社会空间视角下广州居住地犯罪风险的格局与模式

Pattern and model of residential criminal risk based on social space in Guangzhou, China

基于社会解组理论,以广州118个街道为基本评价单元,在外来人口、性别与年龄、收入、失业、教育、婚姻、职业、住房等8类社会因子中选取相应指标,构建侵财犯罪风险和暴力犯罪风险的综合评价体系;利用信息熵方法评价各街道的犯罪风险得分;通过GDI指数测度各圈层地域内部犯罪风险的异质性;以此为基础,基于空间自相关方法探索其犯罪风险热点区,总结其空间模式。结果表明:(1)广州居住地犯罪风险的空间分异显著,旧城犯罪风险最高,核心区犯罪风险最低。两种类型的犯罪风险格局各异,且圈层内部的异质性各有不同。(2)广州居住地犯罪风险具有显著的空间相关性和集聚特征,犯罪风险热点区位于旧城区。(3)广州居住地侵财犯罪风险由中心到外围呈"U"型分布规律,暴力犯罪风险由中心到外围呈现高低交错的"波浪式"分布规律,两种类型的犯罪风险都具有"圈层+扇形"的空间分异模式。

The spatial distribution of inner-city crime rates and crime risks is a topic of great concern in the fields of criminal geography and urban geography. Many empirical studies have shown that inner city criminal behaviors exhibit distinct spatial differentiation characteristics. This is of great importance to pattern analysis of real crime, especially has greater practical significance for its role in predicting patterns of urban crime risk. Because of restrictions related to the difficulty of acquiring real crime data and issues of confidentiality, there are currently few academic summaries of crime risk patterns and model results for Chinese inner- city residents. Using social disorganization theory, the criminal risks of 118 neighborhoods were evaluated by the characteristics of social space in Guangzhou. The comprehensive evaluation system of property criminal risks and violent criminal risks are composed of social factors, including a floating population, sex and age, income, unemployment, education, matrimony, occupation, and housing. The scores of criminal risks in terms of neighborhoods were then examined based on information entropy. The heterogeneities of criminal risks in four layers of a circle were subsequently captured by GDI. On this basis, hot spots of criminal risks were explored by methods of spatial autocorrelation （GMI, LMI）. Finally, the model and dynamics of spatial patterns were summarized according to the results obtained. The results show that： （1） there exists obvious spatial differentiation for criminal risks in Guangzhou. The criminal risk in the old city is the highest, and that in the core area is the lowest. The differentiation of property risks and violent criminal risks in the inner circles are different, and （2） the spatial correlation and agglomeration of criminal risks are obvious in this city. Hot spots of criminal risks are located in the old city and the western fan-shaped area. （3） The curve corresponding to property criminal risk shows a ＂U＂ shape from the city center to the periphery. Moreover, the curve of violent criminal risk is a ＂wave＂ shape. Their spacial models show the ＂circle layer ＋ fan shape＂.