PROGRESS IN GEOGRAPHY ›› 2013, Vol. 32 ›› Issue (7): 1159-1166.doi: 10.11820/dlkxjz.2013.07.018

• Socio-Cultural Geography • Previous Articles     Next Articles

Spatial non-stationarity of the factors affecting crime rate at province scale in China

YAN Xiaobing1,2   

  1. 1. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241003, China;
    2. Zhejiang Police Vocational Academy, Hangzhou 310018, China
  • Received:2012-10-01 Revised:2013-01-01 Online:2013-07-25 Published:2013-07-25

Abstract: Income inequality and floating population are two important factors affecting crime rate. One major problem of the previous studies is that they were all based on ordinary least squares (OLS) estimation with constant coefficients. OLS estimation presumes that the individuals are homogeneous and the relationship between the crime rate and the two affecting factors do not change over spatial units, which contradicts the fact that significant differences exist among the 31 provinces of China. In other words, the relationship between crime and income inequality and floating population is too complicated to be explained by ordinary least squares estimation with constant coefficients. Geographically weighted regression (GWR) is a powerful tool for exploring spatial heterogeneity. GWR recognizes that relationships between variables are likely to vary across space. Instead of estimating one parameter for each independent variable, GWR estimates local parameters. A parameter is estimated for each data location in the study area. In a GWR model, parameters are estimated using a weighting function based on distance so that locations closest to the estimation point have more influence on the estimate. Using geographically weighted regression model, this paper analyzes the local relationship between crime rate and income inequality and floating population in 31 provinces of China. The results show that: (1) The effects on crime rate are spatially non-stationary. The correlation between crime rate and income inequality is significant in some provinces, but not significant in some other provinces. The correlation between crime rate and floating population is significant in all provinces, but not with the same degree. (2) GWR model is more suited than OLS model, the AIC and R square are both improved in GWR model. This study demonstrates the usefulness of GWR for exploring local processes that drive crime rates and for examining the misspecifications of a global model of crime rate. The practical implication of GWR analysis is that different crime prevention policies should be implemented in different regions of China. Because of such a heterogeneity, criminal policy needs to suit the local situations.

Key words: at province scale, China, crime rate, floating population, GWR, spatial heterogeneity