This article proposes a novel method for matching places based on visual similarity, using high-resolution satellite imagery and machine learning. This approach strengthens comparisons when the built environment is a potential confounder, as in many injury research studies.
As an example, I apply this method to study the spatial influence of alcohol outlets (AOs) on firearm violence in Philadelphia, Pennsylvania, specifically beer stores and bar/restaurants. Using a case–control framework, city blocks with shootings in 2017–2018 were matched with similar-looking blocks with no shootings, based on analysis with a pretrained convolutional neural network and t-distributed stochastic neighbour embedding. Logistic regression was used to estimate the OR of a shooting on the same block as an AO and within one-block and two-block distances, conditional on additional factors such as land use, demographic composition and illegal drug activity.
The case–control matches were similar in visual appearance, on human inspection, and were well balanced on covariate measures. The fully adjusted model estimated an increased shootings risk for locations with beer stores within one block, OR=1.5, 95% CI 1.1 to 2.1, p=0.02, and locations with bar/restaurants on the same block, OR=1.6, 95% CI 1.1 to 2.4, p=0.02.
These findings align with previous study findings while addressing the concern that AOs might systematically be located in certain kinds of environments, providing stronger evidence of a causal effect on nearby firearm violence. Matching on visual similarity can improve observational injury studies involving place-based risks.