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Am J Prev Med. Author manuscript; available in PMC Apr 1, 2010.
Published in final edited form as:
PMCID: PMC2844244
NIHMSID: NIHMS105856

Measuring the Built Environment for Physical Activity

State of the Science

Abstract

Physical inactivity is one of the most important public health issues in the U.S. and internationally. Increasingly, links are being identified between various elements of the physical—or built—environment and physical activity. To understand the impact of the built environment on physical activity, the development of high-quality measures is essential. Three categories of built environment data are being used: (1) perceived measures obtained by telephone interview or self-administered questionnaires; (2) observational measures obtained using systematic observational methods (audits); and (3) archival data sets that are often layered and analyzed with GIS. This review provides a critical assessment of these three types of built-environment measures relevant to the study of physical activity. Among perceived measures, 19 questionnaires were reviewed, ranging in length from 7 to 68 questions. Twenty audit tools were reviewed that cover community environments (i.e., neighborhoods, cities), parks, and trails. For GIS-derived measures, more than 50 studies were reviewed. A large degree of variability was found in the operationalization of common GIS measures, which include population density, land-use mix, access to recreational facilities, and street pattern. This first comprehensive examination of built-environment measures demonstrates considerable progress over the past decade, showing diverse environmental variables available that use multiple modes of assessment. Most can be considered first-generation measures, so further development is needed. In particular, further research is needed to improve the technical quality of measures, understand the relevance to various population groups, and understand the utility of measures for science and public health.

Introduction

Physical inactivity is one of the most important public health issues in the U.S. and internationally, due to its contribution to premature mortality and economic costs (e.g., medical costs, lost productivity).13 Increasingly, links are being identified between various elements of the physical or built environment and physical activity.48 The built environment—the physical form of communities—includes land-use patterns (how land is used); large- and small-scale built and natural features (e.g., architectural details, quality of landscaping); and the transportation system (the facilities and services that link one location to another).811 Together, these elements shape access to opportunities for physical activity. (In this article, the terms built environment and physical environment are used interchangeably.)

Conceptual models guiding research on built environments and physical activity propose that different domains of physical activity (e.g., leisure, transportation, household) are affected by different environmental attributes.1215 Leisure physical activity may be most affected by access to, and characteristics of, public and private recreation facilities.16 Transportation physical activity may be most related to the proximity and directness of routes from home to destinations (known as walkability) as well as characteristics of the walking and cycling infrastructure, including sidewalks, bicycle lanes, and trails.8 Therefore, to understand the influences of the built environment on physical activity, a wide range of environmental measures are needed.

Studies of the built environment and physical activity have evolved over the past few decades. Early research focused on compliance with supervised exercise programs in relation to proximity to facilities.17 The next generation of studies examined the impact of the community environment (especially convenience of facilities) on leisure physical activity in various populations.1820 At the same time, transportation and city planning researchers were studying the relationship of land-use patterns to walking and cycling for transportation, using both survey and GIS measures.5,9,10 More recently, better measures of the built environment have been developed, and physical activity surveys have become more comprehensive, allowing assessment of specific behaviors such as walking and cycling for both recreational and transportation purposes.21,22 These measurement advances have allowed research in the past few years to examine multiple elements of the environment in relation to multiple modes and purposes of physical activity.4,5,2329

To understand the impact of the built environment on physical activity, the development of high-quality measures is essential.30 Three categories of built-environment measures are being used. Obtained by interview or self-administered questionnaires, the first group of measures examines the extent to which individuals perceive access and barriers to various elements of recreation, land use, and transportation environments. The second set of measures uses systematic observations, or audits, to “objectively and unobtrusively”31 quantify attributes of the built environment. A third group of measures involves data from archival (existing) data sets that are often layered and analyzed with GIS. Across all three categories (i.e., surveys, audits, and GIS/archival data) development and evaluation of measurement properties are still at a relatively early stage.

This article provides a description of the state of the science in measuring built-environment attributes believed to be related to physical activity. Instruments were identified through searches of the literature, expert input, and feedback from a 2007 workshop. A critical assessment is provided of perceived measures, observational (audit) approaches, and GIS-derived metrics. Whenever possible, the psychometric properties (i.e., reliability and validity) of measures are described, gaps identified in current science, and recommendations made for future progress. Although the focus is primarily on measures of the physical environment, brief mention is included of other contextual variables that are closely intertwined (e.g., crime, social environment, policy variables).32,33

Perceived (Self-Reported) Environment Measures

Evidence on the association between the built environment and physical activity behavior is derived mostly from self-report data on individuals’ perceptions of their environments.4,34 More than 100 published studies have examined physical activity behavior in relation to perceptions of the environment. The environment in these studies includes a combination of the physical (built) environment,10,35 social factors,33,36 and policy influences.3739 In a recent meta-analysis involving 16 studies,7 positive associations were observed between physical activity and several variables, including perceived presence of recreation facilities, sidewalks, shops and services, and perceiving traffic not to be a threat to safety. In the current review, the focus is on survey instruments that are relatively comprehensive (i.e., assess multiple environmental constructs) and that have been tested for psychometric properties (primarily test–retest reliability).

Description of Approach

Several evidence-based frameworks have been developed to aid researchers and practitioners in determining which aspects of the built environment are most likely to influence physical activity (Table 1). Using published evidence, interviews with experts, and Delphi methods, Pikora and colleagues40 identified four key environmental domains: functional, safety, aesthetic, and destination, along with nine specific elements within the domains. This conceptual framework has been used to guide development of perceived-environment measures. Ramirez and colleagues41 used a five-phase expert review process to identify indicators of activity-friendly communities. The Ramirez indicators map reasonably well with the Pikora framework, although the former includes a larger focus on policy-related variables (e.g., local government funding, organizational incentives).

Table 1
Factors in the built environment influencing physical activity (Pikora40 and Ramirez41)

To measure these various indicators, data on the perceived environment have been collected by interviewers (by telephone) and by self-administered methods (in person or by mail). Most often, questions are developed and administered as part of a research project. In other cases, items on the perceived physical environment have been added to surveillance systems, such as the CDC’s Behavioral Risk Factor Surveillance System.42 Individual responses from these surveys can be aggregated to identify patterns in design and neighborhood features by geographic region, population subgroup, or over time (e.g., lack of access to sidewalks or parks), to determine associations between these design features and physical activity.

Tools and Measures

Table 2 presents a set of tools that measure the perceived built environment.29,4362 Because more than 100 studies of the perceived built environment and physical activity have been conducted,34 it is impractical to summarize all instruments used to date. Those shown in Table 2 cover a variety of populations, administration modes, and levels of detail; each provided adequate descriptions of the process of development and psychometric/measurement properties (primarily test–retest reliability). Our review includes 15 instruments used with adults and 4 instruments that collected data from youth.

Table 2
Summary of selected instruments measuring the perceived environment for physical activity

Questionnaires ranged in length from 7 to 68 questions. The most commonly assessed variables involved land use, traffic, aesthetics, and safety from crime at a neighborhood or community level. Most of the studies were conducted in mid-sized to large cities. Of the 19 questionnaires examined, four were used with a substantial sample of minority populations.29,44,49,53,61 Only one study46 presented separate reliability data for urban and rural participants. The tool most frequently used internationally is the Neighborhood Environment Walkability Scale (NEWS),47 or the abbreviated version (ANEWS).54,56 Use of this tool has been fostered by collaborations such as the International Physical Activity and the Environment Network (http://www.ipenproject.org).

Reliability

Ratings of test-retest reliability have been suggested by Landis and Koch63 in the following categories: 1.0–0.8 (almost perfect agreement); 0.8–0.6 (substantial agreement); 0.6–0.4 (moderate agreement); 0.4–0.2 (fair agreement); and 0.2–0.0 (poor agreement). Using these criteria, the vast majority of questions and scales that reported reliability fell in the substantial or almost perfect range of agreement. In studies where both physical and social factors were measured, the variables in the physical environment tended to show higher reliability than those in the social environment (e.g., safety from crime, social capital).

Consensus is lacking about the applicability of other reliability measures, such as inter-item correlations (Cronbach’s alpha) or factor analyses that are commonly used in surveys of beliefs and attitudes. There is little a priori reason to expect conceptually similar environmental variables to co-occur (e.g., parks and trails), so lack of correlation may not reflect a measurement limitation. Conceptually dissimilar items may appear together frequently (e.g., sidewalks and heavy traffic), so alphas and factor analyses may be difficult to interpret. On the other hand, techniques like factor analysis may identify useful groupings of variables.

Validity

Evaluating validity for measures of the perceived environment is challenging and has been comprehensively addressed by only a few studies. Some forms of validity testing require a criterion or gold standard against which to compare a perceived measure. For some attributes of the perceived environment, such as aesthetics, it can be argued that perceptions are the reality.

Three types of validity are most relevant:

  1. Content validity is the extent to which an instrument measures the appropriate content and represents the variety of attributes that make up the measured construct.64 This can be based on formal models, expert opinion, and/or community input. For the perceived measures of the environment, two studies40,41 systematically identified the key domains. In these studies, multidisciplinary panels of experts reviewed a large number of constructs, resulting in a set of domains and/or indicators that are empirical and should be considered for measurement development.
  2. Construct validity is the degree to which a measure “behaves” in a way consistent with theoretical hypotheses64 and is predictive of some external attribute (e.g., physical activity behavior). Most validity work on physical activity and the built environment has involved assessment of construct validity. For example, in evaluating ANEWS,56 researchers examined individual- and block group–level associations of scores for residential density and land-use mix with walking for recreation and transportation (after controlling for sociodemographic factors).
  3. Criterion-related validity (sometimes considered a subset of construct validity) is the degree to which a measure is predictive of some gold-standard measure of the same attribute.64 For measures of the perceived environment, this may involve the degree which perceptions are correlated with observed or archival data. Nine published studies26,28,29,6571 have compared perceived measures of the built environment with data obtained by observation and/or with GIS-derived measures. All of these studies were conducted in the U.S. (five of nine in the Southeast). Three of the nine studies28,29,66 compared perceived measures with audit-obtained data, and eight studies26,29,65,6771 used GIS data as the reference standard. Many different buffer sizes (i.e., the area around a residence) were used in these studies, ranging from 400 meters to 10 miles. The majority of kappa values in these studies were in the poor to fair range (i.e., from 0.0 to 0.4). Only one study70 compared perceived and objectively measured environmental facilities among youth.

Some measures in our review generally had better evidence of criterion validity than did others, but substantial variation also occurred within measures. When participants were asked to report relatively concrete attributes, such as existence of sidewalks or presence of cul-de-sacs, reliability and validity tended to be higher.28,69 Perceived crime seemed to have been among the lowest levels of validity.71 Several explanations have been suggested for the low levels of agreement between perceived and observed neighborhood conditions. It is documented that size of community affects neighborhood perceptions.72 Therefore, for some items, the respondents’ varying ability to estimate distances accurately is likely to influence concordance with observed measures. This is reinforced in Kirtland et al.29 where decreasing the buffer size increased agreement. Sociologic research on neighborhood evaluation suggests that personal perceptions of the neighborhood environment are only indirectly linked to objective characteristics.73 That is, individual perceptions are derived from filtering objective characteristics through standards of evaluation, which are based on past experiences, aspiration levels, adaptation processes, and individual personality characteristics.73 Thus, the existence of unique situational and personality characteristics indicates that two individuals in the same environment may perceive it differently. Another consideration is that source bias may create spurious associations between self-reported neighborhood conditions and observed conditions (e.g., those with poor health inaccurately report poorer neighborhood conditions).32,58

Skills and Trade-Offs Associated with Using Perceived Measures

Perceived-environment data are collected by interview or self-administration. Both methods of administration present challenges, with a common problem being declining response rates for all types of surveys.74 In reliability studies of the perceived environment and physical activity, telephone survey response rates ranged from 31% to 87%.74,75 Response rates can be negatively affected by long questionnaires.76 Therefore, it is important to select the questionnaire that is as short as possible yet measures what is needed for the project.

Observational Measures (Community Audits)

In addition to perceived-environment measures, researchers have developed instruments and protocols to measure the actual physical environment as it is directly observed.77

Description of Approach

Audit tools allow systematic observation of the physical environment, including the presence and qualities of features hypothesized to affect physical activity (e.g., street pattern, number and quality of public spaces, sidewalk quality). Many characteristics of the physical environment can be readily measured without such direct observation, using existing data, such as through GIS or aerial photos (discussed later). Such “remote” methods may be less labor intensive and therefore less time consuming, although no research known to date has directly compared the resources consumed by these various methods. In contrast, researchers use audit tools to collect primary data on physical features that are not commonly incorporated into GIS databases (e.g., street trees, sidewalk width). Audit tools also are used for measuring physical features that are best assessed through direct observation (e.g., architectural character, landscape maintenance).

Not all audit tools are intended for research purposes; some tools were developed to support local decision making. Such tools engage community members in collecting data that will be used to better understand the needs and opportunities for changing the activity environment in their communities. Tools designed for community use are typically less detailed than those designed for research purposes and may not have been assessed for reliability.77 This paper includes a review of the tools that have been published in peer-reviewed sources and are designed primarily for use in research.

Audit tools typically require in-person observation for collecting data (as opposed to videotaping or other methods).11 Researchers walk or drive through a neighborhood, park, or trail, systematically coding characteristics using definitions and a standardized form. For assessing neighborhood or community features, street segment is the typical unit of observation. Segments typically comprise two facing sides of one street block. The audit tool itself is usually a paper form containing close-ended questions (e.g., check boxes, Likert scales) and sometimes open-ended questions or comments. Segments are typically sampled because it is not feasible to audit entire neighborhoods, with some exceptions (e.g., Lee et al.78). Sampling is either random or purposeful. Purposeful sampling ensures that rare but important features of the environment, such as parks or corner stores, are included. Segments of trails79 and areas within parks80,81 also can be units of observation.

Tools and Measures

Researchers have developed several audit tools in recent years. Filling a large gap, Active Living Research (a national program of the Robert Wood Johnson Foundation) supported the development of several observational instruments and provides instruments and related information on its website (www.activelivingresearch.org). Separate tools measure community environments (neighborhoods and cities), parks, and trails. Table 3 summarizes key characteristics of 20 audit tools.11,40,7897 Tools vary significantly in the detail with which they measure various features, from one or two items to dozens of items addressing many distinct characteristics of sidewalks or buildings. Among community audit tools, the Physical Activity Resource Assessment (PARA) tool, Walking Suitability Assessment Form, and Bicycling Suitable Assessment form include less detail. The two park audit tools shown in Table 3 are quite detailed, although the Environmental Assessment of Public Recreation Spaces (EAPRS) Tool is the most extensive (712 items).

Table 3
Summary of instruments measuring the observed environment for physical activity

Most community audit instruments include one or more measures of: land use (e.g., presence and type of housing, retail); streets and traffic (e.g., traffic volume, presence of traffic calming); sidewalks (e.g., presence and continuity of sidewalks); bicycling facilities (e.g., presence of bike lanes); public space/amenities (e.g., presence of street furniture or benches); architecture or building characteristics (e.g., building height); parking/driveways (e.g., presence of parking garages); maintenance (e.g., presence of litter); and indicators related to safety (e.g., presence of graffiti). Other features of the community environment are observed less consistently. For example, only three community audit tools include measures of noise levels or the presence of dogs, and only one tool (the Analytic Audit Tool) includes measures of health promotion supports (e.g., presence of billboards or other elements promoting physical activity). Additional instruments have been developed to count people in specific settings and contextual information (e.g., accessibility of a facility). For example, reliable observational tools have been developed for school settings (System for Observing Play and Leisure Activity in Youth; SOPLAY)98 and parks (System for Observing Play and Recreation in Communities; SOPARC).99

Reliability

Inter-observer reliability is the primary form of reliability assessed, although test–retest reliability is relevant for assessing stability of observed features. For community audit tools that report reliability by item or domain, measures of physical disorder/tidiness/safety-related features tend to be less reliable, compared to measures such as land use and street characteristics.

Skills and Trade-offs Associated with Using Observational Measures

In-person observation is time consuming. Researchers must select sites, define and sample segments within sites, train and monitor observers, collect data, enter data, and compute summary variables from voluminous raw data—all of which take time. Estimates of time required for data collection vary depending on the number of items observed, the type of environment (e.g., mixed use or residential only) and how the time required was calculated. For example, observations require 10.6 minutes/segment for the Analytic Audit Tool, and 20 minutes/segment for the Measurement Instrument for Urban Design Quantities.11,87 Because of the time involved, researchers need to consider carefully whether direct observation is required to answer their research questions or whether existing data (e.g., using GIS) would suffice. Research questions that involve the human qualities of the environment (how a place looks and feels) are especially appropriate for direct observation. The detailed data that can be collected by direct observation can produce results of particular value for those who can act on the findings such as urban designers, landscape architects, and traffic engineers.

As noted in Table 3, audit tools have recently been developed that use personal digital assistant (PDA) devices, such as PalmPilots, or tablet personal computers (PCs) for data collection. PDA-based tools reduce the time required for data entry, as data are automatically entered into software for analysis when collected. PDA devices and tablet PCs also can reduce errors in collecting data by limiting response sets and skipped questions, and can minimize errors that occur in transferring data from paper forms to the computer for analysis. Tools that involve electronic data input should save time for data entry. Among community audit tools that use paper forms, some have a one-page format, which, while not eliminating the need for separate data entry, may be easier to manipulate in the field, compared to multi-page tools.

Relevant skills that are needed for observing the built environment include some knowledge of the content area (e.g., urban planning, recreation studies) as well as the ability to carry out the technical methods of direct observation. Typically, observers are undergraduate or graduate research assistants from various fields (e.g., public health, social science, design, urban planning), who are trained to observe detailed features of the environment. Often recommended is some combination of classroom training (frequently with an illustrated reference manual) and training sessions in the field, in teams and/or individually, to practice measuring elements and to discuss results with a team leader. Because many terms and concepts are likely to be unfamiliar to observers (e.g., setbacks, bollards), the manual and training must provide clear definitions. In each study, observers should be trained until they demonstrate high agreement with the trainer, and inter-observer reliability should be monitored throughout the study to ensure quality of measures.

Selecting from among the available audit instruments requires careful consideration, especially for community audits, where numerous options exist. Researchers should consider factors such as domains and features observed, time required for data collection/data entry, sampling (e.g., all street segments versus a subset), how to manage/aggregate data, instrument reliability (both overall reliability and where available, reliability of specific domains such as land use and the social environment), and ability to compare results with other studies.

Using GIS-Based Measures

Description of Approach

Geographic information systems have much to offer public health researchers interested in the effects of the neighborhood or regional environment on physical activity and obesity. GIS has been defined as the “integration of software, hardware, and data for capturing, storing, analyzing and displaying all forms of geographically referenced information.”100 GIS-based measures as described here simply refer to measures of the built environment derived primarily from existing data sources that have some spatial reference (e.g., address or census boundary identification). Using GIS to characterize the built environment is the only feasible way to generate objective measures for studies involving individuals or neighborhoods dispersed across large areas.101 However, problems with existing data mean that care needs to be taken when using GIS, and more research is needed to better assess the reliability, validity, and comparability of GIS-based measures.

The following focuses on using GIS for assessing associations between built-environment characteristics and physical activity. Other applications of GIS to this field, such as for sampling study participants,102 selecting study areas,103 and organizing audit data,23 are not addressed. More than 50 illustrative studies from the public health and travel behavior literatures are included in this review. Studies using a variety of physical activity–related outcome measures were included (e.g., walking, obesity, vehicle miles traveled, and trail counts).

Measures and Data Sources

The discussion of GIS-based measures of the built environment for physical activity is organized by the following categories that represent the most frequently assessed variables to date:

  • population density;
  • land-use mix;
  • access to recreational facilities;
  • street pattern;
  • sidewalk coverage;
  • vehicular traffic;
  • crime;
  • other (e.g., building design, public transit, slope, greenness/vegetation); and
  • composite variable/index (single variable representing a combination of some of the measures above).

The reviewed studies applied any one or a combination of these measures (Table 4).19,104147 Measures of land-use mix, access to recreational facilities, and street patterns were the most common, followed by population density and composite indices. The main finding from this review was the large degree of variability in the operationalization of measures (Appendix, online at www.ajpm-online.net), making it especially challenging to compare results across studies. The next section briefly describes the GIS-based measures and data sources used in the reviewed studies.

Table 4
Geographic scale and types of GIS-based measures from selected studies, by outcome typea

Population density

Population density is one of the most common measures included in studies of the built environment and transportation-based physical activity, primarily because the data for calculating it are readily available (i.e., census and parcel-level data available from government sources [a parcel is an individual plot of land that serves as a sampling unit; data are collected for land ownership records and urban planning purposes]), it is easy to compute, and it has been consistently associated with walking for transportation.131,149151 The most common density measures from the reviewed studies were gross population density (population per total land area) 105,109,115,123,125,131,148 and net residential density (in this case housing units per residential acre).110,130,139,140,145

Land-use mix

Measures for the level of mixed land use may be categorized as accessibility, intensity, and pattern measures (Table 5), as described in detail elsewhere.152 Although some studies have simultaneously correlated multiple measures of land-use mix with physical activity behavior,111,132,134,138 it is unclear which measures yield the strongest associations with specific forms of physical activity behavior across populations and settings. Parcel-level data were required to compute many land-use mix measures. These data are derived typically from land ownership records and may be used for land-use planning; however, parcel-level data may be unavailable in some locations and in others may lack detail about land use. For business locations, alternative sources of data included Yellow/White Pages or employment records.

Table 5
Summary of types of measures for land-use mix152

Access to recreational facilities

Measures for access to recreational or exercise facilities can also be categorized as accessibility and intensity measures. There was considerable variability in the types (e.g., some included schools69,121,137 and others did not117) or categories (e.g., public or private,135,139 free or pay19) of recreational facilities studied. The Internet and telephone directories were common data sources; however, the search criteria for identifying facilities and the data quality were generally not reported. Most studies used simple calculations to assess distance to nearest facilities or density of groups of facilities. However, Giles-Corti et al.119 progressively adjusted for distance to public open space and its attractiveness and size (e.g., a “gravity measure”) and found stronger associations with use of public open space than the accessibility measures characterized by distance alone.

Street pattern

The number and directness of pedestrian routes may be captured by a variety of GIS-based measures (Table 6) that are described elsewhere.153,154 The most common of the reviewed measures was number of intersections per area (or intersection density),110,125,132,139,145,148 percentage of 4-way intersections (or connected node ratio),104,125,132 and number of intersections per length of street network.105,130,140 Although most street pattern measures used data from the street network, a recent study suggested that omitting pedestrian networks (e.g., sidewalks, pedestrian bridges, and park paths) may appreciably underestimate connectivity, particularly in conventional suburban neighborhoods.155 As pointed out by Forsyth et al.,156 methodologic issues such as this and others (e.g., determining how to handle freeways or other limited-access roads) can have considerable influence on how street patterns are measured,156,157 yet published studies rarely describe how these issues are handled.

Table 6
Abbreviated list of GIS-based variables and associated data sources

Vehicular traffic, crime, sidewalks, and other measures

Data availability for these measures depends on local policies, and these variables often need to be collected by research teams themselves. Measures of vehicular traffic and crime varied, and most data sources are not readily available in all metropolitan areas (Appendix online at www.ajpm-online.net). Measures of sidewalk coverage used mostly existing regional or county databases,108,138 with the most common measure being the ratio of sidewalk length to road length.108,125,132 Although some cities have an inventory of sidewalks, these data rarely exist in electronic format.156 The presence of sidewalks and their attributes may be extracted from aerial photos.115 However, the resolution of the images may not be high enough to distinguish details of the sidewalks, and analyses may be time-consuming and error prone.132,156

Other, less frequently used GIS-based measures included indicators of slope,68,115,125,138,141 greenness/vegetation,67,123 coastal location,126 registered dogs,129 street lighting,129 trees,108,138 public transit,148 regional accessibility,108,112 and bike lanes/shoulders.108,113 Two studies used GIS-based measures with cluster analysis to classify neighborhoods by themes (e.g., rural working class, new suburban development)124 or types (planned unit development, traditional neighborhood development, mixed).114

Composite variables

Eleven studies106,110,112,118,133,137,139,144,146,147,159 combined multiple indicators (primarily for land-use mix, density, and street pattern) into a single composite variable or index. Such indices are thought to capture the inter-relatedness of many built environment characteristics, minimize the effect of spatial collinearity, and ease the communication of results.

Three indices were applied to a single metropolitan area106,110,112,133,137,139 and three were applied nationally in the U.S. and Canada.118,144,146 The neighborhood walkability index developed by Frank and colleagues has been used for studies conducted in Atlanta GA,133 King County WA,110 San Diego CA,137,139 and Australia.159 The number of data sources and degree of computational sophistication varied between studies. Some versions of this index incorporate retail floor area ratio (FAR) as an indicator of pedestrian-oriented design. FAR is the ratio of building square footage to land square footage. Higher numbers indicate that the building is using most or all of the land, and lower ratios suggest much of the land is used for parking. For two indices, only census data were required.146,147 In contrast, the Frank et al.133 walkability index required multiple data sources, including parcel-level data, and the Ewing et al.118 regional sprawl index consisted of 22 variables.

Validity and Reliability

The accuracy and completeness of existing data sources,156,158,160,161 as well as the geographic scale at which measures are available and aggregated, contribute to the validity and reliability of the GIS-based built-environment measures.

Validity

Validity of GIS-based measures can be thought of as the degree to which the data and measures accurately reflect the real world. Inaccurate and incomplete data represent threats to the validity of GIS-based measures and stem from multiple factors. GIS data are collected for multiple purposes such as managing infrastructure investments and transit systems, collecting taxes, and advertising (e.g., Yellow Pages) and not for conducting research on physical activity.156 In addition, because the quality of data depends on personnel time and expertise, accuracy varies by region and even municipality. Also, GIS data may come from multiple sources, making errors difficult to identify.156 Missing-attribute data require that researchers make decisions as to how data may be interpolated (e.g., deriving traffic volume from Annual Average Daily Traffic counts on major roads).26,161 The validity of these estimates is unknown.

Temporal concerns may also be introduced if the age of the existing data does not match the timing of outcome measurement. If the study is carried out in a region experiencing major population or environmental change, the GIS-based measures derived from multiple sources (e.g., census, Yellow Pages) and time periods may represent a “reality” that never actually existed.156 Researchers have addressed such discrepancies by providing evidence that the study area or population has remained fairly constant140 or by using archival data.121

Although inaccurate and incomplete data are frequently cited as threats to the validity of GIS data,156,158,160,161 the degree to which the errors affect associations with physical activity is unknown. To our knowledge, only one study162 in this field has validated data from a commercial database with field census. This study compared the presence and types of physical activity facilities from these two sources in 80 census block groups and found only moderate agreement of presence of any physical activity facility (concordance = 0.39 non-urban and 0.46 urban) and poor-to-moderate agreement of physical activity facility type (kappa range 0.14 to 0.76). Most of the errors were due to missing or invalid facilities from the commercial database. Yet, given the random pattern of error and minimal error in the neighborhood-level counts of facilities, associations with physical activity or other health outcomes may be small and probably biased downward. A better understanding of how built-environment measures from different data sources compare in their association with physical activity would inform prioritization of research-related resources. Such analyses could indicate whether resources could be used efficiently to improve underlying data quality or establish consistent measurement across studies.

The choice of area for aggregating GIS-based measures introduces another source of variation in how environments are characterized and associated with physical activity. Considerable variation exists in the geographic scale used to date (Table 4). Geographic units ranged from administrative boundaries (e.g., census tracts) to buffers of set distances (usually measured “as the crow flies” but can be defined by distance along the street network) around participants’ homes and work places, and this variation likely affects which environmental variables are associated with physical activity.163 The use of standardized buffers (e.g., 400 meter radius) to reflect an individuals’ immediate neighborhoods has helped to manage the “modifiable areal unit problem”—a problem of artificial spatial patterning resulting from artificial geographic units of varying sizes and aggregation levels (e.g., census tracts) being imposed on continuous geographic phenomenon (e.g., land-use mix).164 Yet, there is much debate about the most appropriate buffer size for this research. Using large buffers may mask important within-area variation; 400 meter to 3200 meter buffers have been used commonly, based on the concept of reasonable walking distances However, the size of the relevant geographic unit may vary by age group and setting (e.g., urban core, suburban), as well as for different built environment characteristics (e.g., land-use mix, access to recreational facilities).152 The appropriate geographic scale for assessing GIS-based measures requires empirical examination to clarify.101,165

To date, the empirical evaluation of the validity of GIS-based measures comes mostly in the form of construct validity.5,165,166 To evaluate the validity of GIS-based measures, it is crucial to conduct more head-to-head comparisons of these measures.131,138

Reliability

The reliability of GIS-based data and measures can be viewed as the extent to which existing data from different time periods for a single area can yield the same measurement values (test–retest reliability), as well as the extent to which two independent analysts can produce the same measurement values (inter-rater reliability). In the case of GIS-based measures, test–retest reliability is partially dependent on how quickly the built environment changes, as well as the consistent maintenance of GIS databases across time, regions, and sources. Neither of these issues has been sufficiently examined.

High inter-rater reliability may be achieved by ensuring that analysts apply similar definitions and data for computing their variables.156 Unfortunately, such information is rarely provided in sufficient detail to permit replication. The protocols developed by the University of Minnesota, entitled Environment and Physical Activity: GIS Protocols157 and Environment, Food, and Youth: GIS Protocols,167 serve as models for documenting GIS procedures. However, despite detailed documentation, replication can still be limited by the software used to automate computations of GIS measures, which is prone to inconsistent programming between versions (e.g., computing network distances in ArcView), differences in the nature and quality of the raw data, and incomplete documentation.156

Skills and Trade-offs Associated with Using GIS Measures

Knowing how to obtain, clean, manage, and analyze GIS-based data requires trained personnel and sufficient time to conduct these activities.161 Often there is a mismatch between the variables conceptualized by researchers during a study’s design phase and the messy data encountered by GIS technicians.156 Yet, the considerable time, expense, and discussions of how these data are rectified to yield clean data for analysis are virtually absent from published studies.156

Obtaining GIS data can be time-consuming and expensive. Currently, no standardized method of measuring or cataloging these measures and no centralized national repository of such data exist.101 GIS data may be downloaded from the Internet in some regions, but may require contacting government offices and developing written agreements to use the data in other regions.161 For studies that involve multiple jurisdictions, the sources and cost of data may vary. For example, in the Dallas–Ft. Worth TX metropolitan area, the cost in 2007 of parcel-level data ranged from $0 in one county to $50,000 in another county. In a study conducted at the University of South Carolina, five additional personnel were hired to assist the research team, and a university lawyer was recruited to ensure the confidentiality of shared data.161 The study costs were nearly double the budgeted costs.

Not all studies relating the built environment to physical activity demand expensive and extensive data and numerous research staff. Many of the reviewed studies were conducted in metropolitan areas with well-maintained and detailed built-environment data, such as Portland OR,66 San Diego CA,137,139 Seattle WA,67,116 San Francisco Bay Area CA,107,111 and Minneapolis–St. Paul MN.113,132 Other studies relied primarily on available census data for measuring walkability,118,146 or limited the number of GIS-based measures, for example, studies of leisure-time physical activity focused on access to recreational facilities. These options may conserve time and expenses associated with acquiring and analyzing data, but they may come at a cost in terms of the accuracy, completeness, and specificity of the neighborhood measures, as well as the generalizability of results.

Challenges and Future Directions

This first comprehensive examination of built-environment measures of relevance to physical activity has demonstrated a great deal of progress over the past decade. Measures of diverse environmental variables are available that use multiple modes of assessment. Most can be considered first-generation measures, so further development is needed. Numerous challenges were identified in three broad categories, and overcoming them will require concerted effort and dedicated funding.

Technical Improvements in Measures

The complexity of the built environment constructs targeted by these first-generation measures and the resulting long lists of variables is a major impediment to widespread use and efficient analysis, especially for observational measures. Most of the reviewed measures reflected an approach of collecting many variables hypothesized to be related to physical activity. As a result, both the perceived and observed variables are sometimes difficult to analyze. But before current measures can be simplified, they must be used in multiple studies. Variables repeatedly unrelated to outcomes or found to be redundant with other variables can be deleted to produce more streamlined second-generation measures. This simplification process may be partially counteracted by the inclusion of new constructs or refinement of currently measured variables.

Measurement gaps were identified for all three categories of measures. Lack of clarity about operational definitions is especially problematic for GIS measures, because there is no standardization of raw data across jurisdictions or consensus on approaches to creating variables. Investigators are encouraged to be explicit in reporting operational definitions of variables. Perhaps it would be useful to post technical details of GIS-based computations online or cite specific protocols, such as those by Forsyth et al.157 The present review revealed a lack of validated self-report measures related to parks, trails, and workplaces, so further development is needed.

The measures reviewed here use a variety of geographic scales. For example, definitions of neighborhood or community vary, and different GIS-based buffer sizes are used. The most relevant geographic scale is likely to differ by built environment variable (e.g., walkability, distance to park); behavior of interest (e.g., walking versus biking, transport versus leisure); and population (e.g., age group, those with or without access to automobiles). For GIS measures, it would be useful if more investigators evaluated and reported results using multiple geographic scales (e.g., 0.5-, 1-, 2-, 3-km buffers).

A specific limitation of observed and GIS-derived measures is the difficulty of assessing the quality of environmental features. The difficulty of obtaining reliable reports of simple indicators of quality of such attributes as playground equipment, trail conditions, and street crossing aids illustrates a need for further development of existing measures. Perhaps methods from other fields (e.g., environmental psychology) can be identified that hold promise for application to built-environment measures.

Relevance to Populations, Settings, and Evolving Issues

It is not clear to what extent the existing environment measures are sensitive to the needs of various population groups and settings. It is likely that physical activity barriers and facilitators vary by age, physical abilities, and culture. The lack of relevance of existing measures to rural environments has been acknowledged,5,46 and environmental attributes may have different meanings in low- and high-income communities and in youth versus adults. It is most important to ensure that environmental measures are relevant to populations at highest risk of inactive lifestyles and resulting diseases, such as low-income, racial/ethnic minority, older adult, and rural populations. However, it may not be possible for any single measure to be optimal for each subgroup of interest. Thus, use of core measures with adaptations for specific target populations may be a pragmatic solution. Systematic community input is necessary to develop or adapt measures that are appropriate for the population. An important limitation is that most evaluations of measurement properties were conducted in one region, so there is the possibility that limited variability in environmental variables could reduce reliability and validity coefficients. The majority of the measures were designed to assess neighborhood characteristics of most relevance to active transportation. Few surveys were designed to provide detailed assessments of recreational environments, like parks and trails, which are expected to support recreational physical activity.

In the future, it will be important to include socio-political variables in addition to the measures of the built environment covered in this review. More systematic attention to measuring social and cultural environments could lead to improved understanding of their role in enhancing or inhibiting physical activity. Analyses that include variables from multiple levels of ecologic models are expected to be more powerful in explaining behavior.168171 Principles from ecologic models predict interactions across levels, such that built environment attributes may operate differently in various social contexts. Testing such hypotheses requires adequate measurement of both social and built environment variables. In contrast to the rapid development of built-environment measures, there is a void in published measures of policies that govern built environments.37,172 This policy-relevant information is a clear research need, because valid measures of the policy determinants of built environments and physical activity have direct relevance for public health planning and evaluation.

Utility of Measures in Practice Settings

The obesity epidemic and the continuing burden of diseases created by the low prevalence of meeting physical activity guidelines creates a public health imperative to discover and implement solutions. In this context, environmental measures must be considered for their use in research studies but also for their public health impact.

The scientific contributions of environmental measures depend on the extent to which they are widely and appropriately used. There are major challenges to using observational and GIS measures. Observational measures require investments in staff, training, travel, data management, and analysis. Capacity is limited for implementing these measures, so changes in funding priorities and provision of training and support for investigators seem to be needed. Similarly, access to GIS technicians, especially with skills relevant to the variables of interest, is limited. Systematic training programs could both build capacity of investigative teams and encourage standardization of approaches. The most fundamental problem with GIS measures is not only the lack of data in many locations, but also the low or unknown quality and completeness of data, the difficulty or cost of access, and the lack of standardization. Spatial measures require different statistical approaches than do familiar public health data,173 and the complexity of the measures creates additional challenges, so training and consensus development about the most appropriate analytic approaches are needed.

Geographic information systems data have the potential to be a useful public health surveillance tool, but that potential is largely unrealized. Ideally, the growing evidence of the impact of the built environment on physical activity, obesity, and other health outcomes will lead to the routine collection of the most critical GIS variables for surveillance purposes. However, some public health departments will not have the capacity to collect even the most basic data, so partnerships with transportation, planning, parks and recreation, law enforcement, and housing agencies will likely be required to provide access to data.

“Walkability audits” already are being used by advocacy groups, but simple and reliable measures are not often available for community groups.174 Simplified observational measures of parks, trails, schools, workplaces, and other settings can be developed from existing measures. Creating practical measures for community groups should be a goal for researchers. The incorporation of reliable and valid observational measures into health advocacy efforts should be encouraged to provide an evidence base for advocacy.

Several self-report measures of community environment variables are available and can be used for research and surveillance. It is unclear which measures, or which variables within measures, are most effective in explaining variance in physical activity and informing public health practice. As research on built environment and physical activity progresses, variables with limited utility can be dropped, but there may be a need to add variables for newly conceptualized variables.

Conclusion

A substantial literature on measurement of the built environment for physical activity now exists. These topics are of importance to both researchers and practitioners.175,176 Although limitations were identified for all types of measures, existing measures have stimulated rapid advancements in understanding environmental correlates of physical activity in a variety of populations and settings. Numerous challenges remain, such as continually improving measures, ensuring relevance for diverse population groups, and integrating built-environment measures into public health surveillance and planning systems. Focused attention to the issues raised in this review is likely to move the field forward and contribute to improving public health.

Acknowledgments

This project was funded through the National Cancer Institute and the Robert Wood Johnson Foundation (Healthy Eating Research) grant no. 63090; CDC contract no. U48/ DP000060 (Prevention Research Centers Program); the Robert Wood Johnson Foundation (Active Living Research) grant no. 57152; and the American Cancer Society Mentored Research Scholar Grant no. MRSG-07-016-01-CPPB. An earlier version of this paper was presented at a workshop sponsored by the National Institutes of Health and the Robert Wood Johnson Foundation, “Measures of the Food and Built Environments: Enhancing Research Relevant to Policy on Diet, Physical Activity, and Weight” which was held November 1–2, 2007.

Appendix: Detailed List of GIS-Based Variables and Associated Data Sources

MeasureDefinitionsStudy areasData sourcesExamples of
studies where
applied
Population
Density
No. of residents living in
census tracts or census
blocks per area (gross
population density)
California;
Indianapolis IN;
Chapel Hill NC;
New York City
NY;
El Paso TX;
Puget Sound WA;
Minneapolis–St.
Paul MN
Census17
No. of persons in housing
units per unit land area in
parcels
Minneapolis–St.
Paul MN
Census, parcel-level
data*
6
No. of persons in housing
units per unit land area in
residential parcels
Minneapolis–St.
Paul MN
Census, parcel-level data6
No. of housing units per
residential acre
Buffalo-Niagara
Falls NY
Metropolitan Area
Erie County NY;
Atlanta, GA
King County WA
San Diego CA
Census; parcel-level data;
regional land cover data
from aerial images
812
No. of residential units in
the household parcel
Seattle WACounty’s parcel-level and
building-level assessor’s
data
13
No. of persons in housing
units plus total employees
per unit land area
Minneapolis–St.
Paul MN
Census, parcel-level data6
No. of housing units as
counted by the census,
including both occupied
and unoccupied units, per
unit land area
Minneapolis–St.
Paul MN;
10 largest
consolidated
metropolitan
statistical areas in
U.S.
Census, parcel-level data6,14
Building footprint area
divided by area in parcels,
excluding vacant or
agricultural land uses
Minneapolis–St.
Paul MN
Census, parcel-level data6
No. of residents and jobs
per area
Gainesville FLGainesville built
environment database
15
Developed-area population
density
San Francisco Bay
Area CA
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use)
16
Mean net residential
density within buffer
Seattle WACounty’s parcel-level and
building-level assessor’s
13
Land-use
mix
AccessibilityDistance (network and/or
straight-line) to closest
specified destination(s)
(e.g., fast food restaurant,
school, shopping center) or
groups of destinations
Cincinnati OH;
U.S.;
Rockhampton,
Queensland;
Seattle WA;
Minneapolis–St.
Paul MN;
Northern
California
Yellow/white pages on
Internet, phone book,
school district, county
parcel-level and building-
level assessor’s data
13,1720
Accessibility index (from
gravity model) comprised
of attractiveness and travel
time
San Francisco Bay
Area CA
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use), MIN-UTP (travel
times)
16
Distance to closest
neighborhood retail
establishments based on
North American Industrial
Classification System
categories (having ≤200
workers)
Minneapolis–St.
Paul MN
3rd quarter ES202
employment records
coded, geocoded and
cleaned by the Minnesota
Dept of Employment &
Economic Development
21
IntensityNo. of types of businesses
(service, retail, cultural,
educational, recreation,
neighborhood serving/
retail, employment) located
in a neighborhood (range
from 0 to 7)
Ten largest
consolidated
metropolitan
statistical areas in
U.S.
Standard Industry
Classification codes in
specific area
14
No. of types of destinations
(churches, community
centers, libraries, p-
patches, parks,
playgrounds, post offices,
schools, swimming pools,
theaters, banks, bars,
grocery stores, and
restaurants)
Seattle WAWashington State
Geospatial Data Archive
and Urban Form Lab at
University of Washington
22
No. of types of businesses
and facilities (department,
discount, and hardware
stores; libraries, post
offices; parks; walking and
biking trails; golf courses;
shopping centers; and
museums and art galleries),
ranging from 0 to 7
Pittsburgh PASouthwestern
Pennsylvania
Commission databases
23
No. of types of businesses
and no. of establishments
of each type, classified as
institutional (church,
library, post office, bank),
maintenance (grocery store,
convenience store,
pharmacy), eating out
(bakery, pizza, ice cream,
take out), and leisure
(health club, bookstore,
bar, theater, video rental)
Northern
California
Yellow/white pages on
Internet
20
Commercial floor area
/43,560*commercial land
area
Gainesville FLProperty appraiser’s
database
15
Percentage of area for
different uses (e.g.,
residential, commercial,
industrial, special use, park,
water, parking lot, and
transportation)
Indianapolis INParcel-level data4
Percentage of total parcel
area in the following: major
land uses (commercial,
industrial, office, parks and
rec, residential, tax exempt,
vacant), night time uses,
social uses, retail uses,
industrial and auto-oriented
uses
Minneapolis–St.
Paul MN
Parcel-level data24
Percentage of total number
of parcels (accessible by
the street network) that are
residential
Buffalo-Niagara
Falls NY
Metropolitan Area
Parcel-level data9
Percentage of total
buildings that are
nonresidential
El Paso TXCity of El Paso Planning,
Research and
Development Dept
5
Gross employment density
(no. of employees per area)
Puget Sound,
WA;
Minneapolis–St.
Paul MN
Washington State
Department of Economic
Security, Puget Sound
Regional Council (area of
census tracts in acres),
Census, parcel-level data
2,6
Employment per unit land
area
Minneapolis–St.
Paul MN
Commercial data base,
parcel-level data
24
Retail employment per unit
land area
Minneapolis–St.
Paul MN
Commercial data base,
parcel-level data
24
Density of employees in
major retail subcategories:
general merchandise, food
stores, eating and drinking
places, miscellaneous retail
Minneapolis–St.
Paul MN
Commercial data base,
parcel-level data
24
Jobs densitySan Francisco Bay
Area CA
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use), MIN-UTP (travel
times)
16
Presence of shopping mallPortland ORRegional Land
Information System from
assessment and taxation
records
25
PatternDissimilarity index as a
function of the number of
actively developed hectares
in the tract and an indicator
for whether the central
active hectare’s use type
differs from that of a
neighboring hectare
San Francisco Bay
Area CA
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use)
16
Entropy index as a function
of the proportion of
developed land across six
land-use types (residential,
commercial, public, offices
and research sites,
industrial, and parks
recreation)
San Francisco Bay
Area CA;109 (2)
Minneapolis–St.
Paul MN;133 Puget
Sound111
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use),109 Parcel-level
data,133 King County
BALD file (parcel
data)111
2,16,24
Mean entropy as the
average of neighborhood
entropies computed for all
developed hectares within
each census tract, where
neighborhood is defined to
include all developed area
within 0.8 km of each
relevant active hectare
San Francisco Bay
Area CA
Census Transportation
Planning Package,
Association of Bay Area
Governments’ Land-use
File (hectare -level land
use)
16
Land-use diversity factor
(for both origin and
destination) comprised
measures of mixed use
entropy, employed
resident-to-jobs balance
index, resident-to-
retail/services balances
index, “residentialness”
index
San Francisco Bay
Area CA
Census
Association of Bay Area
Governments
26
Job-residents balance as a
function of the number of
jobs and residents in a TAZ
Gainesville FLGainesville built
environment database
15
Job mix as a function of the
number of commercial,
industrial, and service jobs
Gainesville FLGainesville built
environment database
15
Land-use mix defined as
evenness of distribution of
square footage of
residential, commercial,
and office development
(see equation in text)
Atlanta GA;
King County WA;
San Diego CA
Parcel-level land use
from County Tax
Assessors Data,
metropolitan planning
organization
8,10,12
Land-use mix comprised of
residential and commercial
building area
New York City
NY
Tax assessors data7
Proportion of dissimilar
land uses among grid cells
in an area
Minneapolis–St.
Paul MN
Parcel-level data24
Herfindahl-Hirschman
Index, HHI
Minneapolis–St.
Paul MN
Parcel-level data24
Access to
recreation
facilities
AccessibilityProportion of suburb area
allocated to public open
space
Melbourne,
Australia
Open Space 2002 spatial
dataset supplied by the
Australian Research
Centre for Urban Ecology
27
Distance to (network
and/or straight-line) nearest
facility (playgrounds,
parks, trail, gyms,
recreation centers)
Cincinnati OH;
Rockhampton,
Queensland;
Southeastern SC;
San Diego CA;
Seattle WA;
El Paso TX;
Arlington MA;
Minneapolis–St.
Paul MN;
San Antonio TX
Variety of data sources,
including: health
department inventory;143
Internet searches;
department of parks and
recreation;69metropolitan
planning organization,
yellow pages, web sites,
phone calls;137park layer,
Puget Sound Regional
Council’s regional
transportation network
data;138City of El Paso
Parks and Recreation
Dept, Center for
Environmental Resource
Management (schools),
Online yellow pages
listings (gyms);125and
parcel-level data133
5,13,18,19,28-31
Accessibility to public
open space (>2 acres)
based on gravity model
with adjustment for
attractiveness (based on
observational assessment),
distance, and size
Perth, Western
Australia
Ministry of Planning32,33
IntensityDensity of 48 types of
recreational facilities based
on kernel densities, simple
densities, densities adjusted
for population density.
Recreational facilities did
not include school,
churches, private facilities,
trails not in parks.
Stratified by type of facility
(e.g., related to team/dual
sports) and requirement of
facility user fees.
Forsyth County
NC
Baltimore County
MD
Manhattan and
Bronx boroughs
NY
Online yellow page and
Internet searches;
Departments of city
planning and recreation;
Other GIS units
34
No. of recreational
facilities (out of 169
facility types falling under
schools, public facilities,
youth organizations, parks,
YMCA, public fee
facilities, instruction,
outdoor, member, all
facilities)
U.S. (N=42,857
block groups)
Commercially purchased
set of digitized business
records using Standard
Industrial Classification
(SIC) codes
35
No. of for-fee indoor
exercise facilities,
categorized as private
(commercial, requiring
membership) or public
(owned/managed by local
authority/council, with pay
per session, membership,
or club usage), classified as
gym, sports hall, and/or
swimming pool
EnglandCommercial database36
No. of resources (parks,
gyms, recreation center,
and/or public school with
public access)
Southeastern SC
San Diego CA
Internet searches;
department of parks and
recreation; yellow pages;
metropolitan planning
organization, yellow
pages, web sites
28,30
No. of private (e.g., fitness
clubs, dance studios, skate
rinks) and public (parks,
schools) facilities
San Diego CAYellow page phone
books, phone calls, and
internet. Schools and
public parks obtained
from San Diego Assoc of
Governments
10
No. of recreation facilities
(parks, gyms, schools, and
biking/walking paths)
El Paso TXCity of El Paso Parks and
Recreation Dept, Center
for Environmental
Resource Management
(schools), Online yellow
pages listings (gyms)
5
No. of exercise facilities
(out of 385) that were
classified as either free
(public parks, sports fields,
public recreation centers,
colleges & universities,
public schools) or pay
(tennis/racquet clubs,
aerobic and dance studies,
membership swimming
pools, health or fitness
clubs, YMCAs and
YWCAs, and skating
rinks). Excluded bike and
walking trails, private
tennis courts, private
swimming pools
San Diego CATelephone classified
directory, local sports and
exercise publications and
other commonly available
sources
37
Amount of park area (in
hectares) accessible by the
street network
Buffalo-Niagara
Falls NY
Metropolitan Area
Unspecified9
Acres of parkSan Diego CAMetropolitan planning
organization
30
Presence of park and trailPortland ORRegional Land
Information System from
assessment and taxation
records
25
Percentage of total
residential area that is park
or non-park recreation area
(Park area included nature
trails, bike paths,
playgrounds, athletic fields,
and state, county, and
municipally owned parks.
Recreational area included
ice or roller skating rinks,
swimming pools, health
clubs, tennis courts, and
camping facilities.)
Erie County NYParcel land-use data from
NY State GIS
Clearinghouse
11
Square meters of green
space and recreational
space, including woods,
parks, sport grounds (not
gyms or fitness centers)*,
allotments where people
grow vegetables, and
grounds used for day trips,
e.g., zoo and amusement
parks
Maastricht, The
Netherlands
Existing GIS databases of
Statistics Netherlands on
land utilization including
the amount of green
space and recreational
space.
38
Street
pattern
IndicesComposite measure of
alpha, beta, and gamma
indices (measures of the
ratio of intersections to
street segments)
U.S.Street centerlines17
Composite measures of
block size (average of
street length, block area,
block perimeter)
U.S.Street centerlines17
Walkability score
comprised: negative of ave
block size; percentage of
all blocks having areas of
<0.01 square miles; no. of
3-, 4-, and 5-way
intersections divided by the
total no. of road miles.
U.S.Street centerlines (not
explicitly stated)
39
Pedestrian-/bike-friendly
design factor (for both
origin and destination)
comprised of square meters
per block within 1 mi
(average), proportion of
intersections that are 3-way
intersections, proportion of
intersections that are 4-way
intersections, proportion of
intersections that are 5-way
intersections, proportion of
intersections that dead ends
San Francisco Bay
Area CA
Street centerlines26
Street characteristics factor
(dichotomized as high or
low) comprised of the sum
of the following
dichotomized variables: no.
of road segments (link
count); ratio of road
segments to intersections
(link-node ratio); density of
≥3 way intersections;
census block density
Forsyth County
NC; Jackson, MS
Street centerlines40
Single
variables
No. of intersections with
≥4 roads
Melbourne,
Australia
Street centerlines27
Percentage of intersections
that are 4-way intersections
(connected node ratio)
10 largest
consolidated
metropolitan
statistical areas in
U.S.; El Paso TX;
Minneapolis–St.
Paul MN
Street centerlines5,14
Block length10 largest
consolidated
metropolitan
statistical areas in
U.S.
Street centerlines14
No. of intersections per
length of street network (in
feet or miles)
California;
Buffalo-Niagara
Falls NY
Metropolitan
Area;
Erie County NY
Street centerlines1,9,11
No. of intersections per
area
AtlantaGA;
King County WA;
New York City
NY;
El Paso TX;
Minneapolis–St.
Paul MN
Street centerlines5,7,8,10,12
No. of 4-way intersections
per area
Minneapolis–St.
Paul MN
Street centerlines24
Ratio between airline and
network distances to
specified destination(s)
(e.g., church, office)
Seattle WA;
Minneapolis–St.
Paul MN
County’s parcel-level and
building-level assessor’s,
Puget Sound Regional
Council’s regional
transportation network
data; street centerlines
13
Network segment average
length
Indianapolis INStreet centerlines4
Percentage of intersections
that are cul-de-sacs
El Paso TXStreet centerlines5
Average census block areaMinneapolis–St.
Paul MN
Street centerlines24
Median census block areaMinneapolis–St.
Paul MN
Street centerlines24
No. of access pointsMinneapolis–St.
Paul MN
Street centerlines24
Road length per unit areaMinneapolis–St.
Paul MN
Street centerlines24
Ratio of 3-way
intersections to all
intersections
Minneapolis–St.
Paul MN
Street centerlines24
Median perimeter of blockMinneapolis–St.
Paul MN
Street centerlines24
Street miles per square mileGainesville FLStreet centerlines15
Sidewalk
coverage
Sidewalk length divided by
road length
Minneapolis–St.
Paul MN;
Gainesville FL; El
Paso TX
Street centerlines;133
County’s bicycle and
pedestrian level-of-
service database;108Black
and white photos with 1 ft
resolution, acquired by
Surdex in 1996 and were
subsequently bought by
the Public Senate Board,
available free through the
PdNMapa Initiative
funded by Paso del
Norte125
15,24
Total length of sidewalks
within buffer
Seattle WAPuget Sound Reg’l
Council’s transportation
network
13
Percentage of shortest route
to closest bus stop with
sidewalk; Percentage of
shortest route to campus
with sidewalk
Chapel Hill NCOrthophotographic
images, NC Secretary of
State, Orange County
Land Records Office,
Chapel Hill Planning
Office, and Chapel Hill
Transit
3
Commute time difference
without and with taking
into account
walking/cycling paths
information
Chapel Hill NCOrthophotographic
images, NC Secretary of
State, Orange County
Land Records Office,
Chapel Hill Planning
Office, and Chapel Hill
Transit
3
Average sidewalk widthGainesville FLCounty’s bicycle and
pedestrian level-of-service database
15
Traffic
Indices
Traffic factor
(dichotomized as high or
low) comprised of the sum
of the following
dichotomized variables:
mean speed, maximum
speed, and majority speed
Forsyth County
NC; Jackson, MS
Posted speed limits from
the road network file
from Forsyth County Tax
Office and the Traffic
Engineering Division and
City Ordinance Book
from Jackson, MS
40
Volume factor
(dichotomized as high or
low) comprised of the sum
of the following
dichotomized variables:
maximum traffic volume,
mean traffic volume
Forsyth County
NC; Jackson, MS
Annual Average Daily
Traffic counts
(interpolated values for
roads without counts
using Spatial Analyst)
40
Single
variable
Distance (network and/or
straight-line) to nearest
busy street (e.g., ≥60 kph)
Rockhampton,
Queensland
Unspecified18
Mean traffic volume within
buffer
Seattle WAPuget Sound Regional
Council’s transportation
network
13
No. of crashes involving a
pedestrian or bicyclist per
population for 10-year
period 1993-2002
Forsyth County
NC
University of North
Carolina Highway Safety
Research Center
40
Street width (excluding
sidewalk), likely to affect
the volume of traffic and
incidents of accidents
Erie County NYStreet centerlines
(TeleAtlas)
11
Busy street barrier, defined
as present where at least
one of the four busiest
streets in Arlington MA
would have to be crossed to
access the Minuteman
Bikeway
Arlington MAStreet centerlines31
CrimeNo. of serious crimes per
1,000 residents per year
Cincinnati OHPolice department’s
website
19
No. of emergency police
calls per 1,000 residents
per year
Cincinnati OHPolice department’s
website
19
No. of crimes per 100,000
people (includes both
violent and property
crimes)
U.S.Federal Bureau of
Investigation
39,41
No. of violent crimesSan AntonioPolice blotters published
daily in a San Antonio
newspaper
29
Other
SlopeMean slope within bufferSeattle WAUnspecified13
Any 100 m road segment
with ≥8% slope
Forsyth County
NC; Jackson MS
Digital Elevation Models
from United States
Geological Survey
42
Commute time difference
without and with taking
into account slope
information
Chapel Hill NCOrthophotographic
images, NC Secretary of
State, Orange County
Land Records Office,
Chapel Hill Planning
Office, and Chapel Hill
Transit
3
Average change in
elevation (in ft) in a
subject’s neighborhood.
Calculated by subtracting
the lowest elevation point
from the highest elevation
point.
El Paso TXPurchased from Topo
Depot
(www.topodepot.com)
5
Slope of ≥10% for a
continuous distance of
≥100 m along shortest
route from home to
Minuteman Bikeway
Arlington MAGIS elevation data31
Greenness /
vegetation
Normalized difference
vegetation index (NDVI)
For Tilt 2007, calculated
mean of the NDVI values
within a circle with the
same area as the average
walkable area defined by
GIS Network Analysis (0.4
mi walking distance of
residential parcels)
Indianapolis, IN;
Seattle WA
Biophysical remote
sensing techniques and
multispectral imagery
acquired by the Landsat
Thematic Mapper Plus
(ETM+) remote sensing
system.; Dataset acquired
from Landsat 5 and
process for geo-
registration, instrument
calibration, atmosphere
correction, and
topographic correction by
the Urban Ecology
Research Laboratory at
the University of WA
4,22
Coastal
location
Coastal suburb (Y/N)Melbourne,
Australia
--27
DogsNo. of registered dogsRockhampton,
Queensland
Unspecified18
Street
lighting
Amount of roadway within
20 m of streetlight
Rockhampton,
Queensland
City Council from State’s
electrical supplier
18
Street lights per length of
road
Minneapolis–St.
Paul MN
Aerial photos24
TreesPercentage of street miles
with trees
Gainesville FLCounty’s bicycle and
pedestrian level-of-
service database
15
Total no. of street trees
within buffer
Seattle WAUnspecified13
Street trees (trees within an
certain distance buffer) per
length of road
Minneapolis–St.
Paul MN
Aerial photos24
TransitNo. of bus stops and
subway stations per square
kilometer
New York City
NY
New York City Dept. of
City Planning
7
Distance to nearest transit
stop
Minneapolis–St.
Paul MN
Street centerlines24
Transit stop densityMinneapolis–St.
Paul MN
Street centerlines24
Regional
accessibility
Accessibility index as a
function of (1) the number
of trip attractions in a
specified zone for the
particular trip purpose and
(2) interzonal friction
factor for particular trip
purpose
Gainesville FLUnspecified15
Regional accessibility
using total retail
employment and gravity
model calculation
Central Puget
Sound
metropolitan area
WA
Employment data from
Washington State
43
Bike paths
and shoulders
Distance to on-street and
off-street bike paths
Minneapolis–St.
Paul MN
Minnesota Department of
transportation
21
Length of bike path and
paved shoulders divided by
road length
Gainesville FLCounty’s bicycle and
pedestrian level-of-
service database
15
Neighborhoo
d themes /
patterns
Used cluster analysis to
identify patterns of
environmental
characteristics and to
specify homogeneous, non-
overlapping clusters of
neighborhoods sharing
various meaningful
characteristics. Major
neighborhood types: (1)
rural working class; (2)
exurban; (3) new suburban
developments; (4) older,
upper-middle class
suburbia with highway
access; (5) mixed-
race/ethnicity urban; (6)
low SES, inner city. GIS
variables included four
measures of street
connectivity, one measure
of access to recreational
facilities, two measures of
road type, and one measure
of crime
U.S.Street centerlines (street
connectivity),
commercially purchased
set of digitized business
records using SIC codes
(recreational facilities),
Census feature class
roads (road types), U.S.
Federal Bureau of
Investigation Uniform
Crime Reporting county-
level data from the
National Archive of
Criminal Justice Data
44
Used cluster analysis to
identify neighborhood
themes consisting of (1)
planned unit development;
(2) traditional
neighborhood
development; and (3)
mixed
Orange County
CA
Land-use database from
Orange County
Administration Office,
Census TIGER files
45
Home ageMedian year home builtSouthwestern PACensus14,23
Composite
variables
Neighborhoo
d
accessibility
Comprised: (1) density; (2)
no. of employees for
specific neighborhood
retail businesses; (3) block
area
Central Puget
Sound
metropolitan area
WA
Census, employment data
from Washington State
43
Neighborhoo
d walkability
index
Comprised of land-use
mix, residential density,
and intersection density
Atlanta GA;
King County WA;
San Diego CA
Census, regional land
cover data from aerial
images, street centerlines,
parcel-level land-use data
8,10,20,30
Walkability
score
Comprised of eight
variables related to
proximity/density of
grocery stores and other
retail destinations,
educational parcels, office
mixed use complexes, and
block size.
King County WAAssessor’s files (parcel),
park information, streets,
foot/ bike trails, land
slope, vehicular traffic,
public transit
46
Intensity
factor
Comprised: retail store
density, activity center
density, retail intensity,
walking accessibility, park
intensity, and population
density
San Francisco Bay
Area CA
Census; Census
Transportation Planning
Package; Association of
Bay Area Governments
47
Walking
quality factor
Comprised: sidewalk
provisions, street light
provisions, block length,
planted strips, lighting
distance, flat terrain
San Francisco Bay
Area CA
Census; Census
Transportation Planning
Package; Association of
Bay Area Governments.
Some indicators from
field inventories
47
Sprawl
indices
Comprised: residential
density (7 variables), land-
use mix (6 variables),
degree of centering (6
variables), street
accessibility (3 variables)
U.S. counties
(448) and
metropolitan areas
(83)
Census, U.S. Department
of Agriculture Natural
Resources Inventory
41,48
Comprised: percentage of
total population in low
density (>200 and <3500
persons per square mile)
and high density (≥3500
persons per square mile)
census tracts
330 U.S.
metropolitan areas
Census49
Comprised: proportion of
census metropolitan area
(CMA) dwellings that are
single or detached units,
dwelling density, and
percentage of CMA
population living in the
urban core
CanadaCanadian Census of
Population
50
*Typically derived from tax assessors records though also used for land-use planning.

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Footnotes

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