Analysis of the risk of theft from vehicle crime in Kyoto, Japan using environmental indicators of streetscapes

Abstract

Abstract With the advent of spatial analysis, the importance of analyzing crime patterns based on location has become more apparent. Previous studies have advanced our understanding of the factors associated with crime concentration in street networks. However, it has recently become possible to assess the factors associated with crime at even finer spatial scales of streetscapes, such as the existence of greenery or walls, owing to the availability of streetscape image data and progress in machine learning-based image analysis. Such place-scale environments can be both crime-producing and crime-preventing, depending on the composition of the streetscape environment. In this study, we attempted to assess the risk of crime occurrence through place-scale indicators using streetscape images and their interaction terms through binomial logistic regression modeling of the place-scale crime risk of theft from vehicles in the central part of Kyoto City, Japan. The results suggest that the effects of specific streetscape components on the risk of crime occurrence are certainly dependent on other components. For example, the association of the crime occurrence risk with the occupancy rate of vegetation in a streetscape image is positive when there are few buildings and walls, and vice versa. The findings of this study show the importance of considering the complex composition of visible streetscape components in assessing the place-scale risk of crime occurrence.

Keywords

• Environmental criminology
• Streetscape
• Semantic segmentation
• Urban network analysis
• Point of interest