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The Walkable Accessibility Score (WAS): A spatially granular open-source measure of walkability for the continental US from 1997 to 2019

Author

Listed:
  • Kevin Credit
  • Irene Farah
  • Emily Talen
  • Luc Anselin
  • Hassan Ghomrawi

Abstract

This paper describes a straightforward method for calculating an open-source Walkable Accessibility Score (WAS) that measures walkability at the block group scale based on walking distance to business establishments, schools, and parks. Exploratory analysis of the WAS reveals high concentrations of walkable accessibility in the centres of the densest and/or largest cities. Our optimised specification ( decay = 0.008, upper = 800, k = 30) performs very well, achieving a Spearman rank correlation of 0.912 with proprietary Walk Score® values (for 2011). We provided pre-calculated data for each year from 1997 to 2019 and Python code for calculating the WAS at the project’s GitHub repository . The method is particularly useful in that it uses simple Euclidean distance calculations, and thus can be run at scale on a laptop or personal computer.

Suggested Citation

  • Kevin Credit & Irene Farah & Emily Talen & Luc Anselin & Hassan Ghomrawi, 2026. "The Walkable Accessibility Score (WAS): A spatially granular open-source measure of walkability for the continental US from 1997 to 2019," Environment and Planning B, , vol. 53(3), pages 494-506, March.
    • Handle: RePEc:sae:envirb:v:53:y:2026:i:3:p:494-506
    DOI: 10.1177/23998083251377116
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    References listed on IDEAS

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