Data Mining and Predictive Analysis : Intelligence Gathering And Crime Analysis

Foreword xiii Preface xv Introduction xxv Introductory Section 1(42) Basics 3(16) Basic Statistics 3(1) Inferential versus Descriptive Statistics and Data Mining 4(1) Population versus Samples 4(2) Modeling 6(1) Errors 7(7) Overfitting the Model 14(1) Generalizability versus Accuracy 14(3) Input/Output 17(1) Bibliography 18(1) Domain Expertise 19(6) Domain Expertise 19(1) Domain Expertise for Analysts 20(2) Compromise 22(2) Analyze Your Own Data 24(1) Bibliography 24(1) Data Mining 25(18) Discovery and Prediction 27(1) Confirmation and Discovery 28(2) Surprise 30(1) Characterization 31(1) ``Volume Challenge'' 32(1) Exploratory Graphics and Data Exploration 33(4) Link Analysis 37(1) Nonobvious Relationship Analysis (NORA) 37(2) Text Mining 39(1) Future Trends 40(1) Bibliography 40(3) Methods 43(132) Process Models for Data Mining and Analysis 45(22) CIA Intelligence Process 47(2) CRISP-DM 49(4) Actionable Mining and Predictive Analysis for Public Safety and Security 53(12) Bibliography 65(2) Data 67(26) Getting Started 69(1) Types of Data 69(1) Data 70(1) Types of Data Resources 71(11) Data Challenges 82(5) How Do We Overcome These Potential Barriers? 87(1) Duplication 88(1) Merging Data Resources 89(1) Public Health Data 90(1) Weather and Crime Data 90(1) Bibliography 91(2) Operationally Relevant Preprocessing 93(24) Operationally Relevant Recoding 93(1) Trinity Sight 94(6) Duplication 100(1) Data Imputation 100(1) Telephone Data 101(2) Conference Call Example 103(7) Internet Data 110(1) Operationally Relevant Variable Selection 111(3) Bibliography 114(3) Predictive Analytics 117(26) How to Select a Modeling Algorithm, Part I 117(1) Generalizability versus Accuracy 118(1) Link Analysis 119(1) Supervised versus Unsupervised Learning Techniques 119(2) Discriminant Analysis 121(1) Unsupervised Learning Algorithms 122(1) Neural Networks 123(2) Kohonan Network Models 125(1) How to Select a Modeling Algorithm, Part II 125(1) Combining Algorithms 126(1) Anomaly Detection 127(1) Internal Norms 127(1) Defining ``Normal'' 128(2) Deviations from Normal Patterns 130(1) Deviations from Normal Behavior 130(2) Warning! Screening versus Diagnostic 132(1) A Perfect World Scenario 133(2) Tools of the Trade 135(2) General Considerations and Some Expert Options 137(1) Variable Entry 138(1) Prior Probabilities 138(1) Costs 139(2) Bibliography 141(2) Public Safety---Specific Evaluation 143(22) Outcome Measures 144(5) Think Big 149(4) Training and Test Samples 153(5) Evaluating the Model 158(3) Updating or Refreshing the Model 161(1) Caveat Emptor 162(1) Bibliography 163(2) Operationally Actionable Output 165(10) Actionable Output 165(10) Applications 175(62) Normal Crime 177(10) Knowing Normal 178(3) ``Normal'' Criminal Behavior 181(1) Get to Know ``Normal'' Crime Trends and Patterns 182(1) Staged Crime 183(1) Bibliography 184(3) Behavioral Analysis of Violent Crime 187(28) Case-Based Reasoning 193(3) Homicide 196(3) Strategic Characterization 199(4) Automated Motive Determination 203(2) Drug-Related Violence 205(1) Aggravated Assault 205(1) Sexual Assault 206(2) Victimology 208(3) Moving from Investigation to Prevention 211(1) Bibliography 211(4) Risk and Threat Assessment 215(22) Risk-Based Deployment 217(1) Experts versus Expert Systems 218(1) ``Normal'' Crime 219(1) Surveillance Detection 219(1) Strategic Characterization 220(2) Vulnerable Locations 222(1) Schools 223(4) Data 227(1) Accuracy versus Generalizability 228(1) ``Cost'' Analysis 229(1) Evaluation 229(2) Output 231(1) Novel Approaches to Risk and Threat Assessment 232(2) Bibliography 234(3) Case Examples 237(62) Deployment 239(28) Patrol Services 240(1) Structuring Patrol Deployment 240(1) Data 241(5) How To 246(4) Tactical Deployment 250(1) Risk-Based Deployment Overview 251(1) Operationally Actionable Output 252(7) Risk-Based Deployment Case Studies 259(6) Bibliography 265(2) Surveillance Detection 267(32) Surveillance Detection and Other Suspicious Situations 267(3) Natural Surveillance 270(5) Location, Location, Location 275(7) More Complex Surveillance Detection 282(7) Internet Surveillance Detection 289(5) How To 294(2) Summary 296(1) Bibliography 297(2) Advanced Concepts and Future Trends 299(24) Advanced Topics 301(14) Intrusion Detection 301(1) Identify Theft 302(1) Syndromic Surveillance 303(1) Data Collection, Fusion and Preprocessing 303(3) Text Mining 306(2) Fraud Detection 308(2) Consensus Opinions 310(1) Expert Options 311(1) Bibliography 312(3) Future Trends 315(8) Text Mining 315(2) Fusion Centers 317(1) ``Functional'' Interoperability 318(1) ``Virtual'' Warehouses 318(1) Domain-Specific Tools 319(1) Closing Thoughts 319(2) Bibliography 321(2) Index 323