Calculating Walk Scores with Python

Ablajan Sulaiman
10 min readJun 12, 2020

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…

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Ablajan Sulaiman
Ablajan Sulaiman

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