| X市入室盗窃犯罪人空间出行距离及其影响因素分析 |
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| 引用本文： | 张超鹏,陈鹏,江欢,于越.X市入室盗窃犯罪人空间出行距离及其影响因素分析[J].地球信息科学,2022,24(10):1957-1967. |
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| 作者姓名： | 张超鹏 陈鹏 江欢 于越 |
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| 作者单位： | 1.中国人民公安大学信息网络安全学院,北京 1026002.北京工商大学电商与物流学院,北京 1000483.北京市公安局朝阳分局,北京 100025 |
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| 基金项目： | 教育部人文社会科学规划基金(20YJAZH009);中国人民公安大学基本科研业务费专项(2020JKF501) |
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| 摘 要： | 针对犯罪人空间出行距离测量及影响因素考虑上存在的不足,本文利用X市2015—2017年入室盗窃案件数据,基于百度地图计算了犯罪人居住地到作案地之间的步行、骑行和驾车等3种交通出行距离,作为实际空间出行距离的近似拟合,随后利用最优尺度回归模型分析了居住地和作案地所在区域的空间环境特征、作案时间特征和主体特征对犯罪人空间出行可能产生的影响。实证分析表明：① 计算得到的3种距离的频次分布均表现为距离衰减效应,超过50%的案件中犯罪人作案地到其居住地的交通距离均不超过10 km;② 在影响因素方面,犯罪人的空间出行距离分布主要受其作案地所在区域的空间环境特征影响,具体表现为目标较为集中、交通通达性较好的区域对邻近空间内的犯罪人具有较强的吸引性,而发案量较高的区域对距离较远的犯罪人具有较强的吸引性;③ 在主体特征上,团伙犯罪人较独狼式犯罪人有更长的空间出行距离,反映出团伙犯罪人在空间认知上有着更好的优势。本文有助于进一步深化对犯罪出行现象的理解,对犯罪预测具有一定的实践指导意义。
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| 关 键 词： | 入室盗窃 犯罪出行 最优尺度回归 X市 主成分分析 距离衰减效应 兴趣点 团伙犯罪人 |
| 收稿时间： | 2022-04-27 |
Residential Burglars' Journey-to-Crime Distribution and its Impacting Factors in X city |
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| ZHANG Chaopeng,CHEN Peng,JIANG Huan,YU Yue.Residential Burglars' Journey-to-Crime Distribution and its Impacting Factors in X city[J].Geo-information Science,2022,24(10):1957-1967. |
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| Authors: | ZHANG Chaopeng CHEN Peng JIANG Huan YU Yue |
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| Institution: | 1. Information Technology and Cyber Security Academy, People's Public Security University of China, Beijing 102600, China2. E-commerce and Logistics Academy, Beijing Technology and Business University, Beijing 100048, China3. Beijing Municipal Police Department Chaoyang Branch, Beijing 100025, China |
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| Abstract: | In criminology, journey-to-crime describes the phenomenon that how offenders be off their residing places and search for targets in space. In view of the disadvantages in the measurement of offenders' journey-to-crime and the influencing factors, the data of residential burglary offense in X city from 2015 to 2017 are collected and the walking, riding, and driving distances from the offender's residing location to their corresponding offending location are calculated using Baidu map API service. Then, this paper uses the categorical regression model to analyze the possible impact of environmental features within the offenders' residing area and the offending area, temporal pattern of crime, and subject characteristics of offenders on the journey-to-crime. The empirical analysis shows that: ① The frequency distribution of the three fitted traffic distances obeys the distance decay effect. Among more than 50% of the occurred crimes, the traffic distances between offenders' residing locations and their corresponding offending locations are no more than 10km, and also, the results indicate that there is no significant difference between any pair of three traffic distances, which demonstrates that three types of traffic distances are moderately equal in measuring journey-to-crime cost; ② In terms of influencing factors, the journey-to-crime distance of an offender is mainly affected by the environmental features within the offending area. Specifically, the results demonstrate that the more the targets concentrated and the areas being accessible, the more adjacent offenders are strongly attracted, while the more crime concentrated, the areas more strongly attract offenders from distant places; ③ In terms of the main individual characteristics of offenders, group offenders have longer journey-to-crime distance than single offenders, which reflects that group offenders have advantages in spatial cognition. It is mainly reflected in that for offenders who commit crimes alone, their "lone wolf" behavior restricts their spatial cognition to a low level, and the offenders who commit crimes in groups can learn and accumulate more about targets through information sharing between the members. At the same time, group cooperation can also support offenders to commit crimes in remote places from their residences and help them achieve higher crime benefits. Comparing with previous work on journey-to-crime, this paper includes the diversity of travel functions of offenders in their crime-committing processes, which fills the void of current research and enhances the understanding of journey-to-crime phenomenon. Meanwhile, the work finished in this paper could also be potentially and practically applied in offenders' detection scenarios, for example, geographic profiling. |
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| Keywords: | residential burglary journey-to-crime categorical regression X city principal component analysis distance decay effect point of interest group offenders |
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