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Data assimilation and forecasting for urban crime models

城市犯罪模型的数据同化和预测

基本信息

批准号：
EP/P030882/1
负责人：
Naratip Santitissadeekorn
金额：
$ 12.89万
依托单位：
University of Surrey
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FP030882%2F1
关键词：
Data assimilation forecasting urban crime

项目摘要

In recent years, a great deal of research activities associated with big data analytics of crime events and crime patterns has greatly expanded and been increasingly received attentions from practitioners and government agencies; for example, "Big data, crime and security" programme run by Parliamentary Office of Science and Technology (POST) and many crime-related projects run by the Partnership for Conflict, Crime and Security Research (PaCCS). With an availability of a large amount of crime data, there is an opportunity to transform the 'Big data' into intelligence that will aid the law enforcement in switching from the incident-oriented policing toward proactive and strategic policing, which will lead to the most effective use of resources.With the aim of understanding dynamics of crime patterns, recent mathematical research has developed several crime models such as agent-based model (ABM) for the urban burglary that incorporate well-known interactions between individual criminals and environment at the neighbourhood level. These models provide an important tool to establish the links between hypothesised criminal behaviours embedded in the models and real-world observed crime data. What is missing, however, is a novel data assimilation technique of crime data analytics that can statistically merge the model predictions with the real-world crime data in order to make better projections of future crime patterns as well as quantified the hypothesised criminal behaviour within the crime models.The primary aim of the proposed research is to develop a practical computational tool in the framework of the sequential (Bayesian) data assimilation to statistically merge complex crime models with crime data. The research holds a promise of improving a predictive distribution of the crime rate and identifying the future of crime pattern through joint state-parameter estimation. A long-term policing strategy to reduce overall crime rate would greatly benefit from such data analytic tool. A theoretical understanding of the computational method in term of stability analysis and convergence rate will be studied to gain an insight into the applicability as well as limits of the method. The proposed work will also address the challenges in making a prediction of crime patterns in a rapidly changing situation, reflected through a drastic change of model parameter values. This problem is difficult to overcome in the previous research where non-parametric estimation or maximum likelihood framework was employed since these methods require optimisation under a large volume of data altogether. The sequential data assimilation, however, fit naturally to this problem when appropriately designed. The computational tool will be applied to the real-world crime data, which is accessible via the UCL-based secure data lab. With a collaboration with Prof. Shane Johnson, a criminologist at UCL, we will determine the relevance of the results to the practical use.

近年来，与犯罪事件和犯罪模式的大数据分析有关的大量研究活动大大扩大，并日益受到从业人员和政府机构的重视；例如，议会科学和技术办公室(POST)经营的“大数据、犯罪和安全”方案以及冲突、犯罪和安全研究伙伴关系(冲突、犯罪和安全研究伙伴关系)经营的许多与犯罪有关的项目。随着大量犯罪数据的可用，有机会将‘大数据’转化为情报，帮助执法部门从以事件为导向的警务转变为主动和战略性的警务，这将导致资源的最有效利用。为了了解犯罪模式的动态，最近的数学研究开发了几个犯罪模型，如基于主体的城市入室盗窃模型(ABM)，该模型包含了众所周知的罪犯个体与邻里之间的相互作用。这些模型提供了一个重要工具，可用来确定模型中所载的假想犯罪行为与实际观察到的犯罪数据之间的联系。然而，缺乏一种新的犯罪数据分析的数据同化技术，该技术可以在统计上将模型预测与真实世界的犯罪数据合并，以便更好地预测未来的犯罪模式，并量化犯罪模型中的假设犯罪行为。本研究的主要目的是在序贯(贝叶斯)数据同化框架下开发一个实用的计算工具，在统计上将复杂的犯罪模型与犯罪数据合并。这项研究有望改善犯罪率的预测分布，并通过联合状态参数估计确定犯罪模式的未来。减少总体犯罪率的长期警务战略将极大地受益于这种数据分析工具。从稳定性分析和收敛速度的角度对该计算方法进行理论上的理解，以深入了解该方法的适用性和局限性。拟议的工作还将解决在迅速变化的情况下预测犯罪模式方面的挑战，模型参数值的急剧变化反映了这一挑战。在以往采用非参数估计或最大似然框架的研究中，这一问题很难克服，因为这些方法需要在大数据量下进行优化。然而，如果设计得当，序列数据同化很自然地适合于这个问题。该计算工具将应用于真实世界的犯罪数据，这些数据可以通过基于伦敦大学学院的安全数据实验室访问。与伦敦大学学院犯罪学家肖恩·约翰逊教授合作，我们将确定结果与实际应用的相关性。