Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates

TL;DR

This study employs deep learning on satellite imagery to accurately quantify how the built environment influences neighborhood crime rates, revealing a strong predictive relationship that can inform urban interventions.

Contribution

It introduces a novel, objective method using convolutional neural networks to measure built environment features from satellite images and link them to crime rates.

Findings

Image features explain up to 82% of crime rate variation.

Built environment features are strong predictors of neighborhood crime.

Method demonstrates consistent identification of relevant environmental factors.

Abstract

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.