GIS and Crime Analysis

IF 1.4 4区 社会学 Q2 GEOGRAPHY Geography Pub Date : 2021-07-28 DOI:10.1093/obo/9780199874002-0233
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Abstract

Spatial analysis of crime has gained increasing attention during the past thirty years, coupled with the growth of geographic information systems (GIS). Most crime analysis tasks are either carried out in a GIS environment or supported by a GIS. GIS is typically used as a tool for data management, data processing, data visualization, and data analysis for crime studies. Crime analysis normally involves the following elements: uncovering spatio-temporal patterns of crime distribution, such as crime hotspots; explaining these patterns and discerning major contributing factors based on multivariate regression modeling; predicting future crime patterns using machine learning and other predictive methods; developing crime prevention approaches based on historical and future crime patterns; and evaluating the effectiveness of crime prevention, to find out if crime is reduced in the targeted area and whether the nearby areas are affected by the intervention. It should be noted that crime analysis is inherently multidisciplinary, including but not limited to geography, criminology, computer science, statistics, urban planning, and sociology. Therefore, an effective crime analyst should be well trained in multiple disciplinary approaches. Any crime analysis that leads to real-world impact must rely on sound theories and effective methodologies. Many of the theories covered in this article are related to geography, criminology, and sociology. The methods are mostly influenced by GIS, spatial statistics, and artificial intelligence. Crime analysis also involves multiple stakeholders, including at least government agencies, universities, and private companies. Universities conduct basic and applied research, private companies convert the research to products, and government agencies provide funding for research and implement crime prevention strategies. In addition, crime analysis needs to pay close attention to potential issues related to ethics, privacy, confidentiality, and discrimination.
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GIS与犯罪分析
在过去的三十年里,随着地理信息系统的发展,犯罪的空间分析越来越受到关注。大多数犯罪分析任务要么在地理信息系统环境中执行,要么由地理信息系统支持。GIS通常被用作犯罪研究的数据管理、数据处理、数据可视化和数据分析工具。犯罪分析通常包括以下要素:揭示犯罪分布的时空模式,如犯罪热点;解释这些模式,并基于多元回归模型识别主要影响因素;使用机器学习和其他预测方法预测未来的犯罪模式;根据历史和未来犯罪模式制定预防犯罪办法;以及评估预防犯罪的有效性,以了解目标地区的犯罪是否减少,以及附近地区是否受到干预的影响。应该指出的是,犯罪分析本质上是多学科的,包括但不限于地理学、犯罪学、计算机科学、统计学、城市规划和社会学。因此,一个有效的犯罪分析员应该接受多种学科方法的良好培训。任何能产生现实影响的犯罪分析都必须依赖于健全的理论和有效的方法。本文涉及的许多理论都与地理学、犯罪学和社会学有关。这些方法主要受到GIS、空间统计学和人工智能的影响。犯罪分析还涉及多个利益相关者,至少包括政府机构、大学和私营公司。大学进行基础和应用研究,私营公司将研究转化为产品,政府机构为研究和实施预防犯罪战略提供资金。此外,犯罪分析需要密切关注与道德、隐私、保密和歧视有关的潜在问题。
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来源期刊
Geography
Geography GEOGRAPHY-
CiteScore
1.70
自引率
21.40%
发文量
21
期刊介绍: An international journal, Geography meets the interests of lecturers, teachers and students in post-16 geography.
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