Journal of Physical Activity and Health, 2012, 9, 689-697
© 2012 Human Kinetics, Inc.
Google Walkability: A New Tool for Local Planning
and Public Health Research?
Jason Vargo, Brian Stone, and Karen Glanz
Background: We investigate the association of different composite walkability measures with individual walking behaviors to determine if multicomponent metrics of walkability are more useful for assessing the health
impacts of the built environment than single component measures. Methods: We use a previously published
composite walkability measure as well as a new measure that was designed to represent easier methods of
combination and which includes 2 metrics obtained using Google data sources. Logistic regression was used
to assess the relationship between walking behavior and walkability metrics. Results: Our results suggest that
composite measures of walkability are more consistent predictors of walking behavior than single component
measures. Furthermore, a walkability measure developed using free, publicly available data from Google was
found to be nearly as effective in predicting walking outcomes as a walkability measure derived without such
publicly and nationally available measures. Conclusions: Our findings demonstrate the effectiveness of free
and locally relevant data for assessing walkable environments. This facilitates the use of locally derived and
adaptive tools for evaluating the health impacts of the built environment.
Keywords: active transportation, built environment, physical activity methodology
Interest in the connections between land use and
health has introduced new methods and tools for describing health-related aspects of the built environment.1
These methods often require a significant investment
of time for training and data collection while yielding
uncertain returns in terms of accuracy and strength of
findings. For example, one instrument for assessing the
importance of parks to physical activity incorporates
more than 600 attributes.2 A second widely used metric
for assessing built form and physical activity is a sprawl
index developed by Ewing, et al, which requires for its
computation 22 separate measures combined through
principal components analyses.3 If built environment and
physical activity frameworks are to be useful beyond the
limited geographic and temporal extents for which they
are often developed, these tools will need to be more
readily accessible to a wide range of public health and
planning researchers and practitioners. Efficient use of
these tools requires that they be less methodologically
complex and resource-intensive. Instead, if possible,
they should be based on free and easily accessible
data sources.
This research assesses the relative utility of alternative metrics for measuring the built environment. We
seek to determine whether the integration of 2 or more
Vargo and Stone are with the School of City and Regional
Planning, Georgia Institute of Technology, Atlanta, GA. Glanz
is with the Schools of Medicine and Nursing, University of
Pennsylvania, Philadelphia, PA.
built environment indicators into multicomponent or
composite measures of neighborhood “walkability”
yield greater predictive power for walking outcomes
than single component metrics. Specifically, we test the
predictive power of both composite and single component
variables in explaining walking behavior in different
neighborhoods of the Atlanta, Georgia metropolitan area.
In addition, we evaluate the predictive power of built
environment measures developed from open access data
to assess the potential for such increasingly available
datasets to be employed in planning and public health
research and practice.
Background
Walkability describes those qualities of the built environment that encourage walking behaviors. Composite
measures vary by the components they include (eg,
density, land use, connectivity), the scale at which they
are measured (eg, 1/4 mile, 1/2 mile, or 1 mile from
locations), and the methods used in computation (eg,
combining component metrics via principal components analysis). While studies employing composite
measures of the built environment have demonstrated
associations with physical activity, assessments of the
relative utility of such measures in predicting outcomes
have not been addressed in a comprehensive fashion.
This paper examines walkability measures using 2 main
categories of component variables: those related to
proximity and those related to connectivity of different
land uses.4
689
690 Vargo, Stone, and Glanz
Proximity describes the number and variety of destinations within a specified distance of any location. Population, employment, and household densities measure the
compactness of land uses, while land use mix measures
the heterogeneity of different uses in space. Measures
of connectivity are related to the physical design and the
layout of transportation infrastructure. These measures
quantify the network connections between trip ends to
describe directness of possible paths and the number of
options available. For example, the connectivity of a street
network can be quantified by measuring the number of
intersections per unit of area.5–9 Considering proximity and connectivity at the scale of the neighborhood
is important for describing trips that can potentially be
made by walking.
Urban design and street amenities, such as sidewalks
of a certain width or condition, may also encourage
walking trips. Such data are not readily available and
must often be created from in-situ observations. Kitamura, et al collected and recorded data on sidewalks and
streets from visiting sites in the field.10 Instruments for
measuring the walkability of single street segments have
been evaluated on their strength in predicting physical
activity outcomes.2,11,12 One such tool is the Systematic
Pedestrian and Cycling Environmental Scan (SPACES).
It codes attributes of sidewalks, intersections, and roads
by street segment.13 Such tools are based on evidence of
associations between the built environment and walking but go beyond enumeration of features and toward
assessments of quality.
Composite measures can be used to quantify the
proximity, connectivity, and urban design dimensions
of a street segment or neighborhood areal unit in a
single measure. A number of different methodologies
for combining component values in composite walkability measures have been studied. Factor analyses,14,15
principal components analysis,15,16 and structural equation modeling17 have been used to create indices and
demonstrate multiple pathways between covariates and
walking outcomes. A more straightforward approach
has been the ranking of built environment data values
by decile followed by linear combination.18,19 Several
studies categorize locations into high walkability and low
walkability settings based on composite values.20–22 Using
composite measures offers benefits such as reducing spatial autocorrelation;23 however, the computation of such
variables also involves additional time, data, and expertise
for sophisticated assessment. As such, these measures
may be difficult to interpret for decision-makers and the
public, and could limit their utility for local planning or
public health practitioners.
To date, work with composite measures has been limited to demonstrating associations between walkability
measures and walking outcomes. In this work we will use
previously reported findings to create our own composite measure that includes freely available data from the
US Census and the Internet search firm “Google” and
compare its predictive strength with a similar composite
measure from the technical literature.
Through this work we test the following hypotheses:
1. Composite (multicomponent) measures are superior
to single component measures in predicting walking
behaviors
2. Publicly available measures of the built environment,
such as those derived from Google Maps and openaccess satellite data, are comparable in predictive
power to proprietary measures published in the
technical literature.
Methods
SEQOL Study Design
This work was completed as part of a U.S. Centers for
Disease Control and Prevention (CDC) sponsored study
titled the Study of Employee Quality of Life (SEQOL),
which focused on the travel behaviors and physical
activity of employees at Atlantic Station, a mixed use
and transportation oriented development in the center of
Atlanta’s Midtown business district.22 Participants were
recruited from office, retail, and service-based employers within Atlantic Station. Travel and built environment
measures were constructed for Atlantic Station, as well
as each participant’s residential neighborhood. Survey
data included home address, age, sex, race, marital
status, education attainment, household income, and
salary. In addition, participants were asked to record
their travel activity for 4 consecutive days (2 weekdays
and 2 weekend days) using a previously developed travel
diary.24 Travel diary information was organized by trip
and analyzed to count the number of home-based walking
trips that participants made.
Built Environment Measures
Characteristics of the built environment hypothesized to
be associated with nonautomobile travel (both nonmotorized and transit) are described in Table 1. These included
population, employment, and household densities, as well
as the number of retail destinations, transit stops, and
intersections per unit of area. In addition, the percentage
of streets with 1 or more sidewalks was quantified. Each
of these variables was calculated using a half-mile radius
buffer around each residential and employment location address in ESRI’s ArcMap 9.3. The centers of each
buffer were geo-located using the addresses provided by
participants in the survey.
Within each half-mile radius buffer, population
and employment densities were calculated using areaweighted averages of census tract-level data on midcensus (2005) estimates. The destinations within each
buffer were identified and counted using the publicly
available inventory of ‘places of interest’ from Google’s
mapping utility, “Google Earth” (example in Figure
1). The destinations variable quantifies the number of
neighborhood-scale trip ends, including groceries, gas
stations, pharmacies, restaurants, banks, coffee shops,
Table 1 SEQOL Built Environment (BE) Variable Definitions
SEQOL BE
variable
Operational definition
Units
Source
Conceptual definition
Proximity /
connectivity
Proximity
Population
density
Area weighted average of
Residents /
gross population density
acre
calculated from census tracts
intersecting buffer around
each location.
Atlanta Regional
Commission’s
(Atlanta MPO) midcensus estimates
Concentration of residents in
a place encourages other land
uses to colocate and indicates
the proximity of residents
to each other. Increased
density is also an indicator
of shorter distances between
destinations.
Employment
density
Area weighted average of
Employees /
gross employment density
acre
calculated from census tracts
intersecting buffer around
each location.
Atlanta Regional
Commission’s
(Atlanta MPO) midcensus estimates
Proximity
Concentration of employees
serves as a proxy for jobs and
indicates the intensity of the
land use with regard to commercial activity. This relates
to trip generation for both
residents and other employees
in the area.
Destinations
Number of destinations
inside buffers around each
location. Eight types of destinations were summed to
obtain the total: restaurants,
pharmacies, coffee shops,
grocery stores, bars, gas
stations, retail stores, and
banks.
# of destinations
Google Earth’s
‘Places of Interest’
combined with buffers around each
location created in
AcrGIS
Destinations go beyond
employee density by more
directly measuring the types
of commercial services
involved in daily trips. As
the number is assessed for
identical buffers around each
location, it is effectively a
concentration.
Proximity
Intersections
Number of intersections
inside buffers around each
location. Intersections were
defined as points where 3 or
more street segments converged and were assessed
without limited access
freeways.
# of intersections
Intersections were
determined using
the MPO’s road
network shapefile,
limited access roads
such as freeways
were removed before
analysis
Intersections measure the
connectivity of the street
network and thus the number
of pathways possible for
making walking trips and the
directness of possible paths.
As the number is assessed for
identical buffers around each
location, it is effectively a
concentration.
Connectivity
Transit stops
Number of bus and rail
transit stops inside buffers
around each location.
# of stops
Metropolitan Atlanta
Rapid Transit Association (MARTA)
and Georgia Rapid
Transit Authority
(GRTA)
Transit connects people with
destinations by facilitating
trips that would otherwise
be made by car. In addition,
walking to and from transit
stops can serve as important components of recommended physical activity. As
the number is assessed for
identical buffers around each
location, it is effectively a
concentration.
Connectivity
Sidewalks
Percentage of street length
with one or more
%
Google Maps
Satellite imagery
combined with street
network from MPO
Sidewalks are the infrastructure which facilitate walking trips. The presence of
sidewalks on paths between
origins and destinations can
make walking a more attractive mode choice for trips.
Connectivity
691
692 Vargo, Stone, and Glanz
and general retail establishments. As such, this variable is
hypothesized to capture regular daily commercial activity
likely to generate walking trips. It should be noted that
parks were excluded from this measure because of the
limited quality of the greenspace data available through
Google.
The study’s measures of connectivity were derived
from a regional roads data layer provided by the Atlanta
Regional Commission (ARC), Atlanta’s metropolitan
planning organization. Limited access roads, such as
freeways and their on-ramps, were removed from the
regional roads file to include only those roads where
pedestrian travel was possible. The refined regional roads
network was then used for calculating the number of
intersections in each buffer zone, as well as the presence
of sidewalks along 1 or 2 sides of the street. To determine
which street segments were equipped with sidewalks, we
again chose to use a free and openly available source of
data: Google’s collection of satellite and surface imagery
accessible through Google Maps (example in Figure
2). Street segments in the buffer were coded as having
sidewalks on 1 side, both sides, or no sides of the street.
We tested the predictive power of each single component measure and 2 composite measures, one adopted
from an earlier study and a second measure created as part
of the SEQOL study. The first composite measure is the
Figure 1 — An example destination calculation using Google Earth.
Figure 2 — An example sidewalk calculation using Google Street View.
Google Walkability 693
“neighborhood accessibility” (NA) measure developed
by Krizek.16 Through a longitudinal study of neighborhoods in Seattle, Krizek created this composite measure
based on 3 components: household density, number of
retail employees, and block area. The measure used 2
spatial domains together to aggregate values of built
environment variables. Grid cells 150m by 150m were
used to calculate values of each variable that were then
averaged using quarter-mile buffers around each location.
The composite measure was found to be a statistically
significant correlate of changes in vehicle miles traveled
and person miles traveled.16
We derived the Krizek household density component
for each grid cell in our study area using area-weighted
averages of the 2005 ARC estimates of households by
census tract. The number of retail employees in each grid
cell was calculated using an inventory obtained from the
Georgia Department of Labor for the year 2007. Retail
employment was defined using the 2-digit North American Industry Classification System (NAICS) codes for
retail trade (44-45). Block area per grid cell was measured
through GIS using blocks as defined for the 2000 Census.
The Krizek composite walkability index was calculated
by linear combination of the values from the 3 Krizek
components and adjusted to range from 0 to 100. Coefficients for the components in the linear combination
were obtained by factor analysis performed using SPSS
15 statistical software.
For comparison with the Krizek NA measure, we
created a second composite measure that incorporated
proximity and connectivity measures using data obtained
from several publicly accessible sources. To do so, the
6 SEQOL built environment variables (population,
employment, and intersection density, number of destinations and transit stops, and fraction of street length with
sidewalks) were adjusted to cover a range from 0 to 100
and an average of the 6 was calculated for each location
using Microsoft Excel.
Analysis
Measures of covariation between indices and their components were used to assess colinearity between different
components. For our analysis of the built environment
measures with walking activity, a binary variable to identify walkers from nonwalkers was used as an outcome
in logistic regression analyses. Individuals making 10%
or more of all their home-based trips by walking were
considered walkers. Odds ratios were used to assess the
magnitude of the relationship between built environment
variables and walking outcomes. Results were assessed
using a significance level of 0.05 and statistical analyses
were performed using SAS Version 9.2. Each significant
association identified using bivariate analysis was tested
in multivariate logistic modeling while controlling for
sex, race, and income. The demographic covariates were
introduced into the models to test the associations initially
observed in the presence of attributes believed to influence walking behavior and to control for the possibility
that participants categorized as walkers choose to live
in more walkable settings. Odds ratios are derived through
logistic regression and are used to compare the odds of an
outcome between different groups; in this instance, the
tendency of individuals residing in pedestrian-supportive
neighborhoods to be categorized as “walkers.” The odds ratio
is commonly used as an estimate of relative risk between
individuals or populations. The metrics and methods
of logistic regression are commonly used with survey
research focusing on health behavior outcomes, and
they have been used with walking behaviors and built
environment.25–27
Results
A total of 59 employees at Atlantic Station were recruited
for the study. Of these, 56 individuals completed both
the initial and follow-up surveys. A description of the 56
participants’ demographic characteristics is included in
Table 2. As reported there, our sample was slightly
skewed toward females (59%), white/non-Hispanic (56%)
and households with incomes above $40,000 (58%).
Table 2 Description of Study of Employee
Quality of Life (SEQOL) Participants and
Walking Outcomes
#
%
Sex
Male
Female
24
35
40.70%
59.30%
Age
Under 50
Over 50
54
5
92%
8%
Race
White/Non-Hispanic
Black/Other/Multiple
33
26
55.90%
44.10%
Marital status
Widowed/divorced/separated or
single and never married
Married or living with partner
38
21
64.40%
35.60%
Highest education
High school/some college
College/graduate school
22
37
37.30%
62.70%
Household income
Under $40,000
Over $40,000
25
34
42.40%
57.60%
24
15
42.90%
26.80%
9
16.10%
10
17.90%
Demographics
Walking outcomes
Walking trips*
Made any walking trip
Made a home walking trip
Made both home and work
walking trips
Made 10% or more of home
trips by walking
* Only 56 participants completed travel diaries.
694 Vargo, Stone, and Glanz
During the travel diary period, 15 participants (27%)
made walking trips at residential locations. Of these
participants, 10 made 10% or more of their home-based
trips via walking and were classified as walkers. Bivariate odds ratios for the walkability composite measures
and SEQOL components (Table 3) showed positive
associations with the individual’s likelihood of being a
walker. Results indicate that, for every unit increase in
the composite scores measuring neighborhood walkability, individuals are 6–10% more likely to be in the
walking category, with the Krizek measure found to have
a modestly higher ratio. The individual components of
the SEQOL Index were also investigated and several
had significant odds ratios larger than 1 (see Table 3). In
particular, the bivariate odds ratio for population density
was the largest of all observed significant associations
(OR = 1.212).
The final multivariate models revealed the importance of composite measures over single component
variables, particularly population density. These analyses produced a refined list of composite measures and
components associated with walking (Table 4). When
accounting for demographic factors, population density,
which demonstrated the largest association in the bivariate analysis, was no longer shown to be associated with
walking. However, the associations initially observed for
the Krizek and SEQOL composite measures persisted.
Both of the variable components obtained using Google
sources for the composite SEQOL measure—proximity
of destinations and percentage of sidewalks—were found
to be significantly associated with the walking outcome
in the presence of demographic data.
Discussion
The results of our analysis yield several important insights
for practitioners of public health and planning. First, our
findings show stronger associations between composite
measures and walking outcomes than between single
component measures and walking outcomes. This implies
that more inclusive or comprehensive evaluations of the
built environment, represented here in composite indices,
are of greater relevance for assessing the impact of the
built environment on local walking behaviors. This finding is suggestive of the most effective means of measuring
walkability in general, and of what types of measures are
likely to most accurately predict outcomes resulting from
policies designed to enhance neighborhood walkability.
While commonly employed in physical activity
research, population density alone was not found to be a
significant predictor of walking behavior in the presence
of demographic data. This finding is in conflict with previous studies showing population density to be the most
powerful predictor of walking outcomes.28–30
Second, a composite measure of the built environment derived from free, publicly available, and geographically extensive data sources was found to be a
statistically significant predictor of walking outcomes.
While not found to have the same predictive power in
our study as a previously published measure developed
from less accessible data sources, this public domain
composite variable showed sufficiently strong predictive
power to merit consideration for use by planning and
public health researchers or practitioners lacking access
to more complex measures.
It is important to note that the varying predictive
strengths between the SEQOL and Krizek NA composite
measures may originate in their differing constructions.
The Krizek NA measure uses 2 spatial units (grid cells and
quarter-mile buffers) to evaluate the built environment
while SEQOL uses a single, larger circular buffer (halfmile). Also though the 2 composite measures contain
similar conceptual constructs, they are operationalized
differently. For example, both include an urban form
component related to connectivity. However, Krizek uses
block area while SEQOL includes intersection density.
While Krizek includes measures to capture separate
aspects of proximity and connectivity, SEQOL includes
twice the number of components. Finally, Krizek uses
principal components analysis as a variable reduction
Table 3 Bivariate Odds Ratios of Making >10% of Trips via Walkinga
Index
Odds ratio
95% CI
Krizek NA Index
1.098
1.023–1.179
SEQOL Walkability Index
1.064
1.010–1.121
Population density index (per/acre)
1.212
1.005–1.460
Employment density (emp/acre)
1.055
0.990–1.123
Destinations (#)
1.027
1.003–1.051
Intersections (#)
1.016
0.994–1.038
Transit (# stops)
1.027
0.999–1.057
Sidewalks (% of street length with sidewalks
on 1+ sides)
1.032
1.003–1.061
Component
a Significant
predictors shown in bold; P < .05.
Google Walkability 695
Table 4 Multivariate Model Results for the Odds of Walking
Odds ratio
Variable
Make 10% of trips by walking
95% CI
0.75
0.089–6.312
Model 1
Gender (ref = female)
Race (ref = nonwhite)
0.462
0.067–3.194
Income (ref≤$50K)
1.168
0.182–7.485
Krizek NA Index
1.103
1.075–1.198
0.705
0.099–5.005
Model 2
Gender (ref = female)
Race (ref = nonwhite)
0.645
0.115–3.606
Income (ref≤$50K)
0.809
0.170–5.695
SEQOL walkability
1.064
1.005–1.127
Gender (ref = female)
0.504
0.072–3.544
Model 3
Race (ref = nonwhite)
0.555
0.101–3.055
Income (ref≤$50K)
1.05
0.183–6.004
# of destinations
1.027
1.001–1.054
Model 4
Gender (ref = female)
0.886
0.120–6.544
Race (ref = nonwhite)
0.446
0.074–2.705
Income (ref≤$50K)
0.797
0.140–4.543
% of street length with sidewalks
on 1+ sides
1.035
1.003–1.068
a Significant
predictors shown in bold; P < .05.
technique while SEQOL indices were created using a
scaling approach with equal weighting. These differences in covariate design allowed us to compare different
constructs of the same underlying theory of how the built
environment influences walkability.
Despite these differences, the 2 composite measures
were highly correlated (r = 0.79 P < 0.001), and both
showed significant association with walking outcomes.
This result is important for planners and public health
practitioners facing data availability and cost constraints.
It implies that different composite measures may be
employed with similar effectiveness. This allows practitioners to create locally derived metrics of walkability
rather than relying on findings from other settings.
Third, our SEQOL index was designed to examine
the utility of a composite measure that could be more
easily created for specific areas and used local data. Early
studies of walkability focused on generalized measures
of land use and walking outcomes averaged over large
areas (ie, county-level values of population density and
regional mode split data). Our disaggregated approach
more specifically evaluates the built environment
around specific locations of interest and thus minimizes ecological fallacy due to the use of generalized
values.31 This was important since we are interested in
the association of walkability with individual walking
behaviors and locations. Our half-mile radius buffers
capture more of an individual’s walking trips;14 however, buffers created with shorter radii capture built
environment attributes located closer to an individual’s
place of residence or work and are assumed to have a
greater influence on walking mode choice. Walkability
studies have followed similar assumptions with regard to
buffer size when using GIS measures of the built environment.32 More sophisticated analyses could use buffers
created from the street network to combine connectivity
of an area with proximity measures such as the number
of destinations.
Finally, we included several publicly available, geographically extensive, and free data sources, including 2
obtained from Google products: destinations (a measure
of proximity) from Google Earth and sidewalks (a measure of connectivity) from Google Maps/Street View.
Other metrics in the SEQOL index, such as the number
of intersections or transit stops, can also be obtained from
Google Maps. We estimate that, on average, measures
of the 6 components around a single address could be
collected from Google and the Census in less than 2
hours. Our combination methods were performed using
Microsoft Excel rather than more sophisticated statistical
696 Vargo, Stone, and Glanz
methods. Nonetheless, the SEQOL Index was shown to
be a consistent predictor of individual walking behavior
near residences. This finding suggests that municipalities
of various sizes, levels of ability, and with varying access
to data and resources should be able to create location
specific walkability metrics on which to base decisions.
Furthermore, community health research can combine
such fine-scale measures of the built environment with
meaningful, qualitative data from interviews and focus
groups.
Further study should be devoted to making such
composite measures even simpler to use. As presented
here, indices are created from a sample of local sites. The
walkability index values can be compared against each
other but it would be difficult to perform the assessment
for a single location and get a meaningful metric. Future
work should create universal index values related to the
various components so that individual addresses could
be assessed to determine walkability without needing to
calculate composite measures for a sample of locations
throughout the region. That is, a single address could
assess each component and create a composite measure
that could immediately be evaluated for walkability
based on a uniformly applied scale. The majority of the
component data for a location can be collected in a couple
of hours from Google and other data providers over the
web. This would extend the application of the tool to
individual residents and neighborhood associations and
allow them to argue for local improvements independent
of prior action at the municipal level.
The use of publicly available imagery and destination databases will continue to evolve and expand with
time. Internet products that make use of Google’s data
can already be used to more quickly and cost effectively
gather component information for composite measure
development. The use of Google data are not only more
easily adapted and used for automated assessments of
the built environment, but it is also already familiar to
many users. One example is the popular real estate tool,
Walkscore, which uses the Google Map’s application
programming interface (API) to compile retail and restaurant information within a certain proximity to a location.
The results are given on a 0 to 100 scale and could easily
be incorporated into the SEQOL index. However, such
tools should be used as a means of obtaining individual
components for further combination into more comprehensive composite measures, and may require validation
to assess reliability. In fact, when Walkscore ratings were
considered for SEQOL participants’ addresses, we did
not find a significant association with walking behavior
(OR = 0.967 CI = 0.903, 1.035) in bivariate analysis. Our
study did find 2 single components to exhibit significant
associations with walking behavior; specifically, the 2
Google-derived components sidewalks and destinations.
However, the magnitude of these associations is smaller
than those of composite measures. Thus, we suggest that
more comprehensive composite measures be used as tools
for incorporating walkability into decisions concerning
changes in the built environment.
Conclusion
By encouraging a focus on composite measures, this
study advocates for the concurrent assessment of 2 conceptual components of walkability: proximity and connectivity. Evidence of a synergistic relationship between
these 2 types of measures suggests a need for considering
both connectivity and proximity in decision-making. For
example, investments to increase connectivity are most
advisable where proximity is greatest. The consistency of
findings across different composite indices also encourages practitioners to generate and use local measures
rather than relying on single, uniform recommendations (eg, national averages) when assessing the built
environment. Planners and public health researchers
seeking to describe the built environment accurately
and efficiently, as well as practitioners seeking to
improve the walkability of their own jurisdictions
should evaluate local built environments independently
rather than applying findings from other areas. This study
suggests that the use of Google imagery and spatial databases facilitates such work without sacrificing quality in
assessments of the design of the built environment and
walking outcomes.
Acknowledgments
The authors would like to acknowledge the support and effort
of the entire SEQOL team, including Craig Zimring, Nicole
Dubruiel, Karen Mumford, Julie Brand, Lu Yi, and Arthur
Wendel. This publication was supported by Cooperative Agreement Number U48 DP 000043 from the Centers for Disease
Control and Prevention to the Emory Prevention Research
Center. The findings and conclusions in this journal article are
those of the authors and do not necessarily represent the official
position of the Centers for Disease Control and Prevention.
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