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

Abstract

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.