Using Google Street View Imagery in Land Use Regression to Predict Street Level Particulate Air Pollution

Abstract

Background/aim: Land-use regression (LUR) is frequently applied to estimate spatial patterns of air pollution, which is necessary for exposure assessment and epidemiological studies. Traditional LUR typically relies on data that is jurisdiction-specific or has lower spatial resolution. In this study, we investigate use of a promising new set of variables for LUR - features extracted from Google Street View (GSV) imagery - and compare GSV-only LUR to traditional LUR.Methods: We build models on previously collected mobile monitoring data in Blacksburg, VA (Particle Number [PN] and Black Carbon [BC]). We collected ~14,000 GSV images around the mobile monitoring routes. Images included 2 categories: (1) images within 50 meters of measurements to capture central features and (2) images aggregated within 8 buffers (250-2,000m) to capture background features. A deep learning model was used to classify features (e.g., vegetation, buildings, cars, water). The percentage of features within each image was used in stepwise linear regression to develop GSV-only LUR models.Results: We found that the GSV-only models had comparable performance to traditional LUR. Adjusted R2 (10-fold CV R2) was 0.76 (0.65) for PN and 0.69 (0.58) for BC, which were 5.6% (-5.8%) higher for PN and 4.5% (3.6%) higher for BC than traditional LUR models. Furthermore, adjusted R2 for PN (BC) models that used only central features were 0.45 (0.41) suggesting that street-level images at the measurement location can describe a significant amount of variability for these pollutants. Collectively, our findings suggest that GSV imagery is a promising data source for predicting street-level patterns of air pollution.Conclusions: Our results suggest that GSV imagery may be an effective data source for LUR. GSV offers potentially two major advantages: (1) finer spatial (i.e., street-level) resolution and (2) the ability to apply consistent data collection and processing protocols across large geographies and political boundaries.