Analysing Urban Crime Risk Factors And Modeling Crime Risk Terrain Using GIS

Crime risk, in common with criminal behaviors, is unevenly distributed across space. Risk Terrain Modeling is an emerging approach to identify crime risks that come from features of a landscape and model how they co-relate to create unique behavior settings for crime. This approach is favorable for revealing the crime attractors and generators in physical environment. Not only articulating vulnerable areas in city, our study can also make scientific and accurate forecasts of where the crime will more likely to occur. It is a pleasure to see the study helpful for making crime prevention measures and urban safety protection by administrative apartment of urban planning and public security. Our study firstly introduced foreign theory of crime and environmental crime, reviewed their research perspectives and methods, to construct a system of crime risk factors. Then took the central area of Wuhan City as case study area to model crime risk terrain by combining the big city features, such as social and economic development pattern, layout of urban land use and infrastructure configuration.On the base of four classical theories in geography of crime named ecological theory of crime, routine activity theory, crime pattern theory, and rational choice theory, this article firstly construct a multi-level system of crime risk factor composed of background space, space of places, joint space, and agglomeration space, then select ten crime risk factors including residence districts, population density, office buildings, automated teller machines, configuration of urban road network, commercial facilities, bus and subway stations, hospitals, internet bars, and crime near repeats to be their concrete manifestation for the consideration of both previous studies and data availability. The main findings of this study included the following:Using kernel density tool to analyse the robbery, snatch and theft crime data obtained from web of court of justice, it showed that the spatial distribution of crime in study area has a polycentric model as a whole.Using geographically weighted regression (GWR) tool to explore the intensity and its spatial heterogeneity of the impact of crime risk factors to crime spatial distribution. The output calculated by ordinary least squares (OLS) indicated that the correlation coefficients of above crime risk factors were positive, except population density and residence districts that turned out to has no or less correlation with the spatial distribution of crime in study area.To modeling the crime risk terrain of the central area of Wuhan City, this study applied weighted sum tool, multiplied each crime risk factors, including office buildings, automated teller machines, configuration of urban road network, commercial facilities, hospitals, bus and subway stations, internet bars, crime near repeats, by their given weight and summing them together.Categorizing the high risk area type into three categories:city business centre, railway station hub, urban village and rural-urban fringe zone. City business centre was represented by Simenkou trading area, Jianghan trading area and Guanggu trading area, railway station hub by Hankou railway station, urban village by Fuxing village in Jianghan district, and ural-urban fringe zone by the contact zone between Hongshan district and Qiangshan district, respectively. The formation mechanisms of crime risk terrain in microscale environments were discussed in detail.