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作 者:曲比伟石 赵振斌 邓元杰 张熠 QUBI Weishi;ZHAO Zhenbin;DENG Yuanjie;ZHANG Yi(School of Geography and Tourism,Shaanxi Normal University,Xi’an 710119,China;College of Economics&Management,Northwest A&F University,Yangling 712100,Shannxi Province,China)
机构地区:[1]陕西师范大学地理科学与旅游学院,陕西西安710119 [2]西北农林科技大学经济管理学院,陕西杨凌712100
出 处:《浙江大学学报(理学版)》2019年第6期745-754,共10页Journal of Zhejiang University(Science Edition)
基 金:国家自然科学基金资助项目(41571174)
摘 要:以四川省成都市三环内城区为例,使用标准化犯罪强度指数和核密度估计方法,对成都市"两抢一盗"犯罪者行为的时空规律进行研究。结果发现,成都市中部和中北部存在2个稳定的犯罪热点;在年内月尺度上,"两抢一盗"犯罪案件集中在商贸区、住宅区和火车站,月内日尺度上,犯罪热点存在"波动式"空间偏移,月内周尺度上犯罪热点以原热点为基础,呈现明显的外延式空间偏移,日内时尺度上,犯罪热点沿主要道路转移。犯罪成因分析和影像图片分析表明,成都市"两抢一盗"犯罪以流窜作案为主,犯案者与复杂的环境因素在时空上的交互作用导致了作案地域的演变。整体来看,基于案件汇总数据的"两抢一盗"犯罪多尺度时空分析,可以在一定程度上解释区域内犯罪者的犯罪行为规律。The urban area around the third ring road of Chengdu city as an example, by using the standardized crime intensity index and kernel density estimation to explore the criminal behavior. The data source is adopted from the website of China Judgement online, which includes the specific time, places, type of crimes, amount of crimes, and the number of people involved in the robbery, snatch and theft within the third ring road of the Chengdu city during2013 to 2015. Concerning the spatial pattern, it concludes that there are two stable crime hotspots in the central and north central regions of the Chengdu city. According to the monthly data of each year, the cases of robbery, snatch and theft are gathered in business districts, residential areas and railway stations. According to the data of each day, crime shift patterns can be clearly observed around the main hotspots in the central and north-central of Chengdu city. From the weakly data of a month,it is found that the hotspots of crimes remain stable meanwhile extend outward.The hourly data is of a day manifest that crime hotspots of robbery, snatch and theft are shifted along the main streets as time goes on. It can be seen that the robbery, snatch and theft in the Chengdu city are mainly of moving crimes. On the whole,the multi-scale timing and spatial analysis of crimes based on aggregating crime incidents data can explain the criminal behavior in a specific region to a certain extent.
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