Development and validation of automated microscale walkability audit method

Bon Woo Koo, Subhrajit Guhathakurta, Nisha Botchwey

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Measuring microscale factors of walkability has been labor-intensive and expensive. To reduce the cost, various efforts have been made including virtual audits (i.e., manual audits using street view images) and the introduction of computer vision techniques. Although studies have shown that virtual audits (i.e., manual audits using street view images) can reliably replicate in-person audits, they are still prohibitively expensive to be applied to a large geographic area. Past studies used computer vision techniques to help automate the audit process, but off-the-shelf models cannot detect some of the important microscale walkability characteristics, falling short of fully capturing the multi-facetted concept of walkability. This study is one of the earliest attempts to use the combination of custom-trained computer vision models, geographic information systems, and street view images to automatically audit a complete set of items of a validated microscale walkability audit tool. This study validates the reliability of the automated audit with virtual audit results. The automated audit results show high reliability, indicating automated audit can be a highly scalable and reliable replacement of virtual audit.

Original languageEnglish (US)
Article number102733
JournalHealth and Place
Volume73
DOIs
StatePublished - Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Automated audit
  • Computer vision
  • Google Street View images
  • Reliability

PubMed: MeSH publication types

  • Journal Article

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