Data Mining and Predictive Analysis
Intelligence Gathering and Crime AnalysisBy
- Colleen McCue, Ph.D., Experimental Psychology, Program Manager, Richmond Police Department, Richmond, VA, USA
It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting. It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis, which law enforcement and intelligence professionals can tailor to their own unique situation and responsibilities.
Government agencies and institutions, law enforcement agencies (crime analysts and criminal investigators). Managers and command staff making data mining purchasing decisions, data mining and artificial intelligence developers, private security consultants, legislators, and policy makers.
Paperback, 368 Pages
Published: September 2006
Imprint: Butterworth Heinemann
- Introductory Section Chapter 1: Basics Chapter 2: Domain Expertise Chapter 3: Data mining Methods Chapter 4: Process Models for Data Mining and Analysis Chapter 5: Data Chapter 6: Operationally-relevant preprocessing Chapter 7: Identification, Characterization and Modeling Chapter 8: Evaluation Chapter 9: Operationally-Actionable Output Applications Chapter 10: Normal Crime Chapter 11: Behavioral Analysis of Violent Crime Chapter 12: Risk and Threat Assessment Case Examples Chapter 13: Deployment Chapter 14: Surveillance Detection Advanced Concepts and Future Trends Chapter 15: Advanced Concepts in Data Mining Chapter 16: Future Trends