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

犯罪、警务和公民 (CPC) - 动态网络的时空相互作用

• 批准号: EP/J004197/1
• 负责人: Tao Cheng
• 金额: $ 178.42万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ004197%2F1
• 关键词: Crime; Policing; Citizenship; CPC; Space

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.

今天，犯罪继续给大城市的公民福祉蒙上阴影，同时也带来了巨大的经济和社会代价。预防、及早发现和战略缓解都是有效的政策干预的关键，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，并在伦敦大都会警察局计算机辅助调度(CAD)数据库中进行记录和地理参考。今天，迫在眉睫的资金削减为中央问责和提高效率带来了新的压力，而社区赋权倡议给警务带来了新的机遇和挑战。及时了解犯罪行为是如何产生的，犯罪模式是如何演变的，这对于预测犯罪、在犯罪发生时加以处理以及培养公众对警察部门的信心至关重要。人们普遍认为，警务、犯罪和公众信任都具有很强的空间和时间维度。需要一种综合的时空分析方法，以分析犯罪模式、警察活动模式和社区特征，以便了解和预测犯罪活动发生和持续的时间、地点和方式。这项研究将巩固综合时空数据挖掘和紧急网络复杂性方面的成果，以揭示犯罪、警务和公民感知在一系列空间和时间尺度上的模式。民航处报告的每个警察行动、犯罪(和骚乱)以及市民拨打‘999’电话的数据集构成了一个时空网络(STN)，该网络在时空上有其独特的模式和行为，并与其他STN相互作用。(有地理标记的)警力在空间和时间上的部署、犯罪和骚乱的时空模式以及公众的看法可能在不同程度上相互关联。第一个据称是对第二个网络的预测和响应，而第三个是对前两个网络的滞后响应，这使得我们有理由预计所有三个网络都应该紧密耦合。该项目将首先分析单个犯罪网络的时空模式，然后通过创新的统计回归和机器学习开发的综合时空数据挖掘，将这些模式联系起来。这项研究将利用一系列学科(犯罪学、地理学、地理信息学和计算机科学)来帮助设计有效的实用解决方案来解决犯罪问题。它为探索犯罪模式和整合关于犯罪和警察活动的信息提出了一种新的方法。它系统地处理时空数据挖掘中的分析问题的结构化方案，这些问题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，推动网络复杂性研究的理论、方法和应用。这将使我们不仅有可能了解活动网络，而且还可以利用它们进行预测和决策。这解决了RCUK的全球不确定因素方案在犯罪、恐怖主义和意识形态和信仰方面的目标。它将扩大我们对不同形式的复杂系统之间微妙相互作用的理解，使我们能够将应对恐怖主义和有组织犯罪的战术和战略对策联系起来。它将通过给予意外的预测水平，使伦敦大都会警察局能够进行智能警务。个别大都会行政区的最佳做法可以推广到英国的其他行政区。这里开发的方法将适用于使用类似事件报告系统的其他国际城市。这将使生活、工作和访问伦敦和这些城市的人直接受益，让他们感到安全。

项目成果

A new metric of crime hotspots for Operational Policing

行动警务犯罪热点的新衡量标准

• 发表时间: 2015
• 作者: Adepeju, M.

Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach

• DOI: 10.1016/j.jtrangeo.2020.102673
• 发表时间: 2020-04-01
• 期刊: JOURNAL OF TRANSPORT GEOGRAPHY
• 影响因子: 6.1
• 作者: Bantis, Thanos;Haworth, James
• 通讯作者: Haworth, James

Determining the optimal spatial scan extent (K) of a Prospective space-time scan statistics (PSTSS) that maximises the predictive accuracy of crime prediction

确定前瞻性时空扫描统计 (PSTSS) 的最佳空间扫描范围 (K)，以最大限度地提高犯罪预测的预测准确性

• 发表时间: 2017
• 作者: Adepeju M;Cheng T
• 通讯作者: Cheng T

GeoComputation, Second Edition

地理计算，第二版

• DOI: 10.1201/b17091-4
• 发表时间: 2014
• 作者: Adnan M

Determining the optimal spatial and temporal thresholds that maximize the predictive accuracy of the prospective space-time scan statistic (PSTSS) hotspot method

确定最佳空间和时间阈值，最大限度地提高前瞻性时空扫描统计 (PSTSS) 热点方法的预测精度

• DOI: 10.5311/josis.2018.16.362
• 发表时间: 2018
• 期刊: Journal of Spatial Information Science
• 影响因子: 1.4
• 作者: Adepeju M