关键词: 刑事犯罪, 时空演变, 流动人口, 空间效应

Abstract:

By the methods of ESDA, Markov Chains, and based on the data of crime rates at province evel in China from 1993 to 2008, the spatio-temporal pattern change of crime rates were discussed. The results are shown as follows. First, on the overall distribution of space, the higher criminal rates gradually transferred to coastal areas. Second, The crime rates showed a strong trend of spatial correlation, the similar units cluster in space, the higher level spatial units were concentrated in the provinces of eastern China, and the lower level spatial unites were mainly in the provinces of middle China. There was an obvious trend in the “Polarization” of crime rates development level. Third, there exited a phenomenon of “Club Convergence” in crime rates development level in the study area, spatial discrepancy of crime rates development level has become greater. Referring to the previous studies, the relationship among income inequality, floating population and crime rates. In order to improve the overall predicting ability for crime rates at province level, the “spatial effect” is incorporated into the panel econometric model. The panel econometric model includes two effects, fix effect and random effect. Generally speaking, the fixed effect model is favored when the regression analysis is applied to a precise set of regions; random effect, instead, is an appropriate specification if a certain number of individuals are randomly drawn from a large region of reference. For this reason, the fixed effect panel model is chosen, which is extender to include spatial error autocorrelation or a spatially lagged dependent variable autocorrelation. This article uses spatial lag fixed effect panel regression model to analyze the relationship between macro factors and crime rates. The regression results show that considering the effect of time and spatial heterogeneity of conditions, there is no significant correlation between floating population and crime rates; but under the common role of the industry and the income gap, the floating population and the crime rates has significantly correlation. The correlation between industry income inequality and crime rates is much bigger than the correlation between urban-rural income inequality and regional income inequality and crime rates, and, the result is robust in a series of sensitivity tests, it means that industry income inequality has a leading effect on crime rate. The neighborhood spatial effect is the most important factor when explain crime. The construction of transportation infrastructure leads to time-space compression, and the time-space compression has a profound influence on neighborhood spatial effect.

Key words: crime, spatio-temporal pattern of crime rate, floating population, neighborhood effect