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This book describes an original, empirical study of judicial decision making. The process of determining sentences is a difficult one for judges and often unnecessarily intuitive, subjective, and complex. The present study introduces a conceptual outline and empirical technique for increasing the precision of sentencing policy, thus offering an aid to judges who sentence in the light of this policy. The primary purpose of this model of judicial decision making is to provide a framework for scaling the seriousness of any single case in relation to the facts of that case and for relating this assessment to the appropriate quantum of sentence. The validity of the model is tested and cross-validated in an archival study. This innovative research serves as an important prototype for a system of numerical guidance to judges and sentencers.
Rev. ed. of: Suburban burglary: a tale of two suburbs / by George F. Rengert and John Wasilchick. 2nd ed. 2000.
Crime Analysis With Crime Mapping introduces crime analysis, both the practice and profession, and supports the understanding of it all through discussing concepts, theories, practices, data, analysis techniques, and the relationship with policing.
Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.