Introduction
Walkability has been a major focus of urban analytics. Walkable neighborhoods are directly correlated with better health, a lighter environmental impact, cost-efficient living, and an overall better quality of life. It can be a measure to see how friendly an area is for walking by looking at what is around an area and how easy it is to access amenities such as school, hospital, library, stops, stations, arena, supermarket, stores within a specific walking time. Given the ever-growing population and the environmental and economic challenges in modern cities, measuring walk scores is essential for creating a more livable, healthy, and sustainable environment, and boosting economic growth.
Walkability Measures
Measures of walkability have improved substantially over the past decade. Currently, the most widely used measure is Walk Score by Redfin (2007), which assigns scores between 0 and 100. Walk Score measures the walkability of any address, Transit Score measures access to public transit, and Bike Score measures whether a location is good for biking. Numerous other walkability indexes and methods have been developed in health, transport and urban studies (Jiang and Claramunt, 2004; Pikora et al., 2003; Vargo et al. 2009; Ewing, R.; Handy, S. 2009; Maghelal and Capp, 2011; Duncan 2011; Razmik 2014; Lee, S.; Talen, E. 2014; Geraint Ellis, 2015; Migue et al. 2016; Stockton et al., 2016; Mohammad and Taheri, 2017; Dhanani et al., 2017; Moura et al. 2017; Yen et al (2017); Liyin 2017; Allison (2018); Ivan et al., 2019). Matthew (2019) explained measuring walk times from every address point within the City of Toronto limits to the closest Toronto Transit Commission (TTC) stops. The analysis used open-source python libraries such as NetworkX, Pandana to perform the network distance calculations on a new open data set called the “Pedestrian Network” to better understand walkable access to various amenities across Toronto. It includes data sources, creating Pandana pedestrian network, and calculating pedestrian walk times of 24 different types of amenities such as schools, libraries, hospitals, supermarkets, TTC stops, and convenience stores. The basic approach of this study is first to create a pedestrian network as a connected set of links and nodes, then calculate walking distance and…
