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作 者:卓蓉蓉 郑文升[1,2] 郑天铭 ZHUO Rong-rong;ZHENG Wen-sheng;ZHENG Tian-ming(Key Laboratory for Geographical Process Analysis & Simulation,Hubei Province,Central China Normal University,Wuhan 430079,China;Academy of Wuhan Metropolitan Area,Hubei Development and Reform Commission,Central China Normal University,Wuhan 430079,China)
机构地区:[1]华中师范大学地理过程分析与模拟湖北省重点实验室,武汉430079 [2]湖北省发展和改革委员会/华中师范大学武汉城市圈研究院,武汉430079
出 处:《人文地理》2018年第4期33-42,共10页Human Geography
基 金:国家社会科学基金项目(17BJL052)
摘 要:从犯罪背景空间、场所空间、联接空间和聚集空间四个层次,构建城市犯罪风险区位因子体系。以武汉市主城区为研究区域,以立案判决的"两抢一盗"犯罪为数据源,综合运用空间句法、犯罪近重复分析和地理加权回归模型方法进行犯罪风险地形建模实证研究。结果表明,犯罪风险区位因子对犯罪空间分布的影响具有显著的空间异质性;依据多层次的犯罪风险区位因子体系及其对犯罪行为的影响机制,城市犯罪高风险区域可分为城市商业中心、火车站交通枢纽、城中村和城乡结合部等几大类型。基于犯罪地理学理论构建的犯罪风险区位因子体系模拟的犯罪风险地形对实际犯罪空间分布有良好的解释度,可为城市犯罪分布环境形成机制的研究提供相关借鉴。Taking the central area of Wuhan as case study area, the case judgment of robbery, grabbing and theft as the data source, then eight crime risk factors including office buildings, automated teller machines,configuration of urban road network, commercial facilities, bus and subway stations, hospitals, internet bars,and crime near repeats were selected to be crime locational factors for the consideration of both previous studies and data availability. The main findings of this study included the following: 1) Using kernel density tool to analyze the spatial distribution of robbery, snatch and theft crime, it showed that the spatial distribution of crime in study area has a polycentric model as a whole. 2) The output calculated by ordinary least squares(OLS) indicated that there is a high correlation between crime risk locational factors and crime spatial distribution. 3) Categorizing the high risk area type into three categories, including city business center,railway station hub, urban village and rural-urban fringe zone. 4) To modeling the crime risk terrain of the central area of Wuhan city, this study applied weighted sum tool, multiplied each crime risk factors by their given weight and summing them together.
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