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This book explores the potential of using machine learning methods to predict residential burglaries, with a focus on near-repeat phenomena. It investigates factors that contribute to accurate forecasting, utilizing data from 2010 to 2017 on residential burglaries, supplemented by geographic information about crime scenes. The study employs various machine learning techniques, including Random Forest, XGBoost, Support Vector Machines, Neural Networks, and Soft Voting, achieving a prediction precision of over 60%. The research also demonstrates for the first time the feasibility of making accurate forecasts for rural areas. This work is part of the 'BestMasters' series, highlighting top master's theses from universities in Germany, Austria, and Switzerland, and is intended for both practitioners and researchers in the fields of predictive policing, data science, and criminology.

Machine learning. --- Burglary investigation. --- Machine learning --- Burglary investigation

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This is a practical overview on burglary, theft, and the perpetrators of these most common crimes. The scope of the text is broad; the author covers everything from the offenders to the victims, from selection of partners to psychology of the criminal, from safecracking to computer theft, from organized automobile thievery to shoplifting, from false burglary reports to criminal interrogation, from prevention to investigation, from arrest to punishment. The psychiatric evaluations of over one hundred offenders that form the core of this study are augmented by more informal interviews with crimi

Burglary -- United States. --- Burglary investigation -- United States. --- Larceny -- United States. --- Burglary --- Larceny --- Burglary investigation