Crime, Policing and Citizenship (CPC) - Space-Time Interactions of Dynamic Networks

如今，犯罪继续给大城市市民的福祉蒙上阴影，同时也造成了巨大的经济和社会成本。预防、早期发现和战略性缓解都是有效政策干预的关键，特别是在需要协调应对的领域。每天，大约有10,000起事件由市民报告，记录并在伦敦大都会警察局计算机辅助调度（CAD）数据库中进行地理参考。今天，即将到来的资金削减给中央问责制和提高效率带来了新的压力，而社区赋权倡议给警务工作带来了新的机遇和挑战。及时了解犯罪是如何产生的，以及犯罪模式是如何演变的，这对于预测犯罪、在犯罪发生时采取应对措施以及建立公众对警察服务的信心至关重要。人们普遍认为，治安、犯罪和公众信任都具有很强的空间和时间维度。需要对时空分析采取综合办法，以便分析犯罪模式、警察活动模式和社区特征，以便了解和预测犯罪活动何时、何地以及如何出现和持续。本研究将巩固在综合时空数据挖掘和突发网络复杂性方面的成果，以揭示在一系列空间和时间尺度上犯罪、警务和公民感知的模式。每个在民航处报告的警察行动、犯罪（和混乱）和市民拨打“999”电话的数据集构成了一个时空网络（STN），它在时空中有自己的特征模式和行为，并与其他STN相互作用。警务人员在空间和时间上的（地理标记）部署、犯罪和混乱的时空模式，以及市民的看法，可能在不同程度上相互关联。据称，第一个网络预测并响应第二个网络，而第三个网络是对前两个网络的滞后响应，这让我们有理由预测这三个网络应该紧密耦合。该项目将首先分析单个stn的时空模式，然后通过使用创新的统计回归和机器学习开发的集成时空数据挖掘将这些stn之间的模式关联起来。这项研究将利用一系列学科（犯罪、地理、地理信息学和计算机科学）来帮助设计有效的实际解决犯罪问题的方案。它提出了一种探索犯罪模式和整合犯罪和警察活动信息的新方法。它系统地解决了时空数据挖掘中分析问题的结构化方案，这正在成为地理信息科学的核心。它将通过评估表征犯罪和其他社会经济现象的网络的形式和相互作用，推进网络复杂性研究的理论、方法和应用。这将使我们不仅可以理解活动网络，还可以将它们用于预测和决策。这涉及RCUK在犯罪、恐怖主义、意识形态和信仰方面的全球不确定性计划的目标。它将扩大我们对不同形式的复杂系统之间微妙的相互作用的认识，从而为应对恐怖主义和有组织犯罪的战术和战略反应提供背景。它将通过提供不可预见的预测水平，使伦敦警察局的智能警务成为可能。单个大都会区的最佳实践可以推广到英国的其他地方。这里开发的方法将被转移到使用类似事件报告系统的其他国际城市。这将直接惠及在伦敦生活、工作和旅游的人们，让他们感到安全。

Crime continues to cast a shadow over citizen well-being in big cities today, while also imposing huge economic and social costs. Prevention, early detection and strategic mitigation are all critical to effective policy intervention, especially in domains where coordinated responses are required. Every day, about 10,000 incidents are reported by citizens, recorded and geo-referenced in the London Metropolitan Police Service Computer Aided Dispatch (CAD) database. Today, impending funding cuts bring new pressures for central accountability and improved efficiency, while community empowerment initiatives bring new opportunities and challenges to policing. Timely understanding of how criminality emerges and how crime patterns evolve is crucial to anticipating crime, dealing with it when it occurs and developing public confidence in the police service. It is widely understood that policing, crime and public trust all have strong spatial and temporal dimensions. An integrated approach to space-time analysis is needed in order to analyse crime patterns, police activity patterns and community characteristics, so as to understand and predict the when, where and what of how criminal activities emerge and are sustained.This research will consolidate achievements in integrated spatio-temporal data mining and emergent network complexity to uncover patterning in crime, policing and citizen perceptions at a range of spatial and temporal scales. Each dataset of police movement, crime (and disorder) reported in the CAD, and citizens making '999' calls constitutes a spatio-temporal network (STN), which has its own characteristic patterning and behaviour in space-time, and which interacts with the other STNs. The (geotagged) deployment of police manpower in space and time, the spatio-temporal patterning of crime and disorder, and the perceptions of members of the public are likely to be interlinked to differing extents. The first of these purportedly both anticipates and responds to the second, while the third is a lagged response to the first two, giving reason to anticipate that all three networks should be tightly coupled. The project will first analyse spatio-temporal patterns of individual STNs, then associate the patterns among these STNs via integrated spatio-temporal data mining developed using innovative statistical regression and machine learning.This research will utilise a range of disciplines (crime, geography, geoinformatics, and computer science) to help engineer effective practical solutions to crime problems. It proposes a new method for exploring crime patterns and integrating information on crime and police activity. It systematically addresses a structured programme of analytical issues in spatio-temporal data mining, which are becoming core to Geographical Information Sciences. It will advance the theory, methodology and application of research into network complexity by evaluating the forms and interactions of the networks that characterise crime and other socio-economic phenomena. This will make it possible to not only understand activity networks but also to use them for prediction and decision making. This addresses the aims of RCUK's Global Uncertainties Programme in crime, terrorism, and ideologies and beliefs. It will extend our appreciation of the subtle interplay of different forms of complex systems, in ways that will contextualise tactical and strategic responses to terrorism and organised crime. It will enable intelligent policing of London Met Police by granting unforeseen levels of prediction. The best practice of individual Metropolitan boroughs can be extended to others in the UK. The methodology developed here will be transferrable to other international cities using similar incident report systems. This will directly benefit people who live, work and visit London and those cities to make them feel safe.