CBSA.
56 Mainly the Streets (polyline network) and Zlevels (network node junctions) datasets.
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Node features were stacked with each feature representing an endpoint of a particular link in the Streets
layer. Thus, where three or more coincident node features were found, at least three associated links and
their descriptive attributes could be related to that point, which would (in most cases) represent a three-
way or more intersection. This relationship between the Streets and Zlevels layers allowed street network
and intersections to be summarized by type.
Preparing the network base data to process the SLD design metrics required several steps. First, street
centerlines were grouped into three facility categories: 1) auto-oriented links, 2) multi-modal links, and
3) pedestrian-oriented links. Then the link length by facility category was summed to obtain total facility
miles by type for each CBG. Next, link-level facility groups were joined to the Zlevels layer based on
link identifier. Finally, intersections were counted in each CBG based on the types of facilities found at
the intersection and the number of legs at the intersection (for multi-modal and pedestrian-oriented
intersections only). The summary figures of facility miles by type and intersection total by type and
number of legs were divided by the total land area for each CBG to obtain network density (facility miles
per square mile) and intersection density (intersections per square mile) for each CBG.
Links were grouped into facility categories as follows:
Auto-Oriented Facilities:
o Any controlled access highway, tollway, highway ramp, or other facility on which
automobiles are allowed but pedestrians are restricted
o Any link having a speed category value of 3 or lower (speeds are 55 mph or higher)
o Any link having a speed category value of 4 (between 41 and 54 mph) where car travel
is restricted to one-way traffic
o Any link having four or more lanes of travel in a single direction (implied eight lanes
bi-directional - turn lanes and other auxiliary lanes are not counted)
o For all of the above, ferries and parking lot roads were excluded.
Multi-Modal Facilities:
o Any link having a speed category of 4 (between 41 and 54 mph) where car travel is
permitted in both directions
o Any link having a speed category of 5 (between 31 and 40 mph)
o Any link having a speed category of 6 (between 21 and 30 mph) where car travel is
restricted to one-way traffic
o For all of the above, autos and pedestrians must be permitted on the link
o For all of the above, controlled access highways, tollways, highway ramps, ferries,
parking lot roads, tunnels, and facilities having four or more lanes of travel in a single
direction (implied eight lanes bi-directional) are excluded
Pedestrian-Oriented Facilities:
o Any link having a speed category of 6 (between 21 and 30 mph) where car travel is
permitted in both directions
o Any link having a speed category of 7 or higher (less than 21 mph).
o Any link having a speed category of 6 (between 21 and 30 mph)
o Any pathway or trail57 on which automobile travel is not permitted (speed category 8).
o For all of the above, pedestrians must be permitted on the link
o For all of the above, controlled access highways, tollways, highway ramps, ferries,
parking lot roads, tunnels, and facilities having four or more lanes of travel in a single
direction (implied eight lanes bi-directional) are excluded
57 While NAVTEQ data does include some pedestrian pathways and bicycle trails, coverage is far less
comprehensive than it is for automobile facilities. When these bike/ped facilities do exist in the NAVTEQ
database, they are considered in SLD metrics.
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Street network density measures were calculated by summing links from all three categories described
above and dividing by land area in square miles. Four network density measures were created: 1) total
network density (all facility types) [D3a], 2) auto-orientated network density [D3aao], 3) multi-modal
network density [D3amm], and 4) pedestrian-orientated network density [D3apo].
To identify intersections by facility type, the network links were first joined to the Zlevels layer. Link
nodes at intersections were queried out of the Zlevels layer and then spatially dissolved into discrete
intersections based on the node identifier and Zlevel attributes (the latter ensuring that duplicate grade-
separated nodes were not counted as one intersection). Intersection nodes at roundabouts were
maintained,58 while nodes identified on "manoeuvre"59 links were removed as invalid intersections.
For each intersection, the total number of intersecting links (legs) were summarized to and any nodes
with fewer than three legs were discarded. Intersections were then summarized by type for each CBG,
which are also summarized in Table 6:
Intersections at which auto-oriented facilities met or at which auto-oriented facilities
intersected multi-modal facilities were described as auto-oriented intersections and summed
for each CBG regardless of the total number of legs.
Intersections at which multi-modal facilities met or at which multi-modal facilities
intersected pedestrian oriented facilities were described as multi-modal intersections and
summed for each CBG where the number of legs was equal to three and where the number
of legs was greater than 3.
Intersections at which pedestrian-oriented facilities met were described as pedestrian-
oriented intersections and summed for each CBG where the number of legs was equal to
three and where the number of legs was greater than 3.
Table 6: Summary of intersection density measures by type groupings and corresponding urban design variables.
Intersection Type
Legs
Intersectin
g Facilities
Variable Name
Auto
N/A
Auto
Auto
D3bao
Auto
Multi-Modal
Multi-Modal: 3-leg
3
Multi-Modal
Multi-Modal
D3bmm3
Multi-Modal
Pedestrian-Oriented
Multi-Modal: 4-leg
>4
Multi-Modal
Multi-Modal
D3bmm4
Multi-Modal
Pedestrian-Oriented
Pedestrian-Oriented: 3-leg
3
Pedestrian-Oriented
Pedestrian-Oriented
D3bpo3
Pedestrian-Oriented: 4-leg
>4
Pedestrian-Oriented
Pedestrian-Oriented
D3bpo4
Finally, the total number of intersections was systematically discounted in some cases to account for an
overestimation due to divided highways portrayed as individual one-way links. Thus, when an undivided
street intersected a divided highway, it intersected it in two places, at the "from-bound" link and at the
"to-bound" link causing duplication. These locations should be interpreted as a single intersection, but
they would be tabulated as two intersections in the processes described above. This effect was further
compounded when two divided highways intersect each other.
To account for this condition, individual intersections were discounted based on the number of one-way
links found at the intersection. Where a one-way link intersected a two-way link, the intersection was
counted as half an intersection; and where two one-way links intersected, the intersection was counted as
a quarter of an intersection. This prevented intersection counts in areas with a high density of auto-
oriented facilities (such as in the vicinity of a freeway interchange) from being overestimated. Since most
of these types of intersections were found among auto-oriented facilities, this discount weight primarily
58 May result in some minor duplication of multiple link nodes representing one intersection.
59 Type of link classification for turn lanes in the center of streets typically located mid-block for entrances into
driveways.
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affected auto-oriented intersection counts, though some reduction in the number of multi-modal and
pedestrian-oriented facilities also resulted from the application of this rule.
Street network intersection density [D3b] was calculated by creating a weighted sum of component
intersection density metrics. Auto-oriented intersections were assigned zero weight to reflect that, in
many instances, auto-oriented intersections are a barrier to pedestrian and bicycle mobility. Also, since
three-way intersections do not promote street connectivity as effectively as four-way intersections, their
relative weight was reduced accordingly.60 The formula for [D3b] was calculated as follows:
1)3b = (D3bmm3 *0.667) + Dbmm4 + (D3bpo3 *0.667) + D3bpo4
Transit Accessibility (D4)
Transit service (D4) variables measure availability, proximity, frequency, and density of all public
transit services. Two data sources were used to calculate transit metrics. First, transit service data was
obtained in GTFS format from over 500 transit agencies61 across the U.S. As part of the GTFS
inventory, these data included the geographic location of all transit stops, as well as service schedules
for all routes that serve those stops. Metrics that rely on transit service schedules ([D4a], [D4c], [D4d],
and [D4e]) reflect GTFS data availability and completeness.62 While all agencies follow the data
definition standard, some agencies left values empty that were critical for building schedules,
identifying valid services/routes or identifying transit stop departures by hour of day. See Table 9 in
Appendix B for more information on which transit agencies are included within the SLD.
Secondarily, point location data of latitude and longitude coordinates were also obtained for all
existing fixed-guideway transit service. Access to this type of transit service ([D3b025], [D3b050])
includes all rail transit (metro, light rail, streetcar, etc.), ferry and water taxis, and some bus rapid
transit systems with dedicated right-of-way. All transit stops from the CTOD TOD database classified
as existing were included in the database of fixed-guideway transit stations. Since no updates to this
database had been performed since 2012, planned and proposed fixed-guideway transit systems were
reviewed to identify those systems that have now been brought into service through 2020. Route type
information was then gathered to select non-bus stops from the GTFS stop inventory to identify any
other remaining fixed-guideway stations omitted from the CTOD TOD database.63 A spatial selection
was performed between the TOD database and the selected set of stops from the GTFS database to
ensure no duplication of stops. See Table 8 in Appendix A for more information on the regions that
have fixed-guideway transit service included in the SLD.
Distance to Nearest Transit (D4a)
Distance to the nearest transit stop [D4a] measures the minimum walk distance in meters between the
2010 population-weighted CBG centroid (as used by SLD version 2.0) and the nearest transit stop of any
route type. To generate this metric, a custom geoprocessing model script was run. This processing model
iteratively selected CBG centroids and identified all transit stops (from any GTFS file) within a three-
quarter mile straight-line radius (approximately a 15-minute walk). The resulting sets of CBG to transit
stop pairs were passed to the TravelTime API to estimate the walking distance between them in meters.
The pedestrian network used by the TravelTime API includes walkable roads as well as pedestrian only
60 The weight of three-way intersections was diminished by one third. This weight was chosen to reflect the
diminished choice of routes that a traveler faces when reaching a 3-way intersection when compared to a 4--
way intersection (2 choices instead of 3).
61A full list of transit agencies with GTFS data reflected in these metrics is available in Table 9 in Appendix B.
62 Although the SLD relies on GTFS data from 573 transit agencies, only 499 contained sufficient information to
identify schedule details required for some SLD measures ([D4c], [D4d], [D4e] and [D5br], [D5be]).
63 Note that route information is often, but not always, included in GTFS data published by transit agencies.
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facilities. Note that the initial selection of destinations was based on a straight-line distance, whereas the
network solve is limited to finding those pre-selected destinations that are a 15 minute walk from a transit
stop based on network distances. The initial selection is made simply to limit the number of potential
destinations that are added to the OD matrix network problem.
The network analysis results were appended to a master table of stop-CBG OD pairs with the network
travel distance included as an attribute. When all stop-CBG OD pairs had been found and listed in the
master table, the table was then summarized by CBG to find the minimum network travel distance to a
transit stop from that CBG centroid. This is the only measure in the SLD where the lower the value in
each CBG, the better the access (in this case to nearby transit).64 All CBGs with population-weighted
centroids that were further than three-quarter miles from a transit stop were assigned a value of "-
99999 "65
Since the network problem was solved based on distance rather than travel time, there was no
accounting for delays at intersections or bordering or alighting delays in determining the shortest path
between a stop origin and CBG population-weighted centroid destination. The inclusion of stations
from the CTOD TOD Database allowed stops that have fixed- guideway transit, but which do not
provide GTFS data, to be included in the distance to nearest transit [D4a] tabulation.
Access to Fixed-Guideway Transit (D4b)
Fixed-guideway transit station locations (derived from the CTOD TOD Database and the GTFS database)
were buffered using a crow-fly distance of one-quarter of a mile and then again at one-half of a mile.
Each respective set of buffers was spatially intersected with the CBG unprotected areas polygons
developed unprotected area variable. The resulting intersected features represent the polygons formed by
the intersection of the CBG boundaries, all unprotected areas, and the transit station area crow-fly
buffers. The area of each polygon was compared to the unprotected area of its corresponding CBG to
determine the proportion of the polygon's unprotected area that is found within one-quarter or one-half
mile of a fixed-guideway transit station. This value approximates the proportion of the CBG's activity
(housing units and total employment) that were proximate to fixed-guideway transit.
The station area buffers were based on crow-fly distances, not network distances. The process could be
improved in future versions of the SLD to include the development of network-based service area
polygons around transit stations. A second potential improvement would involve assessing developed
area in a CBG based on land cover data to define the portions of the CBG in which activities are located
rather than referencing the CBG's unprotected area. However, this augmentation is expected to require a
substantially higher level of effort to develop than that associated with defining protected areas.
Access to fixed-guideway stations within 0.25 miles [D4b025] and 0.50 miles [D4b050] were reported as
proportions (values range from zero to one). These proportions may be applied to the CBG's activity
variables (demographics and employment) to approximate the number of housing units and jobs that
CBG contains that are located near rapid transit stations.
Aggregate Frequency of Peak Hour Transit Service (D4c)
GTFS transit schedule information was analyzed to calculate the frequency of service for each transit
route during the weekday evening peak hour (4:00PM and 7:00PM local time). Transit routes with
service that stops within 0.4 km (0.25 miles) crow-fly distance from the boundary of the CBG were then
identified. Lastly, total aggregate service frequency was summed by CBG. Values for this metric are
64 Some CBGs have a value of "0" (indicating transit stops in close proximity) due to population-weighted
centroids snapping directly on top of nearby transit stops on the network.
65 A value of "-99999" was assigned to SLD transit-based variables (D4 and D5b, D5d) that exceeded distance
thresholds or did not have GTFS data coverage. Shoreline or CBGs in water bodies with no land area, no
population and no jobs were also assigned a value of "-99999".
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expressed as service frequency per hour of service. CBGs in areas that do not have transit service were
assigned the value "-99999." Due to the distance threshold and buffer type differing from access to
nearest transit stop, it may be the case that some CBGs have a valid access to nearest transit [D4a] value
while not having an aggregate frequency of transit service [D4c] value or vice versa.
Aggregate Frequency of Peak Hour Transit Service Density (D4d)
This measure applies density characteristics to aggregate transit service frequency per square mile. This
metric was calculated by dividing aggregate transit service frequency [D4c] by total land acreage
[AcLand], then converting to units per square mile. In a few instances where a CBG had no land acreage
([AcLand] = 0), total CBG polygon acreage [Ac Total] was used as the denominator.66 CBGs in areas
that did not have transit service were given the value "-99999."
Aggregate Frequency of Peak Hour Transit Service per Capita (D4e)
Aggregate transit service frequency per capita [D4e] divides aggregate transit frequency [D4c] by total
population [TotPop]. In the few instances where there was transit access and no population ([TotPop] =
0), the per capita transit access was set to 0.67 All CBGs in areas where GTFS service data were
unavailable were assigned a value of "-99999."
Destination Accessibility (D5)
The most sophisticated variables to be included in the SLD address CBG-to-CBG accessibility. The
primary variables ([D5ar], [D5ae], [D5br], [D5be]) all measure jobs or working-age population within
a 45- minute commute via automobile (D5a) or 45 minute commute on a transit vehicle (which can be
up to a 90 minute total travel time when walking access, walking egress, wait and transfer times are
included) (D5b). Variable names with an "r" reflect accessibility from residences to jobs. Variable
names with an "e" reflect accessibility from employment locations to working-age population (ages
18-64). A travel-time decay formula is used in each calculation to weight jobs/population closer to the
origin CBG more heavily than those further away. D5c and D5d measure accessibility relative to other
CBG within the same metropolitan region (CBSA). The approach to developing each of these
measures is described below.
Destination Accessibility via Automobile Travel (D5a)
A geoprocessing model was developed using the TravelTime API to facilitate the calculation and
tabulation of auto-accessible CBGs from a given origin CBG within a 45-minute drive time. The
processing iterated through each CBG to identify candidate accessible CBGs (within a 45-mile radius).
Auto travel times were then estimated for trips starting at each origin CBG at 8:00 AM local time (typical
non-holiday Tuesday) and ending at each candidate destination CBG. If each trip's travel time was 45
minutes or less, the candidate CBG was selected as a match. For each match, the time-weighted access to
jobs [D5ar] and working age population [D5ae] values were recorded. Values for all matches from each
CBG origin were summed to estimate the final [D5ar] and [D5ae] accessibility metrics.
The decay function used to adjust accessibility values (population or employment) was the same equation
used in SLD version 2.0. The original decay formula was derived from the report "Travel Estimation
Techniques for Urban Planning" (NCHRP Report 365, Transportation Research Board, 1998) and is
displayed below:
n
D5 Acq=^£mpj * f(d)ij
j=i
66 In the case where a CBG had no land area, no population and no jobs, the CBG was assigned a value of
99999."
67 It is possible that some block groups that have transit service frequency in urbanized areas, however, they
have no population and only have employment. These block groups were denoted as having a transit service
frequency per capita of 0 in contrast to -99999 for block groups without transit service.
24
-------
where
1)5 Acd is the destination accessibility for CBG
Empj is the measure of Working-Age Population in the CBG /. and
/(d)ij is the measure of impedance between CBG / and CBG j.
f(ci)ij=a*dij"b*e-c*{d'))
Where, a = 1, b = 0.300, and c= 0.070; please note that e, is the exponential function.
This function f(d)y produces the curve displayed in Figure 1. The equation emphasizes close proximity,
decaying rapidly as travel time increases up to about 10 minutes, at which point the friction resulting from
marginal increases in travel time begins to ease. The decay factor approaches zero as travel time increases
beyond 40 minutes.
25
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Figure 1:2017 NHTS travel time distance decay based on reported commute travel times.
u
CG
|J-
cc
u
o
Q
o
u
c
re
¦w
s
NCHRP 365 Distance Decay
Travel Time in
The origin-destination (OD) matrix development process did not account for intrazonal travel. Although
rows were added to the matrices where the destination CBG centroid was the same as the origin CBG
centroid, the travel time reported for the OD pair was zero. A travel time of zero cannot be weighted using
the distance decay formula described above, so to account for intrazonal destinations, intrazonal travel time
26
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was estimated for each CBG. The formula for estimated intrazonal travel time was also taken from NCHRP
365:
r- 60
TJz=0.5*>/a[*
where
/,- is the intrazonal travel time for CBG / in minutes,
Ai is the unprotected area of CBG / in square miles, and
Si is the estimated travel speed within CBG / in miles per hour.
27
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This equation required the estimation of a typical intrazonal travel speed. This was accomplished by
classifying each CBG as "urban," "suburban," or "rural" based on activity density in the CBG. Activity
densities were joined from the D1 - Density table and represent the total number of jobs and dwelling
units per unprotected acre for each CBG. CBGs, where total activity density was less than 0.5 activity
units per unprotected acre, were deemed "rural" and assigned an intrazonal travel speed of 35 miles per
hour. CBGs with activity densities higher than six units per unprotected acre were classified as urban and
assigned a travel speed of 15 miles per hour. All other CBGs were classified as suburban and assigned an
intrazonal speed of 25 miles per hour. These designations were developed through visual inspection of
areas well known to the study team. They only influenced the tabulation of intrazonal travel times and
were not used in any other part of the analysis.
After all travel times had been fully tabulated - whether intrazonal derived from equations or intrazonal
derived from the network analysis model - employment and working age population totals at destination
CBGs were weighted by the decay curve described above and summed for each origin CBG. The sum of
time-decayed employment accessible from each CBG is reflected in the variable [D5ar]; the
corresponding figure for working-age population accessible from each CBG is reflected in the variable
[D5ae].68
Destination Accessibility via Transit (D5b)
Transit accessibility was assessed in essentially the same way as auto accessibility, although the
development of CBG to CBG OD matrices was more complex. The following steps were applied to
generate the D5b estimates:
1. All CBGs population centroids that were within a 15-minute walking distance of any transit stop
(from the full set of all transit agencies with GTFS data) were selected and defined as trip end
points.
2. The TravelTime API was used within a processing engine script to iterate through each trip
endpoint, defining that point as a home destination. Candidate trip origins (work locations) were
selected using a 45-mile radius (same values used in defining D5a candidates). A geoprocessing
model used the TravelTime API to estimate origin (work) to destination (home) travel times and
distances for transit trips starting between 5:00 PM and 5:45 PM (typical Tuesday in October, 2020).
Results that contained origin-to-boarding or alighting-to-destination walk times greater than 15
minutes were excluded. Similarly, trips with time spent on a transit vehicle greater than 45 minutes
were also excluded. A single transfer was allowed within the same transit system or to a separate
transit system. The shortest valid travel time for each OD pair was saved. It should be noted that
this estimate includes non-transit walk trips to candidate CBGs if that walk trip was less than 15
minutes.
3. Given that the TravelTime API was applied to conditions during the 2020 COVID-19 pandemic
when some transit agencies reduced service, an effort was made to identify those changes and adjust
values using older GTFS data from pre-COVID-19 periods. To test this, a second set of OD
calculations was run for each valid OD candidate pair that was available in the raw GTFS files. This
method used the same parameters as step #2 (TravelTime API) but was processed entirely using a
local script. If the resulting set of OD pairs was larger using this approach, then the values from this
approach were selected instead of the TravelTime API approach described in step #2. A primary
and significant difference in this approach compared to the API approach is that transfers across
separate GTFS files (different transit operators) were not possible using static and historic GTFS
data, but they are available through the API. For this reason, the API approach was given first
priority.
4. Once the final sets of matching OD pair travel times were identified, a time decay function was
applied to adjust the access to employment and working-age population. The time-decay function
applied to transit trips is different than the one applied to auto trips. The transit travel time decay
68 CBGs in shorelines or water bodies with no land area, no population and no jobs were assigned a value of "0."
28
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function was generated from the 2017 National Household Travel Survey, where transit journey
times were recorded for participants who made home to work trips. Figure 2 shows three different
time decay functions. The decay function used for SLD version 2.0 and the 2020 auto work trip
decay function is shown in red. The blue line shows the time decay function based on NHTS transit
trips and was applied to the 2020 estimates of D5b job and worker accessibility. The green line is
time decay for auto trips based on NHTS data and is only shown for comparison and was not used
for any analysis.
TIME DECAY FUNCTIONS
100%
80%
60%
40%
20%
0%
SS <¦%
V ^ V
Jo
*
Transit - NHTS
Auto-NHTS
Auto - NCHRP
Poly. (Transit - NHTS)
Poly. (Auto - NHTS)
<& or .cP jr ,*v0 .v /t?
\ \ "v > *v Jv
¦V
20%
MINUTES
Figure 2: Graph demonstrating the various time decay functions by source.
5. Just as in the auto accessibility calculations, intrazonal employment and population accessibility
estimates were added to the transit accessibility estimates to account for non-auto access within a
block group.
In summary, the transit analysis focused on the basic phases of a transit trip: walking to access transit
service, the in-vehicle trip, walking and/or waiting to make a transfer, the second in-vehicle trip (where
available), and walk egress from a transit stop to a destination. Each phase is described below.
Walk Access to Transit
Walk access to transit was modeled as the network distance from a CBG population centroid to each
accessible (within a 15-minute walk allowance) transit stop using either the TravelTime API or the
static GTFS data set. A standard wait time of 5 minutes to make the first boarding was allowed.
In-Vehicle Time (first trip)
From walk accessible stops, additional ride accessible stops were located. These were stops to which a
traveler could ride from the walk accessible stops based on the transit trips serving those stops. The
maximum in-vehicle time permitted was 45 minutes. The total amount of in-vehicle time from the walk
29
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accessible stop of origin was retained when modeling transfer opportunities.
Transfers
For all ride accessible stop events, there may exist transfer opportunities. Ten minutes total transfer time
was permitted, of which five could be spent walking to make the transfer. Transfers across transit systems
from different operators were allowed for the TravelTime API estimates. Transfers using the static GTFS
file method were only allowed within the same, single operator transit system.
In-Vehicle Time (second trip)
A maximum of 45 minutes in-vehicle time was allowed. Thus, the stops accessible by riding during the
second trip had to be reachable within 45 minutes minus the time spent on the trip's first in-vehicle leg.
Stop events were linked to their stop locations, and pairs were summarized to find the fastest travel time
between stop locations by any combination of walking, riding, and transferring during the analysis time
period (PM peak).
Walk Egress
Walk egress is developed using the same data as the walk access to transit, assuming that the alighting to
destination walk time is the same travel time as the reverse direction. The TravelTime API approach
provided total walk time and distance for each calculation, allowing controls of walk times and distances.
Walk Competitiveness
For some OD pairs - especially in highly urbanized areas - walk travel times to neighboring CBGs
were expected to be competitive with transit travel times, especially considering the five minute wait
time required for the first boarding of a transit vehicle in the transit accessibility analysis. Thus, walk
times between neighboring CBGs were analyzed for all CBGs that had some access to transit. A
maximum 15 minute walk from origin to destination was permitted. The minimum travel time between
zones by transit or by walking was compared and walking travel time was selected if it was more
expedient than transit.
Transit accessibility was analyzed for the PM peak travel period only, as typically, this is a period of
relatively intense transit service levels and during which a rich mix of commuting and discretionary trip-
making occurs. GTFS schedules were queried to isolate trips and their related stop events within the 5:00
PM to 6:30 PM time frame. There is no hard and fast departure time from the CBG origin. Rather, since all
possible permutations of traveling by transit between stops were analyzed, the CBG to CBG travel times
reported in the final matrix reflect the optimal transit trip connecting those CBGs in the PM peak period.
The first transit trips had to be boarded prior to 5:45 PM. These and other key parameters of the transit
analysis, as described herein, are summarized in Table 7.
Table 7: Attributes and Parameters of Transit Accessibility Analysis.
Full Travel Period
5:00 PM to 6:30 PM
Travel Period of Walk Departure from CBG origin
5:00 PM to 5:45 PM
Travel Period of First Trip Boarding
5:00 PM to 5:45 PM
Maximum Possible Total Travel Time for the Transit Trip
90 minutes
Maximum Walk Time Allowed for Access
15 minutes
Wait time to Board First Trip
0-5 minutes
Maximum Total In-Vehicle Travel Time
45 minutes (first and second trips
combined)
Number of Transfers Allowed
1
Maximum Time Allowed for Waiting to Make a Transfer
10 minutes
Maximum Time Allowed for Walking to Make a Transfer
(subsumed within time for waiting to make a transfer)
5 minutes
Maximum Walk Time Allowed for Egress
15 minutes
30
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Accounting for directional transit service
The transit accessibility analysis was conducted for the PM peak period. However, several examples of
places are served only by AM peak period service towards downtown and PM peak period service away
from downtown. This analysis assumes that directional transit service always conforms to this symmetrical
pattern. The transit analysis followed this pattern and all results are based on work to home transit travel
accessibility.
Proportional Regional Accessibility (D5c)
An additional set of accessibility variables were also calculated to measure accessibility by automobile
(D5a) and transit (D5b) relative to other CBGs within the same metropolitan region. The CBSA for each
block group was used to identify metropolitan areas.69 Proportional regional accessibility for access to jobs
([D5ar] and [D5br]) and working age population ([D5ae] and [D5be]) were determined as a ratio of total
CBSA accessibility. This was performed by summarizing the total access to jobs and working age
population for each CBSA. Then access to employment and working age population in each CBG was
divided by the total CBSA-level accessibility to attain proportional regional accessibility. Proportional
regional accessibility to jobs ([D5cr] and [D5dr]) and working age population ([D5ce] and [D5de])
represent the ratio of CBG-level to CBSA-level access.
Relative Regional Accessibility (D5d)
To further complement the regional accessibility to jobs ([D5ar] and [D5br]) and working age population
([D5ae] and [D5be]), a secondary set of measures were created to compare access in each CBG to the
CBG with the highest access values in the metropolitan region. This relative regional accessibility
measure, also known as a regional centrality index, was calculated by determining the maximum access to
jobs and working age population for each CBSA. Then access to employment and working age population
in each CBG was divided by the maximum CBSA-level accessibility to attain the regional centrality index.
The relative regional accessibility to jobs ([D5cri] and [D5dri]) and working age population ([D5cei] and
[D5dei]) represent the ratio of each CBG to the maximum CBG value within each CBG's region.
National Walkability Index
Following the release of version 2.0 of the SLD, a subsequent set of measures was used to create a National
Walkability Index (NWI) made available in 2015.70 Walkability is characterized by components of the built
environment that influence the likelihood or feasibility of walking as a form of utilitarian transportation.
The NWI was intended to help address a growing demand for data products that enable users to consistently
compare multiple places based on their suitability for walking as a means of travel. This measure was
designed to also be a source input measure for transportation planning, including for scenario planning
applications.
Along with the NWI data release, a user guide was developed to describe the methods and potential
application . To create this measure of walkability, four SLD measures were combined into a composite
index: 1) employment and household entropy [D2A EPHHM], 2) static eight-tier employment entropy
[D2b_E8MIXA], 3) street intersection density (weighted, auto-orientated intersections eliminated) [D3b]
and 4) distance to nearest transit stop [D4a]. These four measures represent different characteristics of the
built environment that are known to be supportive of walking, including a range of diversity in land uses,
street connectivity and access to public transit. In this case, employment and household entropy and the
static eight-tier employment entropy were both used as proxies for land use mix. A ranked score was
calculated for each of the component measures by placing block groups into 4 quantiles (groupings each
having an equal number, in this case, 25%, of CBGs). CBGs were then ranked from 1 (lowest relative
69 Values for this measure were not calculated for rural and small-town areas outside of metropolitan areas
(not part of a CBSA).
70 National Walkability Index U.S. EPA
31
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support for walking) to 20 (highest relative support for walking) based on their value within the quantiles.71
The ranked scores were then weighted using the following formula: 72
Walkability Index score = {w/^} + (*/g) + ("Vg) + {Z/&)
Where w = CBG ranked score for intersection density
x = CBG ranked score for proximity to transit stops
y = CBG ranked score for employment mix
z = CBG ranked score for employment and household mix
Using the formula above, all CBGs are assigned a National Walkability Index value between 1 (lowest
walkability) and 20 (highest walkability). Scores are categorized into the following basic levels of
walkability: 1) least walkable (1.0-5.75), 2) below average walkable (5.76-10.5), 3) above average walkable
(10.51-15.25) and 4) most walkable (15.26-20.0).
71 Due to access to transit either not available (no transit service exists or transit service not producing GTFS
data) or beyond the 0.75 mile (1,207 m) threshold in many CBGs in the country, any CBG given a "-99999" value
was ranked in the first quantile. As a result, ranked CBGs only comprise rank 1 (without nearby transit service)
and 15-20.
72 The elasticities (magnitude of impact) of intersection density, land use mix, and proximity to transit were all
significant and similar in magnitude (Ewing and Cervero 2010). To keep the methodology behind the National
Walkability Index as simple as possible while still incorporating the known impact of the built environment on
walkability, the variables were weighted as follows: 1/3 to each of the three categories of street intersection
density, land use mix, and proximity to transit. The land use mix category was divided into two to account for
the two different techniques of measurement; employment mix and employment and household mix were each
weighted by 1/6.
32
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Appendix A: Regions with transit service data reflected in SLD metrics
Table 8 provides a summary of regions with fixed-guideway transit service used in the development of
SLD transit variables.73 The type of fixed-guideway transit service for each metropolitan area is also
described. Transit stations and stops shown in this table were specifically used in the development of the
D4b variables related to access to fixed-guideway transit service.
Table 8: Summary of metropolitan regions with fixed-guideway transit service incorporated into SLD variables.
Metropolitan Area
State
System Type*
Albany
NY
Intercity Rail
Albuquerque
NM
Commuter Rail
Atlanta
GA
Metro, Streetcar
Austin
TX
Light Rail
Baltimore
MD
Metro, Light Rail, Commuter Rail, Intercity Rail
Boston
MA
Metro, Commuter Rail, Ferry
Buffalo
NY
Light Rail
Charlotte
NC
Light Rail, Streetcar, Bus Rapid Transit
Chicago
IL
Metro, Commuter Rail, Intercity Rail
Cincinnati
OH
Streetcar
Cleveland
OH
Metro, Bus Rapid Transit
Dallas
TX
Light Rail, Commuter Rail, Streetcar
Denver
CO
Light Rail, Commuter Rail
Detroit
MI
Light Rail**
Eugene
OR
Bus Rapid Transit, Intercity Rail
Grand Rapids
MI
Bus Rapid Transit
Harrisburg
PA
Intercity Rail
Hartford
CT
Commuter Rail, Bus Rapid Transit
Houston
TX
Light Rail
Jacksonville
FL
Light Rail**
Kansas City
MO
Bus Rapid Transit
Las Vegas
NV
Bus Rapid Transit, Monorail
Little Rock
AR
Streetcar
Los Angeles
CA
Metro, Light Rail, Commuter Rail, Bus Rapid Transit
Memphis
TN
Streetcar
Miami
FL
Metro, Commuter Rail, Light Rail**
Milwaukee
WI
Streetcar, Intercity Rail
Minneapolis
MN
Light Rail, Commuter Rail
Nashville
TN
Commuter Rail
New Haven
CT
Commuter Rail
New Orleans
LA
Streetcar
New York
NY
Metro, Commuter Rail, Ferry, Aerial Tram
Norfolk-Virginia Beach
VA
Light Rail, Ferry
Oklahoma City
OK
Streetcar
Orlando
FL
Commuter Rail
Philadelphia
PA
Metro, Commuter Rail
Phoenix
AZ
Light Rail
Pittsburgh
PA
Light Rail, Funicular
Portland
OR
Light Rail, Commuter Rail, Intercity Rail, Streetcar, Aerial Tram
Providence
RI
Commuter Rail
Sacramento
CA
Streetcar, Intercity Rail
Salt Lake City
UT
Light Rail, Commuter Rail
San Diego
CA
Light Rail, Commuter Rail, Streetcar, Bus Rapid Transit, Intercity Rail
73 This table may not include regions with only single stops that are part of intercity (commuter rail) or ferry
terminals.
33
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San Francisco
CA
Metro, Light Rail, Commuter Rail, Intercity Rail, Streetcar, Cable Car,
Ferry
San Juan
PR
Metro
Seattle
WA
Light Rail, Commuter Rail, Intercity Rail, Streetcar, Monorail, Ferry
St. Louis
MO
Light Rail
Tampa
FL
Streetcar, Light Rail*
Tucson
AZ
Streetcar
Washington
DC
Metro, Commuter Rail, Streetcar
* Fixed-guideway system types may vary or be classified using different terminology depending on region.
** Denotes tram or "People Mover" systems which may or may not be automated.
34
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Appendix B: Transit Service Data: GTFS Transit Agencies
Table 9 provides a listing, in alphabetical order, of all transit service agencies that had available GTFS data
incorporated into SLD transit service and accessibility metrics. The table provides the agency name, as well as
an alternative name or commonly used abbreviation, service name or stylization to identify service providers
better. Each transit agency's service area is also included, which may consist of a principal city, county or state
where transit service is offered. Lastly, the data release month and year of the GTFS for each agency is
provided for reference.
Table 9: Summary of transit agencies that have GTFS data reflected in SLD measures.
#
Agency Name
Alternative Name
Service Area
GTFS Date
1
10-15 Transit
Ottumwa, IA
Dec, 2019
2
128 Business Council
Waltham, MA
Oct, 2017
3
ABQ RIDE
Albuquerque, NM
Sep, 2020
4
Addison County Transit Resources
Middlebury, VT
Dec, 2018
5
Advance Transit
Wilder, VT
Sep, 2020
6
Airport (MAC)
Minneapolis-St. Paul, MN
Sep, 2020
7
Airport Valet Express
Bakersfield, CA
Jul, 2020
8
Alameda County Transit
AC Transit
Alameda County, CA
Aug, 2019
9
Albany Transit System
Albany, OR
Jul, 2020
10
Allegany County Transit
Cumberland, MD
Aug, 2019
11
Amador Transit
Amador, TX
Jul, 2020
12
Amarillo City Transit
Amarillo, TX
Apr, 2019
13
Anaheim Resort Transportation
Anaheim, CA
Sep, 2020
14
Anchorage People Mover
Anchorage, AK
Jul, 2019
15
Ann Arbor Area Transportation Authority
The RIDE
Ann Arbor, MI
Sep, 2020
16
Annapolis Transit
Annapolis, MD
Aug, 2019
17
Areata & Mad River Transit System
Eureka-Areata, CA
Sep, 2020
18
Arlington Transit
Arlington, VA
Sep, 2020
19
Asheville Rides Transit
ART
Asheville, NC
Sep, 2020
20
Asian Health and Service Center
Portland, OR
Jul, 2020
21
Athens Public Transit
The Bus
Athens, GA
Sep, 2020
22
Atlanta Streetcar
Atlanta, GA
Sep, 2020
23
Avon Transit
Avon, CO
Apr, 2020
24
Baltimore City Department of Transportation
Charm City Circulator
Baltimore, MD
Jan, 2020
25
Basin Transit Service
Klamath Falls, OR
Jun, 2020
26
Bay Area Rapid Transit
BART
San Francisco, CA
Sep, 2020
27
Bay Area Transportation Authority
BATA
Traverse City, MI
Sep, 2020
28
Bay State Cruise Company
Boston, MA
Jul, 2020
30
Bay Town Trolley
Panama City, FL
Jul, 2020
31
Beaumont Transit
Beaumont, TX
Aug, 2020
32
Beaver County Transportation Authority
BCTA
Beaver County, PA
Sep, 2020
33
Bee-Line Bus
Mount Vernon, NY
Sep, 2020
34
Beloit Transit System
Beloit, WI
Jul, 2020
35
Ben Franklin Transit
BFT
Richland, WA
Sep, 2020
36
Benton Area Transit
Corvallis, OR
Sep, 2020
37
Berkshire Regional Transit Authority
Pittsfield, MA
Aug, 2020
38
Big Blue Bus
Santa Monica, CA
Sep, 2020
39
Birmingham-Jefferson County Transit Authority
BJCTA
Birmingham, AL
Mar, 2020
40
Black Ball Ferry Line
Port Angeles, WA
Jul, 2020
41
Blacksburg Transit
Blacksburg, VA
Jan, 2020
42
Block Island Ferry
Narragansett, RI
Sep, 2020
43
Bloomington Transit
Bloomington, IN
Sep, 2020
44
Blue Lake Rancheria Transit System
Humboldt County, CA
Sep, 2020
45
Bluegrass Ultra-Transit Service
Lexington, KY
Dec, 2019
46
Boston Express
Concord, NH
Sep, 2020
47
Boston Harbor Islands National and State Park
Boston, MA
May, 2018
48
Brockton Area Transit Authority
BAT
Brockton, MA
Sep, 2020
49
Broward County Transit
Miami-Ft Lauderdale, FL
Sep, 2020
35
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#
"so~
51
52
53
54
55
56
57
58
60
61
62
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
109
110
111
Agency Name
Alternative Name
Service Area
Bullhead Area Transit System
BATS
Bullhead City, AZ
Burlington Urban Service
Burlington, IA
Butler County Regional Transit Authority
Butler County, PA
Butte-Silver Bow
Butte, MT
BWI Thurgood Marshall Airport
Baltimore, MD
Calaveras Transit
Calaveras County, CA
Caltrain
San Francisco, CA
Canby Area Transit
CAT
Canby, OR
Cape Ann Transportation Authority
Gloucester, MA
Cape Cod Regional Transit Authority
CCRTA
Hyannis, MA
Cape Fear Public Transportation Authority
Wave Transit
Wilmington, NC
Capital Area Transit
Raleigh-Durham, NC
Capital Area Transportation Authority
CATA
Lansing, MI
Capital District Transportation Authority
CDTA
Albany, NY
Capital Metro
Austin, TX
Capital Transit
Juneau, AK
Capitol Corridor Joint Powers Authority
Sacramento, CA
Caravan Airport Transportation
Portland, OR
Cascades East Transit
Bend, OR
Cascades POINT
Eugene, OR
Casco Bay Lines
Portland, ME
Cecil Transit
Cecil County, MD
Cedar Rapids Transit
Cedar Rapids, IA
Central Arkansas Transit Authority
Little Rock, AK
Central Florida Regional Transit Authority
Lynx
Orlando, FL
Central Midlands Regional Transit Authority
The COMET
Columbia, SC
Central New York Regional Transportation Authority
Centro
Syracuse, NY
Central Ohio Transit Authority
Columbus, OH
Central Oregon Breeze
Bend, OR
Central Pennsylvania Transportation Authority
Rabbittransit
York, PA
Ceres Area Transit
CAT
Modesto, CA
Champaign Urbana Mass Transit District
Champaign-Urbana, IL
Chapel Hill Transit
Chapel Hill, NC
Charleston Area Regional Transportation Authority
CARTA
Charleston, SC
Charlotte Area Transit System
CATS
Charlotte, NC
Charlottesville Area Transit
Charlottesville, VA
Chatham Area Transit
CAT
Savannah, GA
Chattanooga Area Regional Transportation Authority
CARTA
Chattanooga, TN
Cherriots
Salem-Keizer, OR
Chicago Transit Authority
Chicago, IL
Cities Area Transit
CAT
Grand Forks, ND
Citilink
Ft Wayne, IN
Citrus County Transit
Orange Line Bus
Lecanto, FL
City of Bandon Trolley
Bandon, OR
City of Fairfax City-University-Energysaver
Fairfax CUE
Fairfax, VA
City of Milton-Freewater
Milton-Freewater, OR
City of Palo Alto Shuttle
Palo Alto, CA
City of San Luis Obispo Transit
SLO Transit
San Luis Obispo, CA
City of Seattle
Seattle, WA
City of Seattle
Seattle, WA
City of South Portland Transit
South Portland Transit
South Portland, ME
City2City Shuttle
Portland, OR
CityLink
Coeur d'Alene, ID
Clackamas Community College
CCC Xpress
Clackamas County, OR
Clackamas County Consortium
Clackamas County, OR
Clallam Transit System
Clallam County, WA
Clark County Public Transit Benefit Area Authority
C-TRAN
Vancouver, WA
Clemson Area Transit
Clemson, SC
Clinton Municipal Transit Administration
Clinton MTA
Clinton, IA
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113
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150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
167
168
169
170
171
Agency Name
Alternative Name
Service Area
Clovis Transit
Clovis, CA
Coach Company - Massachusetts
Merrimac, MA
Coach Company - New York
New York, NY
Coach USA
New York, NY
CobbLinc
Cobb County, GA
Colorado Department of Transportation
Bustang
Denver, CO
Columbia Area Transit
Hood River, OR
Columbia County Rider
CC Rider
Columbia County, OR
Community Transit
Everett, WA
Community Transit
Everett, WA
Concord Kannapolis Area Transit
Rider
Concord, NC
Connect Transit
Normal, IL
Connecticut Transit - Hartford
CTTransit
Hartford, CT
Connecticut Transit - New Britain
CTTransit
New Britain, CT
Connecticut Transit - New Haven
CTTransit
New Haven, CT
Connecticut Transit - Stamford
CTTransit
Stamford, CT
Connecticut Transit - Waterbury-Meriden
CTTransit
Waterbury, CT
Coos County Area Transit
Coos Bay, OR
Coralville Transit
Coralville, IA
Corona Cruiser
Corona, CA
Corpus Christi Regional Transportation Authority
Corpus Christi, TX
Corvallis Area Transit
Corvallis, OR
Cottonwood Area Transit
Cottonwood, AZ
County Connection
San Francisco, CA
Culver CityBus
Culver City, CA
Curry Public Transit
Coos Bay, OR
Cuttyhunk Ferry Co.
New Bedford, MA
Cuyahoga Valley Scenic Railroad
Peninsula, OH
Dallas Area Rapid Transit
DART
Dallas-Ft Worth, TX
Dart First State
Wilmington, DE
DASH
Alexandria, VA
DATTCO
Fairhaven, MA
DC Circulator
Washington, DC
DC Streetcar
Washington, DC
Denton County Transportation Authority
DCTA
Denton County, TX
Des Moines Area Regional Transit Authority
DART
Des Moines, IA
Detroit Department of Transportation
Detroit, MI
Detroit People Mover
Detroit, MI
Diamond Express
Eugene, OR
Dodger Area Rapid Transit
DART
Fort Dodge, IA
Duarte Transit
Duarte, CA
Duke Transit
Durham, NC
Duluth Transit Authority
Duluth, MN
Eastern POINT
Bend, OR
Eastern Sierra Transit Authority
Inyo County, CA
El Dorado Transit
El Dorado County, CA
El Monte Transit
El Monte, CA
EMBARK
EMBARK
Oklahoma City, OK
Emery Go-Round
Emeryville, CA
Erie Metropolitan Transit Authority
EMTA
Erie County, PA
Escambia County Area Transit
ECAT
Pensacola, FL
Estuary Transit District
9 Town Transit
Middlesex County, CT
eTrans
Escalon, CA
Eureka Transit Service
Eureka-Areata, CA
Everett Transit
Everett, WA
Express Arrow
Greeley, CO
Fairfax Connector
Fairfax, VA
Fairfield & Suisun Transit
FAST
Fairfield, CA
Fargo Moorhead Area Transit
MATBUS
Fargo, ND
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210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
Agency Name
Alternative Name
Service Area
Florida Department of Transportation
Florida
Foothill Transit
West Covina, CA
Franklin Regional Transit Authority
Greenfield, MA
Franklin Transit
Nashville, TN
Frederick County Transit
TransIT
Frederick County, MD
Freedom Cruise Line
Harwich Port, MA
Fresno County Rural Transit Agency
FCRTA
Fresno, CA
Fresno Public Transportation
FAX
Fresno, CA
Gainesville Regional Transit System
RTS
Gainesville, FL
Georgia Regional Transportation Authority
GRTA
Atlanta, GA
Glendale Beeline
Glendale, CA
GO Transit (City of Oshkosh)
Oshkosk, WI
GoCary
Cary, NC
Go Durham
Durham, NC
Gold Coast Transit
Oxnard, CA
Golden Empire Transit District
Bakersfield, CA
Golden Gate Transit
GGT
San Rafael, CA
GoRaleigh
Raleigh, NC
GoTriangle
Raleigh-Durham, NC
Grays Harbor Transit
GH Transit
Hoquiam, WA
Greater Bridgeport Transit
GBT
Bridgeport, CT
Greater Cleveland Regional Transit Authority
Cleveland, OH
Greater Dayton Regional Transit Authority
Greater Dayton RTA
Dayton, OH
Greater Lafayette Public Transportation Corporation
CityBus
Lafayette, IN
Greater Lynchburg Transit Co.
Lynchburg, VA
Greater Portland Transit District
Greater Portland Metro
Portland, ME
Greater Richmond Transit Company
GRTC
Richmond, VA
Green Mountain Community Network, Inc.
GMCN
Bennington, VT
Green Mountain Transit
GMT
Burlington, VT
Greenlink Transit
Greenville, SC
Greenlink Trolley
Greenville, SC
Groome Transportation
Eugene, OR
Gunnison Valley RTA
Gunnison, CO
Gwinnett County Transit
GCT
Lawrenceville, GA
H & L Bloom, Inc.
Bloom Bus
Taunton, MA
Hampton Roads Transit
HRT
Hampton, VA
Harford Transit LINK
Harford County, MD
Harrisonburg Department of Public Transportation
Harrisonburg Transit
Harrisonburg, VA
Hartford Line
Hartford, CT
Hillsborough Area Regional Transit
Tampa, FL
Humboldt Transit Authority
RTS
Humboldt County, CA
Huntsville Shuttle Bus
Huntsville, AL
Hy-Line Cruises
Hyannis, MA
Impact Northwest
Impact NW
Portland, OR
IndyGo
Indianapolis, IN
Intercity Transit
Olympia, WA
Intercity Transit
Olympia, WA
Inter-Island Ferry Authority
IFA
Hollis, AK
Island Transit
Coupeville, WA
Jackson Transit System
JATRAN
Jackson, MS
Jacksonville Transportation Authority
JTA
Jacksonville, FL
Jamestown S'klallam
Sequim, WA
Janesville Transit System
Janesville, WI
JeffCo Express
Arnold, MO
Jefferson Transit Authority
Port Townsend, WA
JFK Airtrain
New York, NY
Johnson County Transit
The JO
Johnson County, KS
Josephine Community Transit
Josephine County, OR
Kansas City Area Transportation Authority
KCATA
Kansas City, MO
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232
233
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238
239
240
241
242
243
244
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251
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254
255
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257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
294
295
296
297
298
299
Agency Name
Alternative Name
Service Area
Kayak Public Transit
Pendleton, OR
Kern Transit
Bakersfield, CA
Key West Transit
Stock Island, FL
King County Marine Division
Seattle, WA
King County Metro Transit
Seattle, WA
Kings Area Rural Transit
KART
Hanford, CA
Kingsport Area Transit System
KATS
Kingsport, TN
Kitsap Transit
Bremerton, WA
Klamath Tribes
Chiloquin, OR
Knoxville Area Transit
KAT
Knoxville, TN
La Crosse Municipal Transit Utility
La Crosse MTU
La Crosse, WI
LA Go Bus
Los Angeles County, CA
Los Angeles Department of Transportation
LADOT
Los Angeles, CA
Laguna Beach Transit
Laguna Beach, CA
Lake Champlain Ferries
Burlington, VT
Lake Transit
Lower Lake, CA
Lakeland Transit
Lakeland, FL
Lakes Region Explorer
Bridgton, ME
Laketran
Lake County, OH
Lane Transit District
Eugene, OR
Lassen Rural Bus
Lassen County, CA
Lawndale Beat
Lawndale, CA
Lee County Transit
LeeTran
Lee County, FL
Lehigh and Northampton Transportation Authority
LANTA
Allentown, PA
Let'er Bus
Pendleton, OR
Lexpress
Lexington, MA
Lextran
Lexington, KY
Lincoln County Transit
Lincoln County, OR
Lincoln StarTran
Lincoln, NE
Link Lane
Eugene, OR
Link Transit
Chelan, WA
Linn Shuttle
Albany, OR
Linn-Benton Loop
Albany, OR
LINX Transit
Albany, OR
Livermore Amador Valley Transit Authority
LAVTA
Livermore, CA
Logan Express
Boston, MA
Long Island Bus
New York, NY
Long Island Rail Road
New York, NY
Los Angeles County Metropolitan Transportation
Metro
Los Angeles, CA
Los Angeles County Metropolitan Transportation
METRO
Los Angeles, CA
Lowell Regional Transit Authority
Lowell, MA
Madera Area Express
MAX
Madera, CA
Madera County Connection
Madera County, CA
Madison Metro Transit
Madison, WI
Makah Public Transit
Neah Bay, WA
Malheur Council on Aging & Community Services
Ontario, OR
Manatee County Area Transit
MCAT
Manatee County, FL
Maple Grove
Minneapolis-St. Paul, MN
Marble Valley Regional Transit District
The Bus
Rutland, VT
Marin Transit
San Rafael, CA
Marshalltown Municipal Transit
Marshalltown, IA
Martha's Vineyard Transit Authority
VTA
Martha's Vineyard, MA
Maryland Transit Administration
MTA
Baltimore, MD
Mason City Public Transit
Mason City, IA
Mason Transit Authority
Mason County, WA
Mass Transportation Authority Flint
MTA
Flint, MI
Massachusetts Bay Transportation Authority
MBTA
Boston, MA
Massport
Boston, MA
Memphis Area Transit Authoritiy
MATA
Memphis, TN
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~300
301
302
303
305
307
308
310
311
312
313
314
315
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
346
347
348
349
350
351
352
353
354
355
356
357
358
361
362
363
364
365
366
367
368
369
370
Agency Name
Alternative Name
Service Area
Mendocino Transit Authority
Mendocino, CA
Merced County Transit
The Bus
Merced, CA
Merrimack Valley Regional Transit Authority
Boston, MA
Met Council
Minneapolis-St. Paul, MN
Metro St. Louis
St. Louis, MO
Metro Transit
Minneapolis-St. Paul, MN
Metro Transit
Root
Omaha, NE
Metrolink
Los Angeles, CA
Metro-North Railroad
New York, NY
Metropolitan Atlanta Rapid Transit Authority
MARTA
Atlanta, GA
Metropolitan Family Service
Portland, OR
Metropolitan Transit Authority of Harris County
METRO
Houston, TX
Metropolitan Tulsa Transit Authority
Tulsa, OK
MetroWest Regional Transit Authority
MWRTA
Framingham, MA
Miami-Dade Transit
Miami-Ft Lauderdale, FL
Michigan Flyer
East Lansing, MI
Middlesex 3 TMA
Middlesex County, MA
Milwaukee County Transit System
Milwaukee, WI
Minnesota Valley Transit Authority
MVTA
Burnsville, MN
Mission Bay Transportation Management Association
Mission Bay TMA
San Francisco, CA
MNR Hudson Rail Link
New York, NY
Modesto Area Express
MAX
Modesto, CA
Monroe County Transit Authority
MCTA
Monroe County, PA
Montachusett Regional Transit Authority
Fitchburg, MA
Monterey Park Spirit Bus
Monterey Park, CA
Monterey-Salinas Transit
MST
Monterey, CA
Montgomery County MD Ride On
Washington, DC
Montgomery Transit
Montgomery, AL
Morro Bay Transit
Morro Bay, CA
Mountain Area Regional Transit Authority
Mountain Transit
Big Bear, CA
Mountain Line
Flagstaff, AZ
Mountain Line
Missoula, MT
Mountain Metropolitan Transit
Colorado Springs, CO
Mountain Rides Transportation Authority
MRTA
Blaine County, ID
Mt. Hood Express
Clackamas County, OR
MTA Bus Company
New York, NY
MTA New York City Transit
MTA
New York, NY
MuscaBus
Muscatine, IA
MVgo Mountain View
Mountain View, CA
Mystic
Minneapolis-St. Paul, MN
Nantucket Regional Transit Authority
The WAVE
Nantucket, MA
Nassau Inter-County Express
NICE
Nassau County, NY
Navajo Transit System
Ft. Defiance, AZ
Neighborhood House
Portland, OR
Nevada County Gold Country Stage
Nevada County
New Orleans Regional Transportation Authority
NORTA
New Orleans, LA
New York City Department of Transportation
New York, NY
Niagara Frontier Transportation Authority
NFTA
Buffalo, NY
NJ Transit
New Jersey (Statewide)
North Carolina State Univeristy Wolfline
NCSU Wolfline
Raleigh-Durham, NC
North County Transit District
Oceanside, CA
North Lake Tahoe Express
Reno, NV
Northeast Oregon Public Transit
Baker City, OR
Northern Indiana Commuter Transportation District
NICTD
Chesterton, IN
NorthWest POINT
Astoria, OR
Norwalk Transit System
Norwalk, CT
NY Waterway
New York, NY
NYC Ferry
New York, NY
Ocean City Transportation
Ocean City, MD
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372
373
374
375
376
377
378
379
380
381
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
"416"
717
TU
TT?
420
421
422
423
424
425
426
427
Agency Name
Alternative Name
Service Area
OMNITRAN S
San Bernardino, CA
Orange County Transportation Authority
OCTA
Orange County, CA
Other
Minneapolis-St. Paul, MN
Pacific Crest Lines
Bend, OR
Pacific Transit
Pacific County, WA
Palm Tran
Palm Beach County, FL
Palo Verde Valley Transit Agency
PVVTA
Blythe, CA
Palos Verdes Peninsula Transit Authority
Rolling Hills, California
Pasco County Public Transportation
PCPT
Port Richey, FL
Patriot Party Boats
Falmouth, MA
People for People
Yakima, WA
People Mover
John Day, OR
Petaluma Transit
Petaluma, CA
Peter Pan Bonanza Division
Springfield, MA
Peter Pan Bus Lines
Springfield, MA
Piedmont Authority for Regional Transportation
PART
Greensboro, NC
Pierce Transit
Tacoma, WA
Pierce Transit
Tacoma, WA
Pinellas Suncoast Transit Authority
PSTA
Tampa, FL
Pioneer Valley Transit Authority
PVTA
Springfield, MA
Placer County Transit
Auburn, CA
Plumas Transit
Plumas Transit, CA
Plymouth
Minneapolis-St. Paul, MN
Plymouth & Brockton Street Railway Co.
P&B
Plymouth, MA
Port Authority of Allegheny County
Pittsburgh, PA
Port Authority of New York and New Jersey
New York, NY
Port Authority Trans-Hudson Corporation
New York, NY
Port Authority Transit Corporation
PATCO Speedline
Philadelphia, PA
Port of Portland
Portland, OR
Portland Streetcar
Portland, OR
Potomac and Rappahannock Transportation
OMNIRIDE
Prince William County, VA
Public Oregon Intercity Transit (Klamath Shuttle)
Oregon POINT
Klamath Falls, OR
Puerto Rico Metropolitan Bus Authority
ATI
San Juan, PR
Pulaski Area Transit
Pulaski, VA
Racine Transit
Ryde
Racine, WI
Radar Transit
Roanoke, VA
Radford Transit
Radford, VA
Razorback Transit
Fayetteville, AR
Red Apple Transit
Farmington, NM
Redding Area Bus Authority
Redding, PA
Redwood Coast Transit
RCT
Eureka-Areata, CA
Regional Transportation Authority
PACE
Chicago, IL
Regional Transportation Authority Metra
RTA METRA
Chicago, IL
Regional Transportation Authority of Central Maryland
RTA Maryland
Howard County, MD
Regional Transportation Authority of Middle Tennessee
RTA
Murfreesboro, TN
Regional Transportation Commission of Southern
Nevada
RTC
Las Vegas, NV
Regional Transportation Commission of Washoe
County
RTC RIDE
Reno, NV
Regional Transportation District
Denver, CO
Rhode Island Public Transit Authority
RIPTA
Providence, RI
Rhody Express
Eugene, OR
Ride Connection
Portland, OR
Rio Metro Regional Transit District
Rio Metro Rail Runner
Albuquerque, NM
Rio Vista Delta Breeze
San Francisco, CA
RiverCities Transit
Cowlitz County, WA
Riverside Transit Agency
RTA
Riverside, CA
Roaring Fork Transportation Authority
RFTA
Aspen, CO
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#
~428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
459
460
462
464
465
466
467
468
469
470
471
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
Agency Name
Alternative Name
Service Area
Rochester City Lines
Rochester, MN
Rochester-Genesee Regional Transportation Authority
RGRTS
Rochester, NY
Rockland County Department of Transportation
TOR
Rockland County, NY
Rocky Mountain National Park
Estes Park, CO
Rogue Valley Commuter Line
Medford, OR
Rogue Valley Transportation District
RVTD
Jackson County, OR
Roseville Transit
Roseville, CA
Rural Community Transportation
St. Johnsbury, VT
Sacramento Regional Transit
Sacramento, CA
Sage Stage
Modoc County, CA
Salina City Go
CityGo
Salina, KS
San Benito County Express
San Benito County, CA
San Diego International Airport
San Diego, CA
San Diego Metropolitan Transit System
MTS
San Diego, CA
San Francisco Bay Ferry
San Francisco, CA
San Francisco Municipal Transportation Agency
SFMTA
San Francisco, CA
San Joaquin Regional Transit District
RTD
Stockton, CA
San Luis Obispo Regional Transportation Authority
RTA
San Luis Obispo, CA
Sandusky Transit System
Sandusky, OH
Sandy Area Metro
SAM
Sandy, OR
Sangamon Mass Transit Authority
SMTA
Springfield, IL
Santa Clara Valley Transportation Authority
VTA
San Jose
Santa Cruz Metro
Santa Cruz, CA
Santa Fe Trails
Santa Fe, NM
Santa Maria Area Transit
SMAT
Santa Maria, CA
Santa Ynez Valley Transit
SYVT
Solvang, CA
Sarasota County Area Transit
Sarasota, FL
Seastreak
New York, NY
Seattle Center Monorail
Seattle, WA
Seattle Children's Hospital
Seattle, WA
Selah Transit
Selah, WA
SEPTA
Philadelphia, PA
Shore Line East
SLE
New London, CT
Simi Valley Transit
Simi Valley, CA
Sioux Area Metro
SAM
Sioux Falls, SD
Sioux City Transit System
Sioux City, SD
Siskiyou Transit and General Express
Yreka, CA
Skamania County Public Transit
Gorge WET Bus
Skamania County, WA
Snowmass Village
Pitkin County, CO
Solano County Transit
SolTrans
Solano County, CA
Sonoma County Transit
Sonoma County, CA
Sound Transit
Seattle, WA
South Clackamas Transportation District
SCTD
Molalla, OR
South Florida Regional Transportation Authority
Tri-Rail
Miami, FL
South Metro Area Regional Transit
Wilsonville, OR
Southeast Area Transit District
SEAT
Preston, CT
Southeast Vermont Transit
MOOver
Wilmington, VT
Southeastern Regional Transit Authority
SRTA
New Bedford, MA
Southwest Ohio Regional Transit Authority
Cincinnati, OH
SouthWest POINT
Medford, OR
SouthWest Transit
Minneapolis-St. Paul, MN
Space Coast Area Transit
Melbourne-Palm Bay, FL
Special Services Transportation Agency
Colchester, VT
Spokane Transit Authority
Spokane, WA
Squaxin Island Transit
Shelton, WA
Stagecoach Transportation Services
Randolph, VT
Stanford Marguerite Shuttle
Stanford, CA
Stanislaus Regional Transit
StaRT
Modesto, CA
STAR Transit
Terrell, TX
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#
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
Agency Name
Alternative Name
Service Area
StarMetro
Tallahassee, FL
Streamline
Bozeman, MT
Sun Metro Mass Transit Department
Sun Metro
El Paso, TX
Sunline Transit Agency
SunLine
Palm Springs-Indio, CA
Sunset Empire Transportation District
Astoria, OR
Sunshine Bus Company
St. Augustine, FL
SunTran
St. George, UT
SunTran (City of Ocala)
Ocala, FL
Susanville Indian Rancheria Public Transportation
Susanville, CA
Swan Island Evening Shuttle
Portland, OR
Tahoe Transportation District
Reno, NV
Tahoe Truckee Area Regional Transit
Reno, NV
Tar River Transit
TRT
Rocky Mountain, NC
Tehama Rural Area Express
TRAX
Tehama County, CA
Terre Haute Transit
Terre Haute, IN
The Current
Rockingham, VT
The Greater Attleboro Taunton Regional Transit
GATRA
Taunton, MA
The Hernando Express
Brooksville, FL
The LINK
Wenatchee, WA
The Rapid
The Rapid
Grand Rapids, MI
The Ride
The Ride
Boston, MA
The Victoria Clipper
Seattle, WA
TheBus
Honolulu, HI
Thousand Oaks Transit
Thousand Oaks, CA
Tideline Water Taxi
Tiburon, CA
Tillamook County Transportation District
The Wave
Tillamook, OR
Toledo Area Regional Transit Authority
TARTA
Toledo, OH
Topeka Metro
Topeka, KS
Torrance Transit System
TTS
Torrance, CA
Transfort
Fort Collins, CO
Transit Authority of Northern Kentucky
TANK
Fort Wright, KY
Transit Authority of River City
Louisville, KY
Transportation Reaching People
TPR
Clackamas County, OR
TriMet
TriMet
Portland, OR
Trinity Metro
FWTA
Fort Worth, TX
Trinity Transit
Trinity County, CA
UDASH - University of Montana
Missoula, MT
Union Gap Transit
Union Gap, WA
Unitrans
Davis, CA
University of AZ - Cat Tran - Free Shuttle Service
Tucson, AZ
University of Colorado Boulder
Boulder, CO
University of Iowa
CAMBUS
Iowa City, IA
University of Maryland College Park Transit Services
Shuttle UM
College Park, MD
University of Michigan Transportation Services
Ann Arbor, MI
University of Minnesota
Minneapolis-St. Paul, MN
Urban League
Portland, OR
Utah Transit Authority
Salt Lake City, UT
U-Trans
Roseburg, OR
Vacaville City Coach
Vacaville, CA
Vail Transit
Vail, CO
Valley Metro
Phoenix, AZ
Valley Metro
Phoenix, AZ
Valley Regional Transit
VRT
Meridian, ID
Valley Retriever (Deprecated)
Newport, OR
Ventura County Transportation Commission
VCTC
Ventura County, CA
Verde Lynx
Cottonwood, AZ
VIA Metropolitan Transit
VIA
San Antonio, TX
Via Mobility Services
Boulder, CO
Victor Valley Transit Authority
VVTA
Hesperia, CA
-------
#
HI
552
553
554
556
557
558
559
560
562
563
564
565
566
567
568
569
570
571
572
573
Agency Name
Alternative Name
Service Area
Vineyard Fast Ferry
North Kingstown, RI
Virginia Railway Express
VRE
Manassas, CA
Volusia County Public Transit System
Votran
Volusia County, FL
Washington Metropolitan Area Transit Authority
WMATA
Washington, DC
Washington Park Shuttle
Portland, OR
Washington State Ferries
Seattle, WA
Washington State Ferries
Seattle, WA
Waukesha County Transit
Waukesha County, WI
WeGo Public Transit
Nashville, TN
Western Contra Costa
WestCat
Contra Costa County, CA
Wichita Transit
Wichita, KS
Winter Park Transit
The Lift
Winter Park, CO
Woodburn Transit
Woodburn, OR
Worcester Regional Transit Authority
WRTA
Worcester, MA
Yamhill County Transit Area
YCTA
Yamhill County, OR
Yankee Line
South Boston, MA
Yolo County Transportation District
Yolobus
Woodland, CA
Yosemite Area Regional Transportation System
Yosemite, CA
Yosemite Valley Shuttle System
Yosemite, CA
Yuba-Sutter Transit
Marysville, CA
Yuma County Intergovernmental Public Transportation
Authority
YCIPTA
Yuma County, CA
-------
Appendix C: Transit Service Data: GTFS Data Coverage by Ridership
Table 10 summarizes total ridership on transit systems with GTFS coverage in the SLD by metropolitan area.
Many metropolitan regions are served by multiple transit agencies operating different types of transit services.
Ridership information from the NTD, maintained by the FHWA, was acquired to compare the relative coverage
of GTFS data by region. Census-designated areas with 50,000 people or more called urbanized regions (UZAs)
were used to summarize the percentage of ridership where GTFS data is reflected in the SLD. This information is
useful to determine the extent of SLD transit accessibility measure coverage for a specific region of interest.
Table 10: GTFS transit data coverage summarized by total 2019 ridership by metropolitan area.
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Aberdeen-Bel Air South-Bel Air North, MD f
0
-
-
Abilene, TX
0
-
-
Akron, OH
6,574,247
0 (0%)
6,574,247(100%)
Albany-Schenectady, NY
15,683,929
15,683,929 (100%)
0 (0%)
Albany, GA
773,757
0 (0%)
773,757 (100%)
Albuquerque, NM
10,313,468
10,313,468(100%)
0 (0%)
Alexandria, LA
0
-
-
Allentown, PA-NJ
4,732,570
4,732,570 (100%)
0 (0%)
Altoona, PA
567,624
0 (0%)
567,624 (100%)
Amarillo, TX f
0
-
-
Ames, IA
6,121,023
0 (0%)
6,121,023(100%)
Anchorage, AK
4,173,959
3,750,404 (89.9%)
423,555 (10.1%)
Anderson, IN
0
-
-
Ann Arbor, MI
14,319,276
14,319,276(100%)
0 (0%)
Antioch, CA
1,985,920
0 (0%)
1,985,920(100%)
Appleton, WI
1,112,264
0 (0%)
1,112,264(100%)
Asheville, NC
2,124,106
1,978,720 (93.2%)
145,386 (6.8%)
Athens-Clarke County, GA
7,272,198
1,280,266(17.6%)
5,991,932 (82.4%)
Atlanta, GA
129,107,991
123,809,358(95.9%)
5,298,633 (4.1%)
Atlantic City, NJ
113,081
0 (0%)
113,081 (100%)
Auburn, AL
0
-
-
Augusta-Richmond County, GA-SC
668,888
0 (0%)
668,888 (100%)
Austin, TX
31,078,420
31,078,420(100%)
0 (0%)
Bakersfield, CA
6,252,450
6,252,450 (100%)
0 (0%)
Baltimore, MD
96,816,359
96,521,182(99.7%)
295,177 (0.3%)
Bangor, ME
0
-
-
Barnstable Town, MA
5,041,758
1,179,775 (23.4%)
3,861,983 (76.6%)
Baton Rouge, LA
3,803,859
0 (0%)
3,803,859(100%)
Battle Creek, MI
0
-
-
Bay City, MI
509,917
0 (0%)
509,917(100%)
Beaumont, TX
416,352
416,352 (100%)
0 (0%)
Bellingham, WA
4,703,865
0 (0%)
4,703,865 (100%)
Beloit, WI-IL f
0
-
-
Bend, OR
745,968
745,968 (100%)
0 (0%)
Benton Harbor-St. Joseph-Fair Plain, MI
0
-
-
Billings, MT
470,975
0 (0%)
470,975 (100%)
Binghamton, NY-PA
1,866,060
0 (0%)
1,866,060(100%)
Birmingham, AL
3,331,511
3,331,511 (100%)
0 (0%)
Bismarck, ND
0
-
-
Blacksburg, VA
4,659,053
4,659,053 (100%)
0 (0%)
Bloomington-Normal, IL
2,533,469
2,533,469 (100%)
0 (0%)
Bloomington, IN
3,197,637
3,197,637(100%)
0 (0%)
Boise City, ID
1,496,068
1,321,605 (88.3%)
174,463 (11.7%)
Bonita Springs, FL
913,727
0 (0%)
913,727(100%)
Boston, MA-NH-RI
374,372,186
374,372,186(100%)
0 (0%)
Bowling Green, KY
0
-
-
Bremerton, WA
3,850,213
3,850,213 (100%)
0 (0%)
0
-------
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Bridgeport-Stamford, CT-NY
9,970,158
9,523,780 (95.5%)
446,378 (4.5%)
Brownsville, TX
1,553,994
0 (0%)
1,553,994(100%)
Buffalo, NY
23,982,380
23,982,380 (100%)
0 (0%)
Burlington, VT
2,843,044
2,843,044 (100%)
0 (0%)
Canton, OH
2,330,539
0 (0%)
2,330,539(100%)
Cape Coral, FL
3,180,902
3,180,902 (100%)
0 (0%)
Carbondale, IL
1,212,976
0 (0%)
1,212,976(100%)
Casper, WY
0
-
-
Cedar Rapids, IA
1,333,692
1,333,692(100%)
0 (0%)
Champaign, IL
11,620,837
11,620,837(100%)
0 (0%)
Charleston-North Charleston, SC
3,200,749
3,200,749 (100%)
0 (0%)
Charleston, WV
1,632,201
0 (0%)
1,632,201 (100%)
Charlotte, NC-SC
24,689,517
24,278,653 (98.3%)
410,864(1.7%)
Charlottesville, VA
316,547
0 (0%)
316,547(100%)
Chattanooga, TN-GA
2,643,299
2,643,299 (100%)
0 (0%)
Cheyenne, WY
0
-
-
Chicago, IL-IN
554,752,682
553,013,278(99.7%)
1,739,404 (0.3%)
Chico, CA
1,985,037
0 (0%)
1,985,037(100%)
Cincinnati, OH-KY-IN
17,747,511
17,626,044 (99.3%)
121,467 (0.7%)
Clarksville, TN-KY
0
-
-
Cleveland, OH
32,985,936
32,879,681 (99.7%)
106,255 (0.3%)
Cleveland, TN
0
-
-
Coeur dAlene, ID
0
-
-
College Station-Bryan, TX
438,979
0 (0%)
438,979 (100%)
Colorado Springs, CO
3,411,436
3,411,436(100%)
0 (0%)
Columbia, MO
1,108,594
0 (0%)
1,108,594(100%)
Columbia, SC
2,733,489
2,733,489 (100%)
0 (0%)
Columbus, GA-AL
0
-
-
Columbus, OH
19,572,009
19,430,144(99.3%)
141,865 (0.7%)
Concord, CA
5,111,416
1,706,551 (33.4%)
3,404,865 (66.6%)
Conroe-The Woodlands, TX
691,409
0 (0%)
691,409 (100%)
Corpus Christi, TX
5,249,776
5,249,776 (100%)
0 (0%)
Corvallis, OR f
0
-
-
Cumberland, MD-WV-PA f
0
-
-
Dallas-Fort Worth-Arlington, TX
76,687,416
75,545,696 (98.5%)
1,141,720(1.5%)
Danbury, CT-NY
682,224
0 (0%)
682,224 (100%)
Danville, IL-IN
0
-
-
Daphne-Fairhope, AL
133,765
0 (0%)
133,765 (100%)
Davenport, IA-IL
3,392,507
0 (0%)
3,392,507(100%)
Davis, CA
3,741,782
3,741,782(100%)
0 (0%)
Dayton, OH
9,586,879
9,416,615 (98.2%)
170,264(1.8%)
Decatur, AL
141,928
0 (0%)
141,928 (100%)
Decatur, IL
1,120,171
0 (0%)
1,120,171 (100%)
DeKalb, IL
509,527
0 (0%)
509,527(100%)
Delano, CA
0
-
-
Denton-Lewisville, TX
2,939,309
2,939,309 (100%)
0 (0%)
Denver-Aurora, CO
105,504,710
105,337,078(99.8%)
167,632 (0.2%)
Des Moines, IA
4,395,323
4,395,323 (100%)
0 (0%)
Detroit, MI
36,593,331
26,683,668 (72.9%)
9,909,663 (27.1%)
Dothan, AL
0
-
-
Dover-Rochester, NH-ME
427,023
0 (0%)
427,023 (100%)
Dubuque, IA-IL
0
-
-
Duluth, MN-WI
2,683,183
2,683,183 (100%)
0 (0%)
Durham, NC
15,290,515
15,290,515 (100%)
0 (0%)
East Stroudsburg, PA-NJ
336,825
0 (0%)
336,825 (100%)
Eau Claire, WI
913,567
0 (0%)
913,567(100%)
El Centro-Calexico, CA
1,412,697
0 (0%)
1,412,697(100%)
El Paso, TX-NM
11,513,869
11,513,869(100%)
0 (0%)
Elizabethtown-Radcliff, KY
195,860
0 (0%)
195,860(100%)
-------
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Elkhart, IN-MI
481,384
0 (0%)
481,384 (100%)
Elmira, NY
0
-
-
Erie, PA
2,638,723
2,638,723 (100%)
0 (0%)
Eugene, OR
10,528,027
10,528,027(100%)
0 (0%)
Evansville, IN-KY
1,273,611
0 (0%)
1,273,611 (100%)
Fairbanks, AK
0
-
-
Fairfield, CA
905,023
905,023 (100%)
0 (0%)
Fargo, ND-MN
1,889,723
1,396,884(73.9%)
492,839(26.1%)
Fayetteville-Springdale-Rogers, AR-MO f
0
-
-
Fayetteville, NC
1,452,842
0 (0%)
1,452,842(100%)
Flagstaff, AZ
2,570,838
2,570,838 (100%)
0 (0%)
Flint, MI
4,784,585
4,784,585 (100%)
0 (0%)
Florence, AL
108,577
0 (0%)
108,577(100%)
Florence, SC
0
-
-
Fond du Lac, WI
0
-
-
Fort Collins, CO
4,685,846
4,503,616(96.1%)
182,230 (3.9%)
Fort Smith, AR-OK
0
-
-
Fort Walton Beach-Navarre-Wright, FL
181,624
0 (0%)
181,624(100%)
Fort Wayne, IN
1,676,800
1,676,800(100%)
0 (0%)
Frederick, MD
593,853
593,853 (100%)
0 (0%)
Fredericksburg, VA
0
-
-
Fresno, CA
10,770,493
10,770,493 (100%)
0 (0%)
Gainesville, FL
9,255,107
9,255,107(100%)
0 (0%)
Gainesville, GA
0
-
-
Galveston, TX
0
-
-
Glens Falls, NY
0
-
-
Grand Forks, ND-MN
290,323
290,323 (100%)
0 (0%)
Grand Junction, CO
0
-
-
Grand Rapids, MI
10,472,095
10,472,095 (100%)
0 (0%)
Great Falls, MT
441,765
0 (0%)
441,765 (100%)
Greeley, CO f
0
-
-
Green Bay, WI
1,324,579
0 (0%)
1,324,579(100%)
Greensboro, NC
4,152,944
686,982 (16.5%)
3,465,962 (83.5%)
Greenville, SC
1,708,186
1,708,186(100%)
0 (0%)
Gulfport, MS
809,534
0 (0%)
809,534 (100%)
Hagerstown, MD-WV-PA
0
-
-
Hanford, CA
4,136,576
702,428 (17%)
3,434,148 (83%)
Harrisburg, PA
2,203,193
2,203,193 (100%)
0 (0%)
Harrisonburg, VA
2,120,458
2,120,458 (100%)
0 (0%)
Hartford, CT
18,778,135
17,583,417(93.6%)
1,194,718(6.4%)
Hickory, NC
244,326
0 (0%)
244,326 (100%)
High Point, NC
0
-
-
Holland, MI
412,143
0 (0%)
412,143 (100%)
Houma, LA
0
-
-
Houston, TX
90,358,931
89,951,217(99.5%)
407,714 (0.5%)
Huntington, WV-KY-OH
952,911
0 (0%)
952,911 (100%)
Huntsville, AL
749,063
749,063 (100%)
0 (0%)
Idaho Falls, ID
0
-
-
Indianapolis, IN
9,701,062
9,641,612 (99.4%)
59,450 (0.6%)
Indio-Cathedral City, CA
4,217,807
4,217,807(100%)
0 (0%)
Iowa City, IA
5,640,630
5,513,111 (97.7%)
127,519(2.3%)
Ithaca, NY
4,291,946
0 (0%)
4,291,946(100%)
Jackson, MI
516,837
0 (0%)
516,837(100%)
Jackson, MS
560,632
560,632 (100%)
0 (0%)
Jackson, TN
446,803
0 (0%)
446,803 (100%)
Jacksonville, FL
11,743,867
11,614,452 (98.9%)
129,415(1.1%)
Janesville, WI f
0
-
-
Jefferson City, MO
0
-
-
Johnson City, TN
162,782
0 (0%)
162,782 (100%)
-------
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Johnstown, PA
1,220,538
0 (0%)
1,220,538(100%)
Jonesboro, AR
0
-
-
Kahului, HI
2,084,376
2,084,376 (100%)
0 (0%)
Kalamazoo, MI
2,766,146
0 (0%)
2,766,146(100%)
Kankakee, IL
671,555
0 (0%)
671,555 (100%)
Kansas City, MO-KS
15,162,331
14,596,578 (96.3%)
565,753 (3.7%)
Kennewick-Pasco, WA
3,126,689
3,126,689 (100%)
0 (0%)
Kenosha, WI-IL
1,404,305
0 (0%)
1,404,305(100%)
Killeen, TX
502,048
0 (0%)
502,048 (100%)
Kingsport, TN-VA f
0
-
-
Knoxville, TN
2,895,316
2,752,602(95.1%)
142,714 (4.9%)
Kokomo, IN
461,187
0 (0%)
461,187(100%)
La Crosse, WI-MN
923,030
923,030 (100%)
0 (0%)
Lafayette, IN
5,099,775
5,099,775 (100%)
0 (0%)
Lafayette, LA
1,358,408
0 (0%)
1,358,408(100%)
Lake Tahoe, CA-NV
338,726
338,726 (100%)
0 (0%)
Lakeland, FL
1,294,771
1,294,771 (100%)
0 (0%)
Lancaster-Palmdale, CA
2,352,468
0 (0%)
2,352,468(100%)
Lancaster, PA
7,259,514
0 (0%)
7,259,514(100%)
Lansing, MI
11,110,771
11,049,330 (99.4%)
61,441 (0.6%)
Laredo, TX
2,562,636
0 (0%)
2,562,636(100%)
Las Cruces, NM
60,713
60,713 (100%)
0 (0%)
Las Vegas-Henderson, NV
70,637,277
65,821,192(93.2%)
4,816,085 (6.8%)
Lawrence, KS
3,396,184
0 (0%)
3,396,184(100%)
Lawton, OK
0
-
-
Lebanon, PA
363,458
0 (0%)
363,458 (100%)
Leesburg-Eustis-Tavares, FL
472,695
0 (0%)
472,695 (100%)
Leominster-Fitchburg, MA
1,120,816
1,120,816(100%)
0 (0%)
Lewiston, ME
233,472
0 (0%)
233,472 (100%)
Lexington-Fayette, KY
4,612,703
4,612,703 (100%)
0 (0%)
Lima, OH
0
-
-
Lincoln, NE
2,441,518
2,441,518(100%)
0 (0%)
Little Rock, AR
2,564,760
0 (0%)
2,564,760(100%)
Lodi, CA
0
-
-
Logan, UT
2,572,181
0 (0%)
2,572,181 (100%)
Lompoc, CA
0
-
-
Longview, WA-OR
390,598
390,598 (100%)
0 (0%)
Lorain-Elyria, OH
0
-
-
Los Angeles-Long Beach-Anaheim, CA
563,859,115
528,296,102(93.7%)
35,563,013 (6.3%)
Louisville/Jefferson County, KY-IN
11,625,802
11,456,984(98.5%)
168,818(1.5%)
Lubbock, TX
3,542,620
0 (0%)
3,542,620(100%)
Lynchburg, VA
2,018,554
2,018,554(100%)
0 (0%)
Macon, GA
0
-
-
Madison, WI
12,969,815
12,969,815 (100%)
0 (0%)
Manchester, NH
0
-
-
Mansfield, OH
0
-
-
McAllen, TX
819,209
0 (0%)
819,209 (100%)
McKinney, TX
0
-
-
Medford, OR
1,232,952
1,232,952(100%)
0 (0%)
Memphis, TN-MS-AR
6,477,372
6,410,327(99%)
67,045 (1%)
Merced, CA
950,730
950,730 (100%)
0 (0%)
Miami, FL
125,231,477
122,415,568(97.8%)
2,815,909(2.2%)
Middletown, OH
0
-
-
Milwaukee, WI
32,021,507
29,998,223 (93.7%)
2,023,284 (6.3%)
Minneapolis-St. Paul, MN-WI
93,779,196
93,211,873 (99.4%)
567,323 (0.6%)
Mission Vieio-Lake Forest-San Clemente, CA
820,829
820,829 (100%)
0 (0%)
Missoula, MT
1,838,334
1,598,692(87%)
239,642 (13%)
Mobile, AL
938,025
0 (0%)
938,025 (100%)
Modesto, CA
2,265,448
2,265,448 (100%)
0 (0%)
-------
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Monessen-California, PA
288,056
0 (0%)
288,056 (100%)
Monroe, LA
0
-
-
Monroe, MI
428,766
0 (0%)
428,766 (100%)
Montgomery, AL
602,397
602,397(100%)
0 (0%)
Morgantown, WV
1,469,292
0 (0%)
1,469,292(100%)
Morristown, TN
0
-
-
Mount Vernon, WA
896,118
0 (0%)
896,118(100%)
Muncie, IN
1,408,230
0 (0%)
1,408,230(100%)
Murfreesboro, TN
0
-
-
Muskegon, MI
0
-
-
Myrtle Beach-Socastee, SC-NC
0
-
-
Napa, CA
1,059,168
0 (0%)
1,059,168(100%)
Nashua, NH-MA
462,549
0 (0%)
462,549 (100%)
Nashville-Davidson, TN
10,378,670
10,255,735 (98.8%)
122,935 (1.2%)
New Bedford, MA
2,749,070
2,749,070 (100%)
0 (0%)
New Haven, CT
7,799,900
7,567,553 (97%)
232,347 (3%)
New Orleans, LA
18,989,830
16,316,609 (85.9%)
2,673,221 (14.1%)
New York-Newark, NY-NJ-CT
4,373,931,006
4,262,515,596 (97.5%)
111,415,410(2.5%)
Newark, OH
113,893
0 (0%)
113,893 (100%)
Norman, OK
0
-
-
North Port-Port Charlotte, FL
130,125
0 (0%)
130,125 (100%)
Norwich-New London, CT-RI
965,658
965,658 (100%)
0 (0%)
Ocala, FL f
0
-
-
Odessa, TX
0
-
-
Oklahoma City, OK
3,122,965
3,122,965 (100%)
0 (0%)
Olympia-Lacey, WA
4,736,809
4,736,809 (100%)
0 (0%)
Omaha, NE-IA
3,368,959
3,368,959 (100%)
0 (0%)
Orlando, FL
26,490,172
26,490,172 (100%)
0 (0%)
Oshkosh, WI
818,919
818,919(100%)
0 (0%)
Owensboro, KY
151,344
0 (0%)
151,344(100%)
Oxnard, CA
4,383,152
4,383,152(100%)
0 (0%)
Palm Bay-Melbourne, FL
2,335,284
0 (0%)
2,335,284(100%)
Palm Coast-Daytona Beach-Port Orange, FL
3,595,864
3,492,725 (97.1%)
103,139(2.9%)
Panama City, FL
508,532
453,127(89.1%)
55,405 (10.9%)
Parkersburg, WV-OH
0
-
-
Pensacola, FL-AL
1,504,625
1,504,625 (100%)
0 (0%)
Peoria, IL
2,750,322
0 (0%)
2,750,322(100%)
Petaluma, CA
349,280
349,280 (100%)
0 (0%)
Philadelphia, PA-NJ-DE-MD
329,212,055
329,050,914 (100%)
161,141 (0%)
Phoenix-Mesa, AZ
84,624,757
72,889,233 (86.1%)
11,735,524(13.9%)
Pittsburgh, PA
67,746,233
64,849,280 (95.7%)
2,896,953 (4.3%)
Pittsfield, MA
524,796
524,796 (100%)
0 (0%)
Pocatello, ID
0
-
-
Port Arthur, TX
0
-
-
Port Huron, MI
1,525,809
0 (0%)
1,525,809(100%)
Port St. Lucie, FL
886,510
0 (0%)
886,510(100%)
Porterville, CA
0
-
-
Portland, ME
3,758,994
2,111,881 (56.2%)
1,647,113 (43.8%)
Portland, OR-WA
110,135,835
110,135,835 (100%)
0 (0%)
Portsmouth, NH-ME
0
-
-
Pottstown, PA
229,253
0 (0%)
229,253 (100%)
Poughkeepsie-Newburgh, NY-NJ
1,558,043
0 (0%)
1,558,043(100%)
Providence, RI-MA
17,508,354
17,465,668 (99.8%)
42,686 (0.2%)
Pueblo, CO
831,954
0 (0%)
831,954(100%)
Racine, WI
1,041,115
1,041,115 (100%)
0 (0%)
Raleigh, NC
9,323,764
9,127,723 (97.9%)
196,041 (2.1%)
Rapid City, SD
0
-
-
Reading, PA
3,139,486
0 (0%)
3,139,486(100%)
Redding, CA
630,122
630,122 (100%)
0 (0%)
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Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Reno, NV-CA
7,863,626
7,863,626 (100%)
0 (0%)
Richmond, VA
9,283,520
9,283,520 (100%)
0 (0%)
Riverside-San Bernardino, CA
19,910,492
19,910,492 (100%)
0 (0%)
Roanoke, VA
1,970,807
1,970,807(100%)
0 (0%)
Rochester, MN
2,155,230
2,155,230 (100%)
0 (0%)
Rochester, NY
14,712,832
14,712,832 (100%)
0 (0%)
Rockford, IL
1,650,532
0 (0%)
1,650,532(100%)
Rome, GA
1,113,342
0 (0%)
1,113,342(100%)
Sacramento, CA
23,524,697
22,364,647(95.1%)
1,160,050(4.9%)
Saginaw, MI
594,217
0 (0%)
594,217(100%)
Salem, OR
3,272,941
3,272,941 (100%)
0 (0%)
Salisbury, MD-DE
325,096
0 (0%)
325,096 (100%)
Salt Lake City-West Valley City, UT
44,578,161
44,578,161 (100%)
0 (0%)
San Angelo, TX
472,045
0 (0%)
472,045 (100%)
San Antonio, TX
42,641,565
42,510,772 (99.7%)
130,793 (0.3%)
San Diego, CA
105,304,898
101,872,110(96.7%)
3,432,788 (3.3%)
San Francisco-Oakland, CA
450,054,427
419,927,513(93.3%)
30,126,914 (6.7%)
San Jose, CA
36,432,963
36,432,963 (100%)
0 (0%)
San Juan, PR
26,188,574
24,561,662 (93.8%)
1,626,912(6.2%)
San Luis Obispo, CA
2,078,087
2,078,087(100%)
0 (0%)
San Marcos, TX
2,942,220
0 (0%)
2,942,220 (100%)
Santa Barbara, CA
6,432,190
0 (0%)
6,432,190(100%)
Santa Clarita, CA
2,681,213
0 (0%)
2,681,213(100%)
Santa Cruz, CA
5,119,469
5,119,469(100%)
0 (0%)
Santa Fe, NM
904,685
904,685 (100%)
0 (0%)
Santa Maria, CA
687,383
687,383 (100%)
0 (0%)
Santa Rosa, CA
3,534,449
1,682,482 (47.6%)
1,851,967 (52.4%)
Sarasota-Bradenton, FL
4,198,441
4,198,441 (100%)
0 (0%)
Savannah, GA
4,069,157
4,069,157(100%)
0 (0%)
Scranton, PA
2,445,478
0 (0%)
2,445,478(100%)
Seaside-Monterey, CA
4,428,381
4,428,381 (100%)
0 (0%)
Seattle, WA
225,876,041
225,876,041 (100%)
0 (0%)
Sebastian-Vero Beach South-Florida Ridge, FL
1,259,578
0 (0%)
1,259,578(100%)
Sheboygan, WI
0
-
-
Sherman, TX
43,852
0 (0%)
43,852 (100%)
Shreveport, LA
2,618,604
0 (0%)
2,618,604(100%)
Simi Valley, CA f
0
-
-
Sioux City, IA-NE-SD
876,826
0 (0%)
876,826 (100%)
Sioux Falls, SD
853,523
853,523 (100%)
0 (0%)
South Bend, IN-MI
1,596,172
0 (0%)
1,596,172(100%)
South Lyon-Howell, MI
0
-
-
Spartanburg, SC
275,135
0 (0%)
275,135 (100%)
Spokane, WA
10,568,157
10,568,157(100%)
0 (0%)
Spring Hill, FL f
0
-
-
Springfield, IL
1,573,175
1,573,175 (100%)
0 (0%)
Springfield, MA-CT
10,380,926
10,380,926 (100%)
0 (0%)
Springfield, MO
1,312,354
0 (0%)
1,312,354(100%)
Springfield, OH
0
-
-
St. Augustine, FL f
0
-
-
St. Cloud, MN
1,680,763
0 (0%)
1,680,763(100%)
St. Joseph, MO-KS
0
-
-
St. Louis, MO-IL
38,809,080
36,642,036 (94.4%)
2,167,044 (5.6%)
State College, PA
6,602,752
0 (0%)
6,602,752(100%)
Stockton, CA
5,376,219
5,376,219(100%)
0 (0%)
Sumter, SC
153,048
0 (0%)
153,048(100%)
Syracuse, NY
11,219,473
11,219,473 (100%)
0 (0%)
Tallahassee, FL
3,643,431
3,643,431 (100%)
0 (0%)
Tampa-St. Petersburg, FL
28,403,299
27,943,719 (98.4%)
459,580(1.6%)
Terre Haute, IN
237,867
237,867(100%)
0 (0%)
-------
Metropolitan Area f
Total
Ridership (FY
2019)J
Ridership on GTFS
Systems (FY 2019)
Ridership on Non-
GTFS Systems
(FY 2019)
Texarkana-Texarkana, TX-AR
0
-
-
Texas City, TX
0
-
-
Thousand Oaks, CA f
0
-
-
Toledo, OH-Mt
2,007,259
2,007,259 (100%)
0 (0%)
Topeka, KS
1,310,702
1,310,702(100%)
0 (0%)
Tucson, AZ
15,844,953
141,958(0.9%)
15,702,995 (99.1%)
Tulsa, OK
2,717,580
2,717,580 (100%)
0 (0%)
Turlock, CA
188,450
0 (0%)
188,450(100%)
Tuscaloosa, AL
0
-
-
Uniontown-Connellsville, PA
251,169
0 (0%)
251,169(100%)
Urban Honolulu, HI
64,427,861
64,065,785 (99.4%)
362,076 (0.6%)
Utica, NY
1,338,743
1,314,656 (98.2%)
24,087(1.8%)
Vacaville, CA f
0
-
-
Vallejo, CA
2,114,933
1,446,163 (68.4%)
668,770 (31.6%)
Victoria, TX
0
-
-
Victorville-Hesperia, CA
2,240,374
2,240,374 (100%)
0 (0%)
Villas, NJ
0
-
-
Vineland, NJ
206,661
0 (0%)
206,661 (100%)
Virgin Islands, VI
0
-
-
Virginia Beach, VA
13,332,764
13,332,764 (100%)
0 (0%)
Visalia, CA
1,615,012
0 (0%)
1,615,012(100%)
Waco, TX
1,287,009
0 (0%)
1,287,009(100%)
Waldorf, MD
806,460
0 (0%)
806,460 (100%)
Washington, DC-VA-MD
410,439,406
405,975,689(98.9%)
4,463,717(1.1%)
Waterbury, CT
4,933,139
4,933,139(100%)
0 (0%)
Waterloo, IA
0
-
-
Wausau, WI
0
-
-
Wenatchee, WA
1,036,007
1,036,007(100%)
0 (0%)
Westminster-Eldersburg, MD
0
-
-
Wheeling, WV-OH
379,457
0 (0%)
379,457(100%)
Wichita, KS
1,366,960
1,366,960(100%)
0 (0%)
Williamsburg, VA
2,119,442
0 (0%)
2,119,442(100%)
Williamsport, PA
1,314,850
0 (0%)
1,314,850(100%)
Wilmington, NC
1,258,731
1,258,731 (100%)
0 (0%)
Winchester, VA
0
-
-
Winston-Salem, NC
2,696,733
0 (0%)
2,696,733 (100%)
Winter Haven, FL
0
-
-
Worcester, MA-CT
3,232,569
3,232,569 (100%)
0 (0%)
Yakima, WA
1,056,918
1,056,918(100%)
0 (0%)
York, PA
2,231,826
2,231,826(100%)
0 (0%)
Young stown, OH-PA
1,568,483
0 (0%)
1,568,483(100%)
Yuba City, CA
931,948
931,948(100%)
0 (0%)
Yuma, AZ-CA
844,374
844,374 (100%)
0 (0%)
Total
9,846,935,772
9,396,541,641 (95.4%)
450,394,131 (4.6%)
f GTFS data available, but no ridership available. Assumed 100% on GTFS systems.
J Did not report ridership information to National Transit Database in 2019.
Source: National Transit Database (NTD), Federal Highway Administration (FHWA), 2020.
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