Assessing State-Dependent Crime Patterns in the USA: A Markov Chain Approach

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Samuel T. Holloway
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引用次数: 0

Abstract

Understanding crime patterns in the USA can significantly contribute to effective policymaking and proactive law enforcement strategies. This study aims to utilize a novel method in the field of criminology - the Markov Chain model - to assess state-dependent crime patterns in the USA. The Markov Chain model, a mathematical system that undergoes transitions between different states based on certain probabilistic rules, provides an innovative approach to visualize and predict crime patterns. The application of this model enables us to make informed predictions about future crime rates based on current and historical data, thereby offering valuable insights into crime progression and recurrence. Data sourced from national and state-level crime databases forms the basis of this research. It is categorized into 'states' as per Markov Chain terminologies to represent different crime levels. The transitions between these states simulate the shifts in crime rates. The Markov Chain model is then implemented to map these transitions, yielding state-dependent crime patterns. Initial findings demonstrate a noteworthy degree of predictability in crime patterns, with variations in patterns across different states. Results also indicate that certain states have higher probabilities of experiencing increased crime rates, given their current state. Moreover, the model's ability to provide probabilistic predictions about future states may serve as a valuable tool for strategic planning in law enforcement. This research contributes significantly to the field by introducing a mathematical, probabilistic model to a largely sociological study area. It also has practical implications, as understanding these state-dependent crime patterns can enhance law enforcement efficiency and inform the development of targeted crime prevention strategies. Future studies may focus on refining the model, incorporating other socio-economic variables, and analyzing their impacts on crime transitions. This study thus opens up new avenues for employing mathematical models in criminology, demonstrating the vast potential of such interdisciplinary approaches. Keywords: Markov Chain Model, Crime Patterns, State-Dependent Crime Rates, Predictive Policing, Probabilistic Crime Analysis
评估国家依赖的犯罪模式在美国:一个马尔可夫链方法
了解美国的犯罪模式可以大大有助于有效的政策制定和积极的执法策略。本研究旨在利用犯罪学领域的一种新方法-马尔可夫链模型-来评估美国的州依赖犯罪模式。马尔可夫链模型是一种基于特定概率规则在不同状态之间进行转换的数学系统,它提供了一种可视化和预测犯罪模式的创新方法。该模型的应用使我们能够根据当前和历史数据对未来的犯罪率做出明智的预测,从而对犯罪的进展和复发提供有价值的见解。来自国家和州级犯罪数据库的数据构成了本研究的基础。根据马尔可夫链术语,它被分类为“州”,代表不同的犯罪水平。这些州之间的变化模拟了犯罪率的变化。然后实现马尔可夫链模型来映射这些转变,产生依赖于状态的犯罪模式。初步调查结果表明,犯罪模式具有显著的可预测性,不同州的模式有所不同。结果还表明,某些州在当前的状态下,犯罪率上升的可能性更高。此外,该模型提供关于未来状态的概率预测的能力可以作为执法战略规划的宝贵工具。这项研究通过将一个数学的概率模型引入一个主要的社会学研究领域,对该领域做出了重大贡献。它还具有实际意义,因为了解这些依赖于国家的犯罪模式可以提高执法效率,并为制定有针对性的预防犯罪战略提供信息。未来的研究可能会集中在改进模型,纳入其他社会经济变量,并分析它们对犯罪转变的影响。因此,这项研究为在犯罪学中使用数学模型开辟了新的途径,展示了这种跨学科方法的巨大潜力。关键词:马尔可夫链模型,犯罪模式,状态依赖犯罪率,预测警务,概率犯罪分析
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来源期刊
International Journal of Information and Learning Technology
International Journal of Information and Learning Technology COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.10
自引率
3.30%
发文量
33
期刊介绍: International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.
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