Data mining and predictive analysis : intelligence gathering and crime analysis

Title:

Personal Author:

McCue, Colleen

Publication Information:

Amsterdam : Butterworth-Heinemann, 2007

ISBN:

9780750677967

Subject Term:

Crime analysis

Data mining

Law enforcement -- Data processing

Criminal behavior, Prediction of

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010124604	HV7936.C88 M38 2007	Open Access Book	Book	Searching... Unknown

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.

Author Notes

Colleen "Kelly" McCue, PhD, is a Senior Research Scientist at RTI International

Foreword	p. xiii
Preface	p. xv
Introduction	p. xxv
Introductory Section	p. 1
1 Basics	p. 3
1.1 Basic Statistics	p. 3
1.2 Inferential versus Descriptive Statistics and Data Mining	p. 4
1.3 Population versus Samples	p. 4
1.4 Modeling	p. 6
1.5 Errors	p. 7
1.6 Overfitting the Model	p. 14
1.7 Generalizability versus Accuracy	p. 14
1.8 Input/Output	p. 17
1.9 Bibliography	p. 18
2 Domain Expertise	p. 1
2.1 Domain Expertise	p. 19
2.2 Domain Expertise for Analysts	p. 20
2.3 Compromise	p. 22
2.4 Analyze Your Own Data	p. 24
2.5 Bibliography	p. 24
3 Data Mining	p. 25
3.1 Discovery and Prediction	p. 27
3.2 Confirmation and Discovery	p. 28
3.3 Surprise	p. 30
3.4 Characterization	p. 31
3.5 "Volume Challenge"	p. 32
3.6 Exploratory Graphics and Data Exploration	p. 33
3.7 Link Analysis	p. 37
3.8 Nonobvious Relationship Analysis (NORA)	p. 37
3.9 Text Mining	p. 39
3.10 Future Trends	p. 40
3.11 Bibliography	p. 40
Methods	p. 43
4 Process Models for Data Mining and Analysis	p. 45
4.1 CIA Intelligence Process	p. 47
4.2 CRISP-DM	p. 49
4.3 Actionable Mining and Predictive Analysis for Public Safety and Security	p. 53
4.4 Bibliography	p. 65
5 Data	p. 67
5.1 Getting Started	p. 69
5.2 Types of Data	p. 69
5.3 Data	p. 70
5.4 Types of Data Resources	p. 71
5.5 Data Challenges	p. 82
5.6 How Do We Overcome These Potential Barriers?	p. 87
5.7 Duplication	p. 88
5.8 Merging Data Resources	p. 89
5.9 Public Health Data	p. 90
5.10 Weather and Crime Data	p. 90
5.11 Bibliography	p. 91
6 Operationally Relevant Preprocessing	p. 93
6.1 Operationally Relevant Recoding	p. 93
6.2 Trinity Sight	p. 94
6.3 Duplication	p. 100
6.4 Data Imputation	p. 100
6.5 Telephone Data	p. 101
6.6 Conference Call Example	p. 103
6.7 Internet Data	p. 110
6.8 Operationally Relevant Variable Selection	p. 111
6.9 Bibliography	p. 114
7 Predictive Analytics	p. 117
7.1 How to Select a Modeling Algorithm, Part I	p. 117
7.2 Generalizability versus Accuracy	p. 118
7.3 Link Analysis	p. 119
7.4 Supervised versus Unsupervised Learning Techniques	p. 119
7.5 Discriminant Analysis	p. 121
7.6 Unsupervised Learning Algorithms	p. 122
7.7 Neural Networks	p. 123
7.8 Kohonan Network Models	p. 125
7.9 How to Select a Modeling Algorithm, Part II	p. 125
7.10 Combining Algorithms	p. 126
7.11 Anomaly Detection	p. 127
7.12 Internal Norms	p. 127
7.13 Defining "Normal"	p. 128
7.14 Deviations from Normal Patterns	p. 130
7.15 Deviations from Normal Behavior	p. 130
7.16 Warning! Screening versus Diagnostic	p. 132
7.17 A Perfect World Scenario	p. 133
7.18 Tools of the Trade	p. 135
7.19 General Considerations and Some Expert Options	p. 137
7.20 Variable Entry	p. 138
7.21 Prior Probabilities	p. 138
7.22 Costs	p. 139
7.23 Bibliography	p. 141
8 Public Safety-Specific Evaluation	p. 143
8.1 Outcome Measures	p. 144
8.2 Think Big	p. 149
8.3 Training and Test Samples	p. 153
8.4 Evaluating the Model	p. 158
8.5 Updating or Refreshing the Model	p. 161
8.6 Caveat Emptor	p. 162
8.7 Bibliography	p. 163
9 Operationally Actionable Output	p. 165
9.1 Actionable Output	p. 165
Applications	p. 175
10 Normal Crime	p. 177
10.1 Knowing Normal	p. 178
10.2 "Normal" Criminal Behavior	p. 181
10.3 Get to Know "Normal" Crime Trends and Patterns	p. 182
10.4 Staged Crime	p. 183
10.5 Bibliography	p. 184
11 Behavioral Analysis of Violent Crime	p. 187
11.1 Case-Based Reasoning	p. 193
11.2 Homicide	p. 196
11.3 Strategic Characterization	p. 199
11.4 Automated Motive Determination	p. 203
11.5 Drug-Related Violence	p. 205
11.6 Aggravated Assault	p. 205
11.7 Sexual Assault	p. 206
11.8 Victimology	p. 208
11.9 Moving from Investigation to Prevention	p. 211
11.10 Bibliography	p. 211
12 Risk and Threat Assessment	p. 215
12.1 Risk-Based Deployment	p. 217
12.2 Experts versus Expert Systems	p. 218
12.3 "Normal" Crime	p. 219
12.4 Surveillance Detection	p. 219
12.5 Strategic Characterization	p. 220
12.6 Vulnerable Locations	p. 222
12.7 Schools	p. 223
12.8 Data	p. 227
12.9 Accuracy versus Generalizability	p. 228
12.10 "Cost" Analysis	p. 229
12.11 Evaluation	p. 229
12.12 Output	p. 231
12.13 Novel Approaches to Risk and Threat Assessment	p. 232
12.14 Bibliography	p. 234
Case Examples	p. 237
13 Deployment	p. 239
13.1 Patrol Services	p. 240
13.2 Structuring Patrol Deployment	p. 240
13.3 Data	p. 241
13.4 How To	p. 246
13.5 Tactical Deployment	p. 250
13.6 Risk-Based Deployment Overview	p. 251
13.7 Operationally Actionable Output	p. 252
13.8 Risk-Based Deployment Case Studies	p. 259
13.9 Bibliography	p. 265
14 Surveillance Detection	p. 267
14.1 Surveillance Detection and Other Suspicious Situations	p. 267
14.2 Natural Surveillance	p. 270
14.3 Location, Location, Location	p. 275
14.4 More Complex Surveillance Detection	p. 282
14.5 Internet Surveillance Detection	p. 289
14.6 How To	p. 294
14.7 Summary	p. 296
14.8 Bibliography	p. 297
Advanced Concepts and Future Trends	p. 299
15 Advanced Topics	p. 301
15.1 Intrusion Detection	p. 301
15.2 Identify Theft	p. 302
15.3 Syndromic Surveillance	p. 303
15.4 Data Collection, Fusion and Preprocessing	p. 303
15.5 Text Mining	p. 306
15.6 Fraud Detection	p. 308
15.7 Consensus Opinions	p. 310
15.8 Expert Options	p. 311
15.9 Bibliography	p. 312
16 Future Trends	p. 315
16.1 Text Mining	p. 315
16.2 Fusion Centers	p. 317
16.3 "Functional" Interoperability	p. 318
16.4 "Virtual" Warehouses	p. 318
16.5 Domain-Specific Tools	p. 319
16.6 Closing Thoughts	p. 319
16.7 Bibliography	p. 321
Index	p. 323

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Author Notes

Table of Contents