基于agent仿真和GIS的警察巡逻区设计

Yue Zhang, Donald E. Brown
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引用次数: 5

摘要

警察巡逻在公共安全方面发挥着重要作用。巡逻区域的设计是影响巡逻性能的重要因素,如平均响应时间和工作量变化。重新划分选区的过程可以被描述为将较小的地理单元划分为几个较大的区域,并具有邻近性和紧凑性的约束。可能样本空间的大小很大,相应的图划分问题是np完全的。在我们的方法中,我们使用基于代理的仿真模型在地理信息系统(GIS)环境中Java Repast实现了一个参数化重新划分过程生成的巡逻分区计划。研究了分区参数与响应变量之间的关系,从而得出更好的分区方案。在对这些计划进行深入评估之后,我们对模拟的输出执行帕累托分析,以找到每个目标上的非支配计划集。本文还包括对美国弗吉尼亚州夏洛茨维尔警察局的案例研究。仿真结果表明,与现有的分区方案相比,该方案可以提高巡逻性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Police patrol district design using agent-based simulation and GIS
Police patrols play an important role in public safety. The patrol district design is an important factor affecting the patrol performances, such as average response time and workload variation. The redistricting procedure can be described as partitioning smaller geographical units into several larger districts with the constraints of contiguity and compactness. The size of the possible sample space is large and the corresponding graph-partitioning problem is NP-complete. In our approach, the patrol districting plans generated by a parameterized redistricting procedure are evaluated using an agent-based simulation model we implemented in Java Repast in a geographic information system (GIS) environment. The relationship between districting parameters and response variables is studied and better districting plans can be generated. After in-depth evaluations of these plans, we perform a Pareto analysis of the outputs from the simulation to find the non-dominated set of plans on each of the objectives. This paper also includes a case study for the police department of Charlottesville, VA, USA. Simulation results show that patrol performance can be improved compared with the current districting solution.
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