Evaluating Walkability Using Walk Score

Walk Score^® (www.walkscore.com) is a publicly available large-scale method for calculating walkability. Walk Score was developed by Front Seat Management (www.frontseat.org), a software development company based in Seattle, WA, which focuses on software with civic applications. Walk Score uses publicly available data to assign a score to a location based on the distance to and variety of nearby commercial and public frequently-visited facilities. Data sources used by Walk Score include Google, Education.com, Open Street Map and Localeze. Facilities are divided into five categories: educational (e.g., schools), retail (e.g., grocery, drug, convenience and bookstores), food (e.g., restaurants), recreational (e.g., parks and gyms) and entertainment (e.g., movie theaters). The Walk Score algorithm then calculates the distance to the closest of each of the five facilities, using straight-line distances, and calculates a linear combination of these distances weighted both by facility type priority and a distance decay function [29 ]. The result is normalized to fit a 0 to 100 scale, with 0 being the lowest (lowest walkability/car dependent) and 100 being the highest (most walkable). If one of each of the five facilities is within a quarter-mile radius from the input location, that location receives a perfect 100 score. If no facilities are within a one-mile radius of the input location, that location will be assigned a score of zero. The location can be entered as geographic coordinates, or as an address which is then geolocated using Google Geolocation [30 ]. Front Seat provides an application programming interface (API), which can be used to query the Walk Score database through URL calls, eliminating the need to use the website interface [31 ]. In order to use the Walk Score API, the user must first obtain a key number, which can be requested on the Walk Score website. This unique key is used in all API calls, and has a limit on the number of uses per 24 hours. Using a scripting language, the user is able to paste a set of geographic coordinates along with the key number into an API call to quickly retrieve a Walk Score for each location. For this study, a program was created within the R programming language (R Foundation for Statistical Computing, Vienna, Austria), which queries the Walk Score database for each address used in the study. The script then scans the API response, which is in the form of an HTML page, and extracts the corresponding Walk Score. We obtained the walkability scores from Walk Score in mid May of 2011 using the Walk Score API and the geographic coordinates.