Crime mapping features

Author:

Манжай О. В.,Потильчак А. О.

Abstract

In this paper tools, organization and tactics of crime mapping are analyzed. The directions of application of mapping for maintenance of public safety and order, in criminal intelligence process, etc. are outlined. The domestic experience of mapping is briefly analyzed. The main goals that are achieved with the use of mapping are defined. Features of visualization of criminogenic cells are revealed. Pin mapping features (when points which symbolize a certain event are placed on the map on the corresponding coordinates) are outlined. Kernel density mapping is described, which makes it much easier to detect criminogenic foci, as hot-spot maps clearly reflect the concentration of certain events in the region. A method of mapping using proportional symbol mapping is disclosed when the increase in the size of the symbol denoting a point on the map is proportional to the increase in the number of events or other parameters at these coordinates. The building of geographical profiles of criminals is briefly described. The theoretical basis of mapping for the prediction of crimes is outlined. Prediction strategies based on equations and machine calculations and actuarial strategies based on expertise and clinical strategy are analyzed. Considerations are given to the appropriateness of applying appropriate strategies in different countries. The phenomenon of near repeat patterns is studied. Some software solutions for the implementation of the tasks of mapping criminal manifestations and the use of artificial intelligence systems for this purpose are described. Examples are given. It is noted that the use of cartography to prevent and predict crimes in Ukraine is in its infancy. Some solutions are proposed that could improve the situation in the field of crime mapping in Ukraine.

Publisher

Kharkiv National University of Internal Affairs

Subject

Applied Mathematics

Reference11 articles.

1. Herchenrader T. and Myhill-Jones S., 2014. GIS supporting intelligence-led policing. Police Practice and Research, Vol. 16, Iss. 2, pp. 136-147. https://doi.org/10.1080/15614263.2014.972622).

2. Rezapour M., Yue E. and Ksaibati Kh., 2020. Integrating GIS and statistical approaches to enhance allocation of highway patrol resources. Police Science & Management, Vol. 22, Iss. 1, pp. 84-95. https://doi.org/10.1177/1461355719888939.

3. O’Sullivan D. and Unwin D.J, 2010.Geographic information analysis. 2nd ed. Wiley.

4. Kharevich D.L., 2010. Undercover investigation in Germany [Neglasnoe rassledovanie v Germanii]. Minsk: Akademiya MVD.

5. Canter D. and Youngs D., 2017. Principles of Geographical Offender Profiling. London: Routledge. https://doi.org/10.4324/9781315246086.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3