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 Show less
