Bayesian analysis of urban theft crime in 674 Chinese cities.

Current academic research on fitting the volume of urban theft crimes at a macro size is limited, especially from the urban functionality perspective. Given this gap, this study utilizes a Bayesian model to conduct a fitting analysis of theft crime data from 674 cities in China from 2018 to 2020. This research aims to explore novel pathways for theft crime fitting. Results indicate that the size of urban functionality, particularly points of interest (POIs), exhibits excellent performance in fitting theft crimes, with POIs related to public services and commercial activities demonstrating the most significant fitting effects. This research successfully identifies effective indicators for crime fitting, thereby offering a new perspective and supplement to theft crime research. This study holds significant value for gaining a profound understanding of criminal phenomena and explaining the causes and mechanisms underlying the differences in theft crimes among various cities in China.