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Predictive policing : the role of crime forecasting in law enforcement operations /
Predictive policing is the use of analytical techniques to identify targets for police intervention with the goal of preventing crime, solving past crimes, or identifying potential offenders and victims. These tools are not a substitute for integrated approaches to policing, nor are they a crystal b...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Santa Monica, CA :
RAND,
[2013]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Introduction
- Making predictions about potential crimes
- Using predictions to support police operations
- Using predictions to support investigations of potential offenders
- Findings for practitioners, developers, and policymakers.
- Cover; Title Page; Copyright; Preface; Contents; Figures; Tables; Summary; Acknowledgments; Abbreviations; CHAPTER ONE: Introduction; What Is Predictive Policing?; A Criminological Justification for Predictive Policing: Why Crime Is "Predictable"; A Brief History of Predictive Policing; Background; Training; Study Objectives and Methods; Objectives; Approach; The Nature of Predictive Policing: This Is Not Minority Report; A Taxonomy of Predictive Methods; Prediction-Led Policing Processes and Practices; Data Collection; Analysis; Police Operations; Criminal Response; About This Report.
- CHAPTER TWO: Making Predictions About Potential CrimesNotes on Software; Hot Spot Analysis and Crime Mapping; Grid Mapping; Covering Ellipses; Single and Dual Kernel Density Estimation; Heuristic Methods; Regression Methods; Types of Relationships; Selecting Input Variables; Leading Indicators in Regression (and Other) Models; A Regression Example; Data Mining (Predictive Analytics); Clustering; Classification; Training and Testing a Model; Near-Repeat Methods; Spatiotemporal Analysis; Basics of Spatiotemporal Analysis; Heat Maps; Spatiotemporal Modeling Using the Generalized Additive Model.
- SeasonalityRisk Terrain Analysis; A Heuristic Approach: Risk Terrain Modeling; A Statistical Approach to Risk Terrain Analysis; Discussion of Risk Terrain Analysis Approaches; Prediction Methods; CHAPTER THREE: Using Predictions to Support Police Operations; Evidence-Based Policing; Taking Action on Hot Spots in Washington, D.C.; Koper Curve Application in Sacramento; Investigating Convenience Store Robberies in Chula Vista, California; Predictive Policing in Context: Case Studies; Shreveport, Louisiana: Predictive Intelligence-Led Operational Targeting.
- Memphis, Tennessee: Crime Reduction Utilizing Statistical HistoryNashville, Tennessee: Integrating Crime and Traffic Crash Data; Baltimore, Maryland: Crash-Crime Project; Iraq: Locating IED Emplacement Locations; Minneapolis, Minnesota: Micro Crime Hot Spots; Charlotte-Mecklenburg County, North Carolina: Foreclosures and Crime; Crime Maps: Community Relations; Police Actions; CHAPTER FOUR: Using Predictions to Support Investigations of Potential Offenders; Protecting Privacy Rights and Civil Liberties; Predictive Policing Symposium Assessment.
- Privacy Under the Fourth Amendment of the U.S. ConstitutionPrivacy with Respect to Policing Intelligence Information Systems; Privacy Resources for the Law Enforcement Community; Dealing with Noisy and Conflicting Data: Data Fusion; Heuristic and Simple-Model Methods; More Sophisticated Fusion Methods; Risk Assessment for Individual Criminal Behavior; Commonly Used Behavioral Instruments; Limitations of Behavioral Instruments; Quebec, Canada: Assessing Criminogenic Risks of Gang Members; Pittsburgh, Pennsylvania: Predicting Violence and Homicide Among Young Men.