An Assessment of GIS-Enabled Walkability Audits

Abstract

Introduction Research on walking and the built environment is a fairly recent area of inquiry, accelerated over the past ten years by an increased interest in the relationship between urban form and public health. As the research has progressed, so has the interest in developing ways to collect data at a very fine scale--in essence, to be able to collect data at the streetscape level and link this data to active transportation behavior (Schlossberg 2007). However, the lack of quick and cost-effective tools for collecting block-by-block data about the streetscape has prevented more widespread research and application of such tools. This paper discusses the development and implementation of a GIS-based pedestrian audit tool that allows users to collect data in electronic form using a handheld computer (i.e., a pocket PC or personal digital assistant (PDA)). In a recent article on visualizing and measuring walkability, Schlossberg suggested looking forward to new technological approaches. In theory, tools that could allow for detailed, GIS-enabled data collection about pedestrian environments on handheld computers would allow planners to better understand the relationship between specific characteristics of the built environment and their relationship to either overall walking within an area or preferences for walking along one route or another. Once this relationship between the walking environment and walking behavior could be established, then specific recommendations to improve walking conditions could be made to policy makers, planners, transportation officials, and other decision makers could be made to improve conditions for walking. This paper discusses the development and implementation of such a pedestrian audit tool and evaluates its promise for use in future projects. The larger project within which this tool was developed examined correlations between aspects of the built environment with the actual route choices that people make when walking from home to their nearest transit stations. The variables within this GIS-based audit tool were adapted from existing research literature and from other pedestrian audit tools, namely the Pedestrian Environment Data Scan (PEDS) developed by Dr. Kelly Clifton and Andrea Livi at the University of Maryland and Dr. Daniel Rodriguez at the University of North Carolina. This paper, however, will not focus on the types of data collected; rather, what follows is an evaluation of using this technology as a way to gather more detailed and nuanced data about walkable environments. CONCEPTUAL BACKGROUND As researchers, practitioners, and policy makers have become increasingly interested in the relationship between the built environment and physical activity, recognition of tools that appropriately measure urban form at a pedestrian scale has also increased. Perhaps the best-known and utilized tool in this area is an environmental audit instrument called SPACES, a comprehensive tool that helps inventory the characteristics of and along a roadway segment (Pikora, Giles-Corti et al. 2003). The authors categorize different factors of a walking environment into five classifications: (1) functional (physical attributes of the street), (2) safety (characteristics of a safe environment), (3) aesthetic (elements such as trees or gardens), (4) destination (relationship of neighborhood services to residences), and (5) subjective. Moudon and Lee (2003) focused their work in a similar area, but dedicate more time to developing a conceptual framework for measuring walkability to help direct future research efforts. To develop their framework, they performed an exhaustive review of more than 30 published methodologies and inventorying tools that have been developed to assess walkability. They outlined a theoretical framework called the Behavioral Model of Environments (BME) that seeks to account for personal, physical, and internal responses factors that may explain the connection between individual pedestrians and their walking environments. …

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.