Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of Google Street View images

Shohei Nagata, Tomoki Nakaya, Tomoya Hanibuchi, Shiho Amagasa, Hiroyuki Kikuchi, Shigeru Inoue

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Although the pedestrian-friendly qualities of streetscapes promote walking, quantitative understanding of streetscape functionality remains insufficient. This study proposed a novel automated method to assess streetscape walkability (SW) using semantic segmentation and statistical modeling on Google Street View images. Using compositions of segmented streetscape elements, such as buildings and street trees, a regression-style model was built to predict SW, scored using a human-based auditing method. Older female active leisure walkers living in Bunkyo Ward, Tokyo, are associated with SW scores estimated by the model (OR = 3.783; 95% CI = 1.459 to 10.409), but male walkers are not.

Original languageEnglish
Article number102428
JournalHealth and Place
Volume66
DOIs
Publication statusPublished - 2020 Nov

Keywords

  • Deep learning
  • Google street view
  • Neighborhood walkability
  • Semantic segmentation
  • Walking behavior

ASJC Scopus subject areas

  • Health(social science)
  • Sociology and Political Science
  • Life-span and Life-course Studies

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