Journal
HEALTH & PLACE
Volume 66, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.healthplace.2020.102428
Keywords
Neighborhood walkability; Walking behavior; Google street view; Deep learning; Semantic segmentation
Categories
Funding
- JSPS KAKENHI, Japan [17H00947, JP18KK0371, 20500604, 20H00040]
- Grants-in-Aid for Scientific Research [17H00947] Funding Source: KAKEN
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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.
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