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TEMPORAL AND SPATIOTEMPORAL MODELS FOR SHORT-CRIME PREDICTION
Title
TEMPORAL AND SPATIOTEMPORAL MODELS FOR SHORT-CRIME PREDICTION
Creator
Liu, Xiaomu
Date
2017, 2017-07
Description
One of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future...
Show moreOne of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future crimes. The ability to make short-term predictions may be of particular importance for optimizing police resource allocation. The goal of this study is to investigate the temporal and spatiotemporal pattern of crime in the city of Chicago and to build corresponding predictive models. First, a temporal model for forecasting citywide violent crime time count is proposed. This model is composed of a long-term trend and short-term variations using data of time, weather and crime. The importance of model reproducibility is addressed in this study to produce low-complexity models. We introduce an approach that provides a way to extend the model selection criterion to both prediction accuracy and model reproducibility. The experimental results show that models produced by this approach outperform several simple time-series models. It is also found that these models typically include fewer variables; therefore, they are more interpretable, and may provide superior generalization error. Next we develop a framework that provides predictions for tomorrow’s violent crime counts at the level of a police district. The procedures include citywide daily violent crime count prediction, violent crime density estimation, and distributing citywide predictions to districts according to the estimated densities. In order to estimate the crime spatial densities, we use mesh modeling and demonstrate that a mesh model can be used as the structure for modeling the spatial variation of crime rate since it is well adapted to the inhomogeneous crime distribution. The experimental results show that our method provides more-accurate forecasts than those given by historical crime statistics. One aspect of studying spatial pattern of crimes is identifying geographical regions with similar crime characteristics. Specifically, we illustrate applying unsupervised clustering techniques to segment the city into sub-regions. We explore the use of Gaussian mixture models combined with a Markov random field for the purpose of regularization. We also propose a framework for the evaluation of clustering models without knowing the ground truth, which can present a more-complete picture for model selection in unsupervised clustering problems. Finally, we develop a spatiotemporal prediction method that predicts the locations where violent crimes or property crimes are most likely to occur tomorrow. Crime incidents are rasterized by a spatiotemporal grid. Other factors that affect the time and location preferences of criminal activities are also leveraged and represented by that grid. Each spatiotemporal grid cell is treated as an example for training and testing our models. We also explore whether pooling data from various sub-regions based on spatial clustering can improve model performance. The experimental results show that our models are more accurate than conventional hot-spot models. It is found that the effects of using different training samples are not consistent, which depends on target crime type.
Ph.D. in Electrical Engineering, July 2017
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MACHINE LEARNING METHODS FOR PREDICTING GLOBAL AND LOCAL CRIME IN AN URBAN AREA
Title
MACHINE LEARNING METHODS FOR PREDICTING GLOBAL AND LOCAL CRIME IN AN URBAN AREA
Creator
Navarro Comes, Eric
Date
2018, 2018-05
Description
In recent years there has been growing interest in development of computer methods that can model and predict crime events for crime...
Show moreIn recent years there has been growing interest in development of computer methods that can model and predict crime events for crime prevention in law enforcement agencies. A popular example is the creation of crime density maps which are used to provide early-warning information about potential hotspots of crime in an urban area. One important aspect of predictive policing is to identify the time and place of likely crime occurrence so as to prevent future crime events. The ability to make accurate, timely predictions can be particularly important for optimizing police resource allocation in an urban area. This thesis focuses on developing machine learning techniques in crime analysis and prevention for predicting the overall crime trend in an urban area, as well as the likelihood of crime occurrence in a given local area during a time period. By using crime data extracted from the Citizen and Law Enforcement Analysis and Reporting (CLEAR) system in the city of Chicago, we demonstrate that state-of-the-art learning algorithms can achieve improved prediction accuracy over traditional methods based on time series models. We then study prediction techniques for determining the likelihood of crime occurrence at a specific local area during a given time window. We demonstrate these techniques in the operational framework of the Strategic Decision Support Centers (SDSCs) in the Chicago Police Department, where only a small number (up to six) surveillance cameras can be monitored simultaneously at any given time in a single district. We apply prediction techniques to select those cameras that most likely have crime events happening within their viewsheds during a determined time window, thereby maximizing the crime monitoring efficiency. Using these models, we can achieve higher accuracy than the methods based on local crime density alone.
M.S. in Electrical Engineering, May 2018
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