Data Mining and Predictive Analysis
Intelligence Gathering and Crime Analysis
- 1st Edition - September 15, 2006
- Imprint: Butterworth-Heinemann
- Author: Colleen McCue
- Language: English
- Paperback ISBN:9 7 8 - 0 - 7 5 0 6 - 7 7 9 6 - 7
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 4 6 4 6 2 - 6
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… Read more
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Request a sales quoteIt 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.
* Serves as a valuable reference tool for both the student and the law enforcement professional
* Contains practical information used in real-life law enforcement situations
* Approach is very user-friendly, conveying sophisticated analyses in practical terms
* Contains practical information used in real-life law enforcement situations
* Approach is very user-friendly, conveying sophisticated analyses in practical terms
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.
- Dedication
- Foreword
- Preface
- Introduction
- Introductory Section
- Chapter 1: Basics
- 1.1 Basic Statistics
- 1.2 Inferential versus Descriptive Statistics and Data Mining
- 1.3 Population versus Samples
- 1.4 Modeling
- 1.5 Errors
- 1.6 Overfitting the Model
- 1.7 Generalizability versus Accuracy
- 1.8 Input/Output
- Chapter 2: Domain Expertise
- 2.1 Domain Expertise
- 2.2 Domain Expertise for Analysts
- 2.3 Compromise
- 2.4 Analyze Your Own Data
- Chapter 3: Data Mining
- 3.1 Discovery and Prediction
- 3.2 Confirmation and Discovery
- 3.3 Surprise
- 3.4 Characterization
- 3.5 “Volume Challenge”6
- 3.6 Exploratory Graphics and Data Exploration
- 3.7 Link Analysis12
- 3.8 Nonobvious Relationship Analysis (NORA)13
- 3.9 Text Mining
- 3.10 Future Trends
- Methods
- Chapter 4: Process Models for Data Mining and Analysis
- 4.1 CIA Intelligence Process
- 4.2 CRISP-DM
- 4.3 Actionable Mining and Predictive Analysis for Public Safety and Security
- Chapter 5: Data
- 5.1 Getting Started
- 5.2 Types of Data
- 5.3 Data2
- 5.4 Types of Data Resources
- 5.5 Data Challenges
- 5.6 How Do We Overcome These Potential Barriers?
- 5.7 Duplication
- 5.8 Merging Data Resources
- 5.9 Public Health Data
- 5.10 Weather and Crime Data
- Chapter 6: Operationally Relevant Preprocessing
- 6.1 Operationally Relevant Recoding
- 6.2 Trinity Sight
- 6.3 Duplication
- 6.4 Data Imputation
- 6.5 Telephone Data
- 6.6 Conference Call Example
- 6.7 Internet Data
- 6.8 Operationally Relevant Variable Selection
- Chapter 7: Predictive Analytics
- 7.1 How to Select a Modeling Algorithm, Part I
- 7.2 Generalizability versus Accuracy
- 7.3 Link Analysis
- 7.4 Supervised versus Unsupervised Learning Techniques2
- 7.5 Discriminant Analysis
- 7.6 Unsupervised Learning Algorithms
- 7.7 Neural Networks
- 7.8 Kohonan Network Models
- 7.9 How to Select a Modeling Algorithm, Part II
- 7.10 Combining Algorithms
- 7.11 Anomaly Detection
- 7.12 Internal Norms
- 7.13 Defining “Normal”
- 7.14 Deviations from Normal Patterns
- 7.15 Deviations from Normal Behavior
- 7.16 Warning! Screening versus Diagnostic
- 7.17 A Perfect World Scenario
- 7.18 Tools of the Trade
- 7.19 General Considerations and Some Expert Options
- 7.20 Variable Entry
- 7.21 Prior Probabilities
- 7.22 Costs
- Chapter 8: Public Safety—Specific Evaluation
- 8.1 Outcome Measures
- 8.2 Think Big
- 8.3 Training and Test Samples
- 8.4 Evaluating the Model
- 8.5 Updating or Refreshing the Model
- 8.6 Caveat Emptor
- Chapter 9: Operationally Actionable Output
- 9.1 Actionable Output
- Applications
- Chapter 10: Normal Crime
- 10.1 Knowing Normal
- 10.2 “Normal” Criminal Behavior
- 10.3 Get to Know “Normal” Crime Trends and Patterns
- 10.4 Staged Crime
- Chapter 11: Behavioral Analysis of Violent Crime
- 11.1 Case-Based Reasoning
- 11.2 Homicide
- 11.3 Strategic Characterization
- 11.4 Automated Motive Determination
- 11.5 Drug-Related Violence
- 11.6 Aggravated Assault
- 11.7 Sexual Assault
- 11.8 Victimology
- 11.9 Moving from Investigation to Prevention
- Chapter 12: Risk and Threat Assessment
- 12.1 Risk-Based Deployment
- 12.2 Experts versus Expert Systems
- 12.3 “Normal” Crime
- 12.4 Surveillance Detection
- 12.5 Strategic Characterization
- 12.6 Vulnerable Locations
- 12.7 Schools
- 12.8 Data
- 12.9 Accuracy versus Generalizability
- 12.10 “Cost” Analysis
- 12.11 Evaluation
- 12.12 Output
- 12.13 Novel Approaches to Risk and Threat Assessment
- Case Examples
- Chapter 13: Deployment
- 13.1 Patrol Services
- 13.2 Structuring Patrol Deployment
- 13.3 Data
- 13.4 How To
- 13.5 Tactical Deployment
- 13.6 Risk-Based Deployment Overview
- 13.7 Operationally Actionable Output
- 13.8 Risk-Based Deployment Case Studies7
- Chapter 14: Surveillance Detection
- 14.1 Surveillance Detection and Other Suspicious Situations
- 14.2 Natural Surveillance
- 14.3 Location, Location, Location
- 14.4 More Complex Surveillance Detection
- 14.5 Internet Surveillance Detection
- 14.6 How To
- 14.7 Summary
- Advanced Concepts and Future Trends
- Chapter 15: Advanced Topics
- 15.1 Intrusion Detection
- 15.2 Identify Theft
- 15.3 Syndromic Surveillance
- 15.4 Data Collection, Fusion and Preprocessing
- 15.5 Text Mining
- 15.6 Fraud Detection
- 15.7 Consensus Opinions
- 15.8 Expert Options
- Chapter 16: Future Trends
- 16.1 Text Mining
- 16.2 Fusion Centers
- 16.3 “Functional” Interoperability
- 16.4 “Virtual” Warehouses
- 16.5 Domain-Specific Tools
- 16.6 Closing Thoughts
- Index
- Edition: 1
- Published: September 15, 2006
- No. of pages (eBook): 368
- Imprint: Butterworth-Heinemann
- Language: English
- Paperback ISBN: 9780750677967
- eBook ISBN: 9780080464626
CM
Colleen McCue
Dr. Colleen McCue is the Senior Director of Social Science and Quantitative Methods at DigitalGlobe. Her areas of expertise within , in the applied public safety and national security environment include the application of data mining and predictive analytics to the analysis of crime and intelligence data, with particular emphasis on deployment strategies; surveillance detection; threat and vulnerability assessment; geospatial predictive analytics; computational modeling and visualization of human behavior; Human, Social, Culture and Behavior (HSCB) modeling and analysis; crisis and conflict mapping; and the behavioral analysis of violent crime in support of anticipation and influence.
Affiliations and expertise
Program Manager, Richmond Police Department, Richmond, VA, USARead Data Mining and Predictive Analysis on ScienceDirect