Using Street View Images to Examine the Impact of Built Environment on Street Property Crimes in the Old District of CA City, China

Abstract

Property crimes on the street are common in cities, posing a certain threat to people’s daily life safety and social stability. Therefore, it is essential to analyze the characteristics and spatial patterns of street property crimes in the built environment to make cities safe. Based on environmental criminological theories, this study takes the MC old district in CA City as a case study and uses a negative binomial regression model to analyze the influencing factors of street property crimes in different periods. The results show the temporal and spatial differentiation in street property crimes. In terms of time, the number of crime cases presents the features of “three peaks and two troughs.” In terms of space, crime cases show spatial clustering patterns, mainly concentrated in the commercial and prosperous areas where the main roads of the city are located. During the whole day, openness, banks, bars, and restaurants have a significant positive effect on crime occurrence; closeness, police cameras, grocery stores, and distance to the nearest police patrol station had a significant negative effect on crime occurrence. There are two explanations for the positive and negative correlations of some environmental variables with a crime before dawn, daytime, and nighttime. This study explored the spatial-temporal distribution and factors that influence the old district street property crimes by extracting physical environmental characteristics from street view images using deep learning algorithms and providing a reference base for police departments to prevent and combat crime.