Physical environment features that predict outdoor active play can be measured using Google Street View images

International Journal of Health Geographics

Research

Nadine Schuurman¹  David Swanlund¹  Mariana Brussoni²  Christina Han²  William Pickett³  Randy Boyes⁴  Ian Janssen⁵  Louise Masse⁶ 

[1] Department of Geography, Simon Fraser University, RCB 6119/7134, V5A 1S6, Burnaby, BC, Canada;Department of Pediatrics, School of Population and Public Health, Human Early Learning Partnership, University of British Columbia, British Columbia Children’s Hospital, Room F511, 4480, Oak Street, V5H 3V4, Vancouver, BC, Canada;Department of Public Health Sciences, Queen’s University, K7L 3N6, Kingston, ON, Canada;Faculty of Applied Health Sciences, Brock University, 1812 Sir Isaac Brock Way, L2S 3A1, St. Catharines, ON, Canada;Department of Public Health Sciences, Queen’s University, K7L 3N6, Kingston, ON, Canada;Presage Group, Inc, 3365 Harvester Road, Suite 206, L7N 3N2, Burlington, ON, Canada;Department of Public Health Sciences, Queen’s University, K7L 3N6, Kingston, ON, Canada;School of Kinesiology and Health, Queen’s University, K7L 3N6, Kingston, ON, Canada;School of Population and Public Health, University of British Columbia, British Columbia Children’s Hospital, Room F508, 4480 Oak Street, V5H 3V4, Vancouver, BC, Canada;

关键词: Child;    Built environment;    Social factors;    Cities;    Exercise;    Play;   

DOI  :  10.1186/s12942-023-00346-3

 received in 2023-04-18, accepted in 2023-09-14,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundChildrens’ outdoor active play is an important part of their development. Play behaviour can be predicted by a variety of physical and social environmental features. Some of these features are difficult to measure with traditional data sources.MethodsThis study investigated the viability of a machine learning method using Google Street View images for measurement of these environmental features. Models to measure natural features, pedestrian traffic, vehicle traffic, bicycle traffic, traffic signals, and sidewalks were developed in one city and tested in another.ResultsThe models performed well for features that are time invariant, but poorly for features that change over time, especially when tested outside of the context where they were initially trained.ConclusionThis method provides a potential automated data source for the development of prediction models for a variety of physical and social environment features using publicly accessible street view images.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 5

Fig. 1

13690_2023_1170_Article_IEq112.gif

Fig. 4

Fig. 2

42004_2023_995_Article_IEq36.gif

Fig. 3

Fig. 1

13690_2023_1170_Article_IEq97.gif