Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model

Abstract

Offender residences have become a research focus in the crime literature. However, little attention has been paid to the interactive associations between built environment factors and the residential choices of offenders. Over the past three decades, there has been an unprecedented wave of migrant workers pouring into urban centers for employment in China. Most of them flowed into urban villages within megacities. Weak personnel stability and great mobility have led to the urban villages to be closely related to decreased public safety and the deterioration of social order. The YB district in China was selected as the study area, which is located in one of the most developed cities in Southern China and has an area of approximately 800 km2 and a population of approximately four million people. This study aims to explore the associations between the neighborhood environment and the offender residences by using the geographical detector model (GeoDetector) from the perspective of interaction. The conceptual framework is based on the social disorganization theory. The results found that urban villages were the most important variable with a relatively high explanatory power. In general, taking the urban village as the carrier, various places (hotels, entertainment places, and factories) within the urban village may be more likely to include offender residences. This study also found the social disorganization theory applicable in the non-Western context. These findings may have important implications for offender residences identification, crime prevention, and the management of urban villages in Chinese cities.

Exploring the Relative Importance and Interactive Impacts of Explanatory Variables of the Built Environment on Ride-Hailing Ridership by Using the Optimal Parameter-Based Geographical Detector (OPGD) Model

“…The geographical detector can detect the influence of a single variable and the interaction of variables, and eliminate the influence of the multicollinearity of variables [40]. The geographical detector model has been widely used in research in various fields, such as health risk assessments [40][41][42][43][44][45][46], crime prediction [47,48], land health assessments [49,50], carbon emission influencing factors [51,52], etc. To the best of our knowledge, the geographical detector model has not been used in the impact of the built environment on the ridership of ride-hailing.…”