Explaining Crime Diversity with Google Street View

Abstract

Crime diversity is a measure of the variety of criminal offenses in a local environment, similar to ecological diversity. While crime diversity distributions have been explained via neutral models, to date the environmental and social mechanisms behind crime diversity have not been investigated. Building on recent work demonstrating that crime rates can be inferred from street level imagery with neural network computer vision models, in this paper we consider the task of inferring crime diversity through street level imagery. We use the Google Vision API, a deep learning image tagging service, to extract objects from sampled Google Street View (GSV) images in each census block of Los Angeles. For each census block we then compute indices for (1) object diversity, (2) diversity related to commonly employed census variables, and (3) crime diversity from reports provided by the Los Angeles Police Department. We then build ordinary least squares and geographically weighted regression models to explain crime diversity as a function of environmental diversity, population diversity, and population size. We show that crime diversity arises via a combination of environmental diversity (as measured through street view object diversity), household diversity (as measured through the census), and population size. Population size and area of the census block both lend credence to the neutral model proposed by Brantingham for crime diversity. However, environmental and demographic diversity combined play an equally important role in explaining variation in crime diversity. Our study has two primary implications for research on crime and place. First, Google Street View (via the Google Vision API) can provide important, cost-effective empirical insights to best understand distinct geographic environments of crime. Second, environmental diversity, as measured by image tagging in GSV, was observed to be more predictive of crime diversity (variety of crime types) than commonly used census measures.