Investigating Crime Rate Prediction Using Street-Level Images and Siamese Convolutional Neural Networks

Abstract

The analysis of the environment for crime prediction is based on the premise that criminal behavior is influenced by the nature of the environment in which occurs. Street-level images are the closest digital depiction available of the urban environment, in which most street crimes take place. This work proposes a crime rate prediction model that uses street-level images to classify street crimes into low or high crime rate levels. For that, we use a 4-Cardinal Siamese Convolution Neural Network (4-CSCNN) and train and test our analytic model in two regions of Rio de Janeiro, Brazil, that showed high street crime concentrations between the years of 2007 and 2016. With this preliminary experiment, we investigate the use of convolutional neural networks (CNN) for the task of crime rating through visual scene analysis and found possibilities towards automatic crime rate predictions using CNN models.