Front Cover -- Title page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Introduction -- How To Use This Book -- Bibliography -- Introductory Section -- 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 -- 1.9 Bibliography -- 2 Domain Expertise -- 2.1 Domain Expertise -- 2.2 Domain Expertise for Analysts -- 2.3 Compromise -- 2.4 Analyze Your Own Data -- 2.5 Bibliography -- 3 Data Mining -- 3.1 Discovery and Prediction -- 3.2 Confirmation and Discovery -- 3.3 Surprise -- 3.4 Characterization -- 3.5 "Volume Challenge" -- 3.6 Exploratory Graphics and Data Exploration -- 3.7 Link Analysis -- 3.8 Nonobvious Relationship Analysis (NORA) -- 3.9 Text Mining -- 3.10 Future Trends -- 3.11 Bibliography -- Methods -- 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 -- 4.4 Bibliography -- 5 Data -- 5.1 Getting Started -- 5.2 Types of Data -- 5.3 Data -- 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 -- 5.11 Bibliography -- 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 -- 6.9 Bibliography -- 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 Techniques -- 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 -- 7.23 Bibliography -- 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 -- 8.7 Bibliography -- 9 Operationally Actionable Output -- 9.1 Actionable Output -- Applications -- 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 -- 10.5 Bibliography -- 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 -- 11.10 Bibliography -- 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.

12.14 Bibliography -- Case Examples -- 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 Studies -- 13.9 Bibliography -- 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 -- 14.8 Bibliography -- Advanced Concepts and Future Trends -- 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 -- 15.9 Bibliography -- 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 -- 16.7 Bibliography -- Index.