  The built environment has been postulated to have an impact on neighborhood
crime rates, however, measures of the built environment can be subjective and
differ across studies leading to varying observations on its association with
crime rates. Here, we illustrate an accurate and straightforward approach to
quantify the impact of the built environment on neighborhood crime rates from
high-resolution satellite imagery. Using geo-referenced crime reports and
satellite images for three United States cities, we demonstrate how image
features consistently identified using a convolutional neural network can
explain up to 82% of the variation in neighborhood crime rates. Our results
suggest the built environment is a strong predictor of crime rates, and this
can lead to structural interventions shown to reduce crime incidence in urban
settings.
