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- 2018
犯罪网络构建及其时空分析——以入室盗窃为例
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Abstract:
提出了一种基于时空影响范围的网络构造方法,构造了一种基于节点影响强度的犯罪传输网络,并引入复杂网络的度、平均度、聚集系数等特征参数分析犯罪传输网络。提取了犯罪预测过程中需要关注的重要节点,分析了其时间分布和空间分布特性,研究结果表明:(1)近邻的时空单元的犯罪率具有一定的关联关系。其中,节点的出度与入度具有正相关性,因此可以引入邻居时空单元的犯罪密度以量化和分析犯罪规律。(2)节点的度分布具有无标度特性,犯罪较少的小区也可能出现度较大的节点,而节点的度与未来犯罪率具有较大的关联性。因此,即便犯罪率较低的小区也要关注节点的度变化情况。(3)犯罪聚集系数大小与未来犯罪率的变化具有一定的关联性,较高的聚集系数意味着未来犯罪状态的变化
[1] | Short M B, D'Orsogna M R, Brantingham P J, et al. Measuring and Modeling Repeat and Near-Repeat Burglary Effects[J]. <em>Journal of Quantitative Criminology</em>, 2009, 25(3):325-339 |
[2] | Nakaya T, Yano K. Visualising Crime Clusters in A Space-Time Cube:An Exploratory Data-Analysis Approach Using Space-Time Kernel Density Estimation and Scan Statistics[J]. <em>Transactions in GIS</em>, 2010, 14(3):223-239 |
[3] | Shiode S, Shiode N. Network-Based Space-Time Search-Window Technique for Hotspot Detection of Street-Level Crime Incidents[J]. <em>International Journal of Geographical Information Science</em>, 2013, 27(5):866-882 |
[4] | Watts D J. Networks, Dynamics, and the Small World Phenomenon Model[J]. <em>Phys Lett A</em>, 1999, 263:341-346 |
[5] | Jian F, Huang L, Ying D, et al. Research on the Spatial-Temporal Characteristics and Mechanism of Urban Crime:A Case Study of Property Crime in Beijing[J]. <em>Acta Geographica Sinica</em>, 2012, 67(12):1645-1657(冯健, 黄琳珊, 董颖,等. 城市犯罪时空特征与机制:以北京城八区财产类犯罪为例[J]. 地理学报, 2012, 67(12):1645-1656) |
[6] | Ye X, Xu X, Lee J, et al. Space-Time Interaction of Residential Burglaries in Wuhan, China[J]. <em>Applied Geography</em>, 2015, 60:210-216 |
[7] | Zhang L, Messner S F, Liu J. A Multilevel Analysis of the Risk of Household Burglary in the City of Tianjin, China[J]. <em>British Journal of Criminology</em>, 2007, 47(6):918-937 |
[8] | Brunsdon C, Corcoran J, Higgs G. Visualising Space and Time in Crime Patterns:A Comparison of Methods[J]. <em>Computers, Environment and Urban Systems</em>, 2007, 31(1):52-75 |