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. 2023 Sep 28;22(1):26.
doi: 10.1186/s12942-023-00346-3.

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

Randy Boyes  1   2 , William Pickett  3   4 , Ian Janssen  3   5 , David Swanlund  6 , Nadine Schuurman  6 , Louise Masse  7 , Christina Han  8 , Mariana Brussoni  8
Affiliations

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

Randy Boyes et al. Int J Health Geogr. .

Abstract

Background: Childrens' 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.

Methods: This 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.

Results: The 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.

Conclusion: This 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.

Keywords: Built environment; Child; Cities; Exercise; Play; Social factors.

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Conflict of interest statement

The authors declare no competing interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Fig. 1
Fig. 1
Example of pixel-level image segmentation of a representative Google StreetView image
Fig. 2
Fig. 2
Points in Kingston, ON which were measured using Google's StreetView API
Fig. 3
Fig. 3
Comparison between vegetation pixel counts and true count of trees in the study area
Fig. 4
Fig. 4
Classification accuracy for sidewalks in the study area
Fig. 5
Fig. 5
Top decile of pixel count for traffic lights compared with true locations of traffic lights in the study area
Fig. 6
Fig. 6
Top decile of pixel counts for traffic signs compared to locations of traffic signs at intersections in the study area
Fig. 7
Fig. 7
Pixel counts of car and road compared to vehicle traffic count in the study area
Fig. 8
Fig. 8
Quartile of person pixel count compared to quartile of pedestrian traffic in the study area
Fig. 9
Fig. 9
Quartile of bicycle pixel count compared to quartile of bicycle intersection traffic in the study area
Fig. 10
Fig. 10
Points in Vancouver where StreetView image data were obtained from the StreetView API
See this image and copyright information in PMC

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