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Download or read book Spatio-temporal Modeling of Crime in Urban Environments written by Yeondae Jung and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasing availability of georeferenced data provides researchers a new source they can use to study criminal behavior and law enforcement in space and time dimensions. Although these studies can help broaden our understanding of crime and criminal justice and examine criminological theories, researchers or practitioners analyzing patterns of crime in space and time should be aware of the characteristics of the data and how to handle it with the proper quantitative method. Since spatio-temporal data may violate the independence assumption in conventional regression due to a spatial and/or a temporal structure, an analysis ignoring these effects can result in statistically misleading inferences. Thus, this dissertation is devoted to exploring three issues in the analysis of crime and law enforcement, which may need a methodological adjustment to account for structures in both space and time dimensions. The first chapter introduces the topics, the rationales, and the methodologies in the three papers detailed in Chapters 2 through 4 in this dissertation. Chapter 2 investigates how temperature, as well as socio-economic factors, are associated with crime in an urban environment. Using a Bayesian analysis on three years of monthly data in Seoul, South Korea, this study shows that an association of temperature to assaults varies with economic status and commercial land use of an area. Chapter 3 investigates crime density in four time periods in a day with two types of population measures and environmental variables with a case study in a sub-district of Seoul. The results show that ambient population better explains the variations of assaults for all time periods than residential population. In addition, socio-economic factors that are also significantly associated with the assault are identified, even after population factors are accounted for. Chapters 2 and 3 compare the results of models with different spatial and/or temporal structures and find that the model accounting for both structures better explains the data. Chapter 4 connects crime clearance rates to installed public surveillance cameras using four years of data from Dallas, TX. Focusing on the interaction between pre/post installation and camera distance, the study shows that crime clearance rates are higher after camera installation. However, the effects of surveillance cameras are shown to be dependent on crime types. Finally, Chapter 5 summarizes the main findings and implications of each paper, and discusses the delimitations and limitations to be addressed in future research.