The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China

Abstract

Drug addiction and drug-related crime caused by drug dealing are serious problems for many countries. Such problems have gained urgency in China during recent years. However, there has been no research on the relationship between drug dealing and associated factors and its variation over space at a fine scale, such as the police station management area (PSMA), in China. Based on a seven-year data set obtained in ZG city, China, a geographically weighted Poisson regression (GWPR) model was employed to explore the spatial heterogeneity in the relationship between drug dealing and related risk factors, including social-demographic factors and environmental characteristics. The model results indicated that there were more drug dealings in the socially disorganized areas, typically associated with urban villages and floating population. Spatial accessibility had significant impacts on drug dealing. While the main road showed a negative effect, areas with more branch roads and bus stops tended to attract more drug dealings. Additionally, we found that these relationships were spatially nonstationary across space. This research represents the first in discerning spatial variation of drug dealing within a major Chinese city. These findings not only help policy makers better understand drug dealings from a geographical perspective, but can also help them to develop more targeted local intervention strategies.

The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan

“…In recent studies, geographically weighted Poisson regression (GWPR) has been effectively employed to explore the relationship between the number of crimes and influential factors. In these cases, the GWPR method was observed to outperform the GLM method [16,17].…”