Predicting perceptions of the built environment using GIS, satellite and street view image approaches

Highlights

• Substantial within and between city differences in built environment perceptions exist.

• Environmental exposures and urbanicity measures differed between high and low perception locations.

• Visible street level features explained 17–18% of the variation in safety, lively, and beauty perceptions.

• GIS and remote sensing data explained only 3–10% of the variation in safety, lively, and beauty perceptions.

• Models were able to predict between- cities differences in perceptions much better than within-city differences.

Abstract

Background

High quality built environments are important for human health and wellbeing. Numerous studies have characterized built environment physical features and environmental exposures, but few have examined urban perceptions at geographic scales needed for population-based research. The degree to which urban perceptions are associated with different environmental features, and traditional environmental exposures such as air pollution or urban green space, is largely unknown.

Objective

To determine built environment factors associated with safety, lively and beauty perceptions across 56 cities.

Methods

We examined perceptions collected in the open source Place Pulse 2.0 dataset, which assigned safety, lively and beauty scores to street view images based on crowd-sourced labelling. We derived built environment measures for the locations of these images (110,000 locations across 56 global cities) using GIS and remote sensing datasets as well as street view imagery features (e.g. trees, cars) using deep learning image segmentation. Linear regression models were developed using Lasso penalized variable selection to predict perceptions based on visible (street level images) and GIS/remote sensing built environment variables.

Results

Population density, impervious surface area, major roads, traffic air pollution, tree cover and Normalized Difference Vegetation Index (NDVI) showed statistically significant differences between high and low safety, lively, and beauty perception locations. Visible street level features explained approximately 18% of the variation in safety, lively, and beauty perceptions, compared to 3–10% explained by GIS/remote sensing. Large differences in prediction were seen when modelling between city (R2 67–81%) versus within city (R2 11–13%) perceptions. Important predictor variables included visible accessibility features (e.g. streetlights, benches) and roads for safety, visible plants and buildings for lively, and visible green space and NDVI for beauty.

Conclusion

Substantial within and between city differences in built environment perceptions exist, which visible street level features and GIS/remote sensing variables only partly explain. This offers a new research avenue to expand built environment measurement methods to include perceptions in addition to physical features.