Current Issue

In recent years, the relationship between the characteristics of the built environment and walking has gained attention from various disciplines. The accumulated evidence suggests that the built environment is associated with physical activity. As a result, there is a corresponding need to measure the quality of the built environment for walking and identify built environmental determinants that can potentially create a walkable built environment. This need has gained growing interest among researchers, practitioners, and planners who aim to establish strategies targeting walking to increase physical activity (Moudon and Lee, 2003). A proliferation of methods has been incorporated into neighborhood research to measure a neighborhood’s walkability, and each empirical study varies its measuring approach because researchers have different built environmental determinants that affect walkability measurements (Clifton et al., 2006).

Neighborhood research has developed a variety of measuring methods, each with its own strengths and limitations (Schaefer-McDaniel et al., 2010). Surveys, GIS, and audits are some of the approaches used, but there is no consensus among researchers on the most appropriate method (Lee and Talen, 2014). With the proliferation of walkability measurements, it has become increasingly difficult for researchers to choose suitable measuring methods and tools for their empirical studies. Given the various walkability measuring methodologies available, this paper focuses on the audit methodology, which assesses the pedestrian safety, accessibility, and comfort of a neighborhood’s built environment via observation by raters.

Several earlier studies have reviewed the methods of walkability audit tools to understand the various types of walkability measurements. Nickelson et al. (2013) identified 31 audit instruments and thoroughly examined the domains and subdomains associated with each instrument. Similarly, Schaefer-McDaniel et al. (2010) reviewed 51 empirical studies that involved neighborhood observations, focusing on methodological rigor, geographical boundaries, and the relationship between neighborhood observations and the health of residents. Moudon and Lee (2003) reviewed built environment variables of existing audit instruments used to measure the bikeability and walkability of environments. In addition, Rzotkiewicz et al. (2018) systematically reviewed 51 English-language studies to analyze the current use of Google Street View (GSV) in health research, characterizing major themes, strengths, and weaknesses. Existing literature reviews inventory the fundamental information to aid auditors’ selection and development process of audit instruments. (Nickelson et al., 2013; Schaefer-McDaniel et al., 2010). Despite existing research efforts, there is a scarcity of reviews dedicated to the in-depth interpretation of walkability audits, especially their diverse objectives, unit of analysis, and user perspectives, to highlight the development trend of audit instruments. Additionally, several reviews conducted approximately a decade ago, which focused on neighborhood audit tools, lack the inclusion of up-to-date information regarding the latest tools available for assessing neighborhood walkability. Therefore, there is a critical need for a comprehensive review that thoroughly examines both in-person and virtual audits, elucidating the transition from traditional in-person methodologies to virtual alternatives within the context of walkability literature. With these backgrounds, this study aims to fill the gap in the literature on neighborhood audit instruments by providing a critical review of the current state of research on walkability audit tools, specifically those available for quantifying the walkability of neighborhood pedestrian environments.

To this aim, this paper identified commonly used walkability audit instruments and analyzed the characteristics of each audit tool to provide an essential overview of the key factors of walkability audits. We first analyzed the trend of walkability measurement research by documenting the flow of research in measuring the walkability of a neighborhood. Subsequently, we conducted a thorough review of 65 studies and 20 tools, examining data collection methods, geographical scale, purpose, measured domains, and the primary users of each audit instrument. By providing the inventory of available neighborhood audit tools, this study is expected to aid researchers in understanding the nature of each audit tool and finding adequate tools for their neighborhood walkability studies.

The development of walkability audit tools is underpinned by a conceptual framework that addresses the challenge of translating the quality of the pedestrian environment into quantifiable measures suitable for empirical research (Forsyth et al., 2006). As a walkability audit instrument is defined as a tool used to inventory or assess the physical environmental conditions associated with walking, its main objective is to measure walkability by analyzing the quality of pedestrian environment features and aggregating them into a singular walkability index or a composite score that encompass various factors of walkability (Baobied et al., 2021; Moudon and Lee, 2003). The fundamental purpose of the walkability audit tools is to provide a set of quantitative measures that practitioners or community members can use to assess the suitability of the neighborhood environment for walking. (Moudon and Lee, 2003).

Based on this conceptual framework, the early developed walkability audit instruments were conducted by in-person observation, in which a person observes the street segment by direct observation. Later, the “remote” approaches that can measure built environment variables using GIS or aerial photos have been incorporated into audit instruments (Brownson et al., 2009). These walkability audit instruments that can remotely measure built environments, are alternatively called as virtual audit tools (Steinmetz-Wood et al., 2019).

The process of walkability audit instruments unfolds as follows: selecting sites, defining and sampling street segments within sites, training for auditors, collecting data, entering data, and synthesizing variables from the raw data to compute the walkability score (Brownson et al., 2009). The in-person and virtual audit procedures are similar, except for the data collection method. When using the in-person audit instruments, the auditors collect data by walking through sampled street segments and entering data directly into a Tablet PC or paper version of audit instruments (Day et al., 2006). Whereas when using the virtual audit tools, the auditors locate the street segments using the QGIS map, which allows the auditors to navigate through streets to view GSV images (Steinmetz-Wood et al., 2019). The detailed processes of the in-person and virtual audits are further explained in Chapter Ⅲ.

The primary objective of walkability audits in <Table 1> is to measure the walkability of different population groups. The audit tools are often tailored to measure the specific walking experience of different types of pedestrians, as environmental features that affect walking vary among them due to differences in their physical activity level. For instance, several audit tools have been developed to measure the built environment features associated with older adults’ physical activity (Michael et al., 2009). These specific audit instruments are crucial in measuring the environmental obstacles for older adults in the neighborhood, which is essential for planning livable communities and enhancing the quality of life for seniors (Cunningham et al., 2005). These audits address the specific considerations of senior pedestrians, such as their sensitivity to loud noises and bright lights, cognitive ability and memory, slower walking pace, and susceptibility to steep inclines (World Health Organization, 2023). Senior specific audit tools measure the domains that potentially affect seniors’ walking, such as signage accessibility, traffic and pedestrian signal timing lengths, and crosswalk lengths (Cunningham et al., 2005). The purpose of each audit instrument is explained in the columns of <Table 1>.

Different intended users can also explain the purpose of walkability audit instruments. For instance, Moudon and Lee (2003) explained the audits into categories based on user groups. The first category is the audits designed to assess the built environment for research purposes. Audits for research usually inventory the environmental features that need to be observed during the data collection phase (Moudon and Lee, 2003). The audits are often called checklists because they provide an index of environmental variables for walkability assessment (Moudon and Lee, 2003). The primary purpose of the audits for the first category is to provide a standardized protocol for empirical research. The second category is the audits for transportation engineers, who measure the safety of road segments and rank the roads based on the pedestrians’ perception of safety and comfort (Moudon and Lee, 2003). The audits from the third category are for the planners and policymakers, who identify the pedestrian concentrated area and ensure pedestrians’ safety and comfort (Moudon and Lee, 2003). Lastly, the audit, such as the AARP walk audit, is intended to be used by the community members. The audits for community members are often accessible online and can be easily conducted by non-professionals. The training session is minimal compared to the other categories’ audits, and the protocols are relatively simplified. The intended users of each audit instrument are specified in the columns of <Table 1>.

The data collection methods of walkability audit tools in <Table 1> have gone through various stages. Primary data collection methods used in walkability audit tools include collecting data in-person and using virtual methods such as Google Street View. Several audits in <Table 1> use in-person data collection methods, where the auditors characterize the quality of walkability by directly observing the built environmental features using a checklist while walking through the neighborhood (Pikora et al., 2002; Clifton et al., 2006; Day et al., 2006; Cunningham et al., 2005). The data collected in-person refers to the data collected by the auditors who visit the streets and directly observe the street infrastructure and conditions. This process begins with preparing the auditors by familiarizing them with the walking route and the audit instructions based on a manual (Pikora et al., 2003). In-person audits can be carried out by local participants, working with a trained coordinator who consults with them throughout the audit process. The in-person audit methods generally require tools such as a pen or pencil to complete the responses, a personal digital assistant (PDA) recording device, and a pedometer (National Transport Authority, 2021). The audit session requires a minimum 30 minutes of walking usually with a team of two auditors for each audit session (Emery et al., 2003). While observing the street, the auditors record the responses for each question on the audit form asking about the quality or presence of environmental attributes. The auditors then utilize a rating scale such as 1 to 10 to evaluate the overall quality of each environmental feature. The rating numbers are then combined to derive a composite score that indicates the overall quality of the environment’s walkability (Lee and Talen, 2014; Partnership for a Walkable America, 2023). Additionally, in-person audit tools often incorporate qualitative measurements of the walking environment, allowing auditors to provide subjective assessments of their satisfaction with the environment (Pikora et al., 2003). The early auditing process relied heavily on manual input, which involved direct observation of a given neighborhood area or a street segment.

The use of virtual auditing methods has revolutionized the process of conducting audits by allowing researchers to perform audits at their desk (Steinmetz-Wood et al., 2019). Virtual audits generally employ more intricate protocols than in-person audits, including different interfaces to improve the inter-reliability of the audit results. For example, the Computer Assisted Neighborhood Visual Assessment System (CANVAS) classified the process according to the different interfaces, such as the ‘study deployment interface’ and the ‘analytics interface’ to improve inter-reliability of the audit results (Bader et al., 2015). Researchers are able to specify the street locations to be audited, audit items, and assign the street segments to auditors using the ‘study deployment interface’ (Bader et al., 2015). Locations can be either identified in ‘manual-confirm’ mode or ‘auto-select’ mode, where the virtual audit program displays the map with nearest viewable streets by issuing calls to Google’s API. After designating the street segments, auditors select items to rate from the existing audit form or adjust the items in the list. Several virtual audits include the ‘analytics’ interface, in which study manager maintains the data of auditor’s progress, inter-rater kappa statistics, and rating times for each audit session. This interface is particularly useful in handling data in the future, as these data can be downloaded in text files and imported into statistical software for further analysis (Bader et al., 2015). In addition, the virtual audit with different interfaces allows researchers to modify audit protocols to their specific research needs without deviating from the audit protocol (Bader et al., 2015). Therefore, the introduction of virtual audits using GSV has brought promising advantage for expediting the audit procedure, by enabling researchers to audit remotely, which dramatically reduced the time and cost of visiting street segments. The data collection method of each audit instrument is described in the columns of <Table 1>.

The audits summarized in <Table 1> employ street segments as the fundamental unit of analysis. To provide clarity, the audits operationalize a street segment as the “section of street or road between two intersections within each street” (Pikora, 2002). Within this context, a street or road encompasses one or more segments. For instance, segments A-B and C-D are treated as distinct entities, necessitating separate evaluations (Figure 2). Each segment is uniquely identified and assigned an ID number prior to data collection. The length of each street segment exhibits variability. According to WSAF, a typical street segment is generally under 2 miles in length, while SLU specifies a range of 1/4 mile or less. Depending on the audit protocol, observers may be tasked with assessing either one or both sides of a segment. For example, PEDS requires auditors to evaluate both sides of the segment for low-volume roads. On the other hand, for high-volume roads, auditors are directed to focus on the side with the greater traffic volume. Auditors are responsible for observing and recording various attributes of each street segment on an audit form. These attributes encompass elements such as the slope, number of intersections, types of pedestrian facilities, and materials used for pathways. In some cases, a segment may be represented by a cul-de-sac or another street variant lacking two intersections within 1/4 mile radius from each other (Prevention Research Center, 2020). It is pertinent to note that in the IMI protocol, a street segment is alternatively referred to as a “block face”. As defined in the IMI instrument codebook, a “block face,” encompasses both sides of a street (Day et al., 2006).

Figure 2. 
Definition of a street segment

The sampling protocols differ across various studies. Some studies employed random segment sampling, while others conducted observations for entire neighborhoods. The IMI protocol suggests a sampling range of around 15-20 blocks within a neighborhood consisting of 60-80 blocks (Day et al., 2006). For instance, 36 segments were randomly selected to assess the inter-rater reliability of SWEAT (Cunningham et al., 2005). In contrast, all segments within a neighborhood were evaluated to ensure the inclusion of a significant number of segments with sidewalks, a crucial attribute for evaluating the reliability of PEDS (Clifton et al., 2006). It is important to note that this study recognizes several limitations. The small sample size may have an impact on limited variability and result in diminished reliability (Clifton et al., 2006). However, when dealing with neighborhoods characterized by homogeneous street attributes, sampling specific streets can significantly reduce observation time while providing a sufficient characterization of neighborhood walkability. To address the question of whether a sample size less than 100% of street segments adequately represents the pedestrian-built environment of a neighborhood, McMillan et al (2010) conducted a comparison of audit results using PEDS across eleven neighborhoods in Houston TX. Their findings indicated that the sampling 25% of residential street segments within a 400m radius of a neighborhood sufficiently represented the pedestrian-built environment (McMillan et al., 2010).

The audits in <Table 1> measure diverse environmental features because there are no universal measures that can capture all significant factors of walkability due to the variety of built environments (Baobied et al., 2021). However, researchers have developed categorical terms to classify the measured attributes based on the scale of the measured attributes. Macroscale features refer to the wider pedestrian network and urban scale, including pedestrian network connectivity, land-use mix, and population density (Shields et al., 2021). Microscale elements refer to environmental features that potentially influence walking behavior on a human scale, such as the presence and conditions of sidewalks, benches, trees, and the level of crime (Shields et al., 2021). Several GIS-based audits, such as Walkability Index, consider macroscale elements in measuring neighborhood walkability. Audits that mainly assess macroscale components rely on the understanding of walkability as a quality that derives from urban-scale characteristics (Shields et al., 2021). However, walkability audits have increasingly measured microscale environmental elements based on accumulating evidence that such factors influence walking behavior in the neighborhood environment (Sallis et al., 2022). This consideration of microscale audits reflects an interest in creating street environments and neighborhoods that increase walking by enhancing pedestrian environments at the human scale (Shields et al., 2021).

The definition of neighborhood boundaries varies across studies, often being described as the spatial extent of the study area. Establishing the neighborhood within circular boundaries presents several advantages. First, circular boundaries encompass all areas to which a resident may be exposed during daily travels, whether on foot or by automobile (McMillan et al., 2010). Second, the use of straight-line distance enables the inclusion of foot travel distances and “shortcut” routes that may not be accounted for when employing a street network or aerial satellite photography strategy (McMillan et al., 2010). One approach to determining neighborhood boundaries involve specifying a radius distance from residents’ homes. For example, streets were selected within a 400-meter radius of each participant’s residence to test the reliability of the SPACES instrument (Pikora et al., 2002). This 400-meter radius is chosen based on the walkable neighborhood boundaries defined by the Western Australian Planning Commission (Pikora et al., 2002). Similarly, Lee et al. (2008) established a neighborhood buffer using a 0.25-mile radius from the school’s location as the epicenter, approximately equating to a 5-min walk. In a different context, Mraz (2005) assessed street connectivity in suburban Tucson, AZ, considering the distance from the neighborhood center to a point on its perimeter, up to a distance of two miles and its bikeability. It is noteworthy that these studies align with the prevailing urbanist concept of the walkable neighborhood boundary, often approximated as a 1/4 mile radius, estimated as the distance an individual could walk in about 5 minutes (Pikora et al., 2002).

Census tracts serve as widely adopted delineations for defining neighborhood boundaries, particularly in studies encompassing the United States and Canada. These tracts constitute compact, relatively stable geographic zones within census metropolitan areas, with an average population typically around 4000 individuals (Statistics Canada, 2018). To illustrate, Chaudhury et al. (2011) used census tracts (CTs) to identify neighborhoods consisting of individuals aged 65 and older, constituting at least 13% of the total population in Portland and Metro Vancouver. In a similar vein, Brownson et al. (2004) used 2000 US Census Bureau data to assess the number of households and the percentage of population below poverty line in 1999. Drawing from this data, they selected both higher and lower-income neighborhoods in St. Louis, MO, representing a “low walkable” neighborhood, and Savanna, GA, representing a “high walkable” neighborhood. These examples underscore the applicability of census tracts as neighborhood boundaries, particularly in studies evaluating walkability based on diverse socio-economic characteristics. CTs offer a consistent, nationwide standard for delineation and furnish socio-economic insights within each census tract (Chaudhury et al., 2011). Analogous to the concept of the Census Tracts, in areas beyond North American regions, studies frequently adopt their country’s standardized administrative boundaries. As an illustration, research conducted in the Netherlands employs the nation’s administrative division boundaries.

Some studies establish unique neighborhood boundaries tailored to their research objectives. For example, Boarnet et al. (2011) employed a distinctive approach, sampling 716 subjects from 36 square focus areas measuring half a mile by half a mile (805×805 m). From these square focus areas, street segments were selected. Additionally, street segments were chosen from square study areas measuring one mile by one mile (1,609×1,609 m) (Boarnet et al., 2011). In another study, street segments were carefully selected from four distinct “zones” encompassing the “Town Center,” Thoroughfare,” “Neighborhood Cluster,” and “Isolated School.” These “zones” were conceived to encompass all accessible streets outside of the town center, including areas adjacent to schools located on the town’s periphery or along highways, remote residential zones, and thoroughfares (Yousefian et al., 2010). <Table 2> summarizes the definition of neighborhood boundaries of the reviewed studies and audit instruments.

Many walkability audits employ a hierarchical system to organize physical environmental items and clearly state the factors of walkability they intend to measure. One such framework is the system of domains, and subdomains, where domains refer to the prominent categories consisting of minor environmental attributes. In line with the commonly measured domains identified by Nickelson et al. (2013) the most frequently measured domains were Functionality, Safety, Aesthetics and Land Uses. The Functionality domain includes subdomains, such as ‘walking/cycling surface,’ ‘streets,’ and ‘traffic’ (Pikora et al., 2003), while the Safety domain comprise components such as stop signs that potentially enhance the perception of safety (Chaudhury et al., 2011). The Aesthetics domain assesses the visual appeal and the quality of the streetscape (Cunningham et al., 2005; Chaudhury et al., 2011), while the Land Uses domain measures the functional usage of the land in the street segments (Nickelson et al., 2013). The categorical terms used to describe the domains may vary among the walkability audits, as each researcher who develops the audits considers different factors when capturing neighborhood walkability. Another example is the system of features, elements, and items where features align with the same hierarchical definition as domains, elements align with subdomains, and items align with individual built environmental factors (Pikora et al., 2003). Although audits may use different terminology to describe the attributes, they all describe the same concept of the built environment factors that correlate to walkability (Nickelson et al., 2013). <Table 3> summarizes the differences in measured items of the audits listed in <Table 1>, based on the six categories and attributes.