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. * 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
Author(s): Colleen McCue
Edition: 1
Year: 2007
Language: English
Pages: 368
Front Cover......Page 0
Data Mining and Predictive Analysis......Page 5
Copyright Page......Page 6
Contents......Page 9
Foreword......Page 15
Preface......Page 17
Introductory Section......Page 35
1.1 Basic Statistics......Page 37
1.3 Population versus Samples......Page 38
1.4 Modeling......Page 40
1.5 Errors......Page 41
1.7 Generalizability versus Accuracy......Page 48
1.8 Input/Output......Page 51
1.9 Bibliography......Page 52
2.1 Domain Expertise......Page 53
2.2 Domain Expertise for Analysts......Page 54
2.3 Compromise......Page 56
2.5 Bibliography......Page 58
3 Data Mining......Page 59
3.1 Discovery and Prediction......Page 61
3.2 Confirmation and Discovery......Page 62
3.3 Surprise......Page 64
3.4 Characterization......Page 65
3.5 “Volume Challenge”......Page 66
3.6 Exploratory Graphics and Data Exploration......Page 67
3.8 Nonobvious Relationship Analysis (NORA)......Page 71
3.9 Text Mining......Page 73
3.11 Bibliography......Page 74
Methods......Page 77
4 Process Models for Data Mining and Analysis......Page 79
4.1 CIA Intelligence Process......Page 81
4.2 CRISP-DM......Page 83
4.3 Actionable Mining and Predictive Analysis for Public Safety and Security......Page 87
4.4 Bibliography......Page 99
5 Data......Page 101
5.2 Types of Data......Page 103
5.3 Data......Page 104
5.4 Types of Data Resources......Page 105
5.5 Data Challenges......Page 116
5.6 How Do We Overcome These Potential Barriers?......Page 121
5.7 Duplication......Page 122
5.8 Merging Data Resources......Page 123
5.10 Weather and Crime Data......Page 124
5.11 Bibliography......Page 125
6.1 Operationally Relevant Recoding......Page 127
6.2 Trinity Sight......Page 128
6.4 Data Imputation......Page 134
6.5 Telephone Data......Page 135
6.6 Conference Call Example......Page 137
6.7 Internet Data......Page 144
6.8 Operationally Relevant Variable Selection......Page 145
6.9 Bibliography......Page 148
7.1 How to Select a Modeling Algorithm, Part I......Page 151
7.2 Generalizability versus Accuracy......Page 152
7.4 Supervised versus Unsupervised Learning Techniques......Page 153
7.5 Discriminant Analysis......Page 155
7.6 Unsupervised Learning Algorithms......Page 156
7.7 Neural Networks......Page 157
7.9 How to Select a Modeling Algorithm, Part II......Page 159
7.10 Combining Algorithms......Page 160
7.12 Internal Norms......Page 161
7.13 Defining “Normal”......Page 162
7.15 Deviations from Normal Behavior......Page 164
7.16 Warning! Screening versus Diagnostic......Page 166
7.17 A Perfect World Scenario......Page 167
7.18 Tools of the Trade......Page 169
7.19 General Considerations and Some Expert Options......Page 171
7.21 Prior Probabilities......Page 172
7.22 Costs......Page 173
7.23 Bibliography......Page 175
8 Public Safety–Speci.c Evaluation......Page 177
8.1 Outcome Measures......Page 178
8.2 Think Big......Page 183
8.3 Training and Test Samples......Page 187
8.4 Evaluating the Model......Page 192
8.5 Updating or Refreshing the Model......Page 195
8.6 Caveat Emptor......Page 196
8.7 Bibliography......Page 197
9.1 Actionable Output......Page 199
Applications......Page 209
10 Normal Crime......Page 211
10.1 Knowing Normal......Page 212
10.2 “Normal” Criminal Behavior......Page 215
10.3 Get to Know “Normal” Crime Trends and Patterns......Page 216
10.4 Staged Crime......Page 217
10.5 Bibliography......Page 218
11 Behavioral Analysis of Violent Crime......Page 221
11.1 Case-Based Reasoning......Page 227
11.2 Homicide......Page 230
11.3 Strategic Characterization......Page 233
11.4 Automated Motive Determination......Page 237
11.6 Aggravated Assault......Page 239
11.7 Sexual Assault......Page 240
11.8 Victimology......Page 242
11.10 Bibliography......Page 245
12 Risk and Threat Assessment......Page 249
12.1 Risk-Based Deployment......Page 251
12.2 Experts versus Expert Systems......Page 252
12.4 Surveillance Detection......Page 253
12.5 Strategic Characterization......Page 254
12.6 Vulnerable Locations......Page 256
12.7 Schools......Page 257
12.8 Data......Page 261
12.9 Accuracy versus Generalizability......Page 262
12.11 Evaluation......Page 263
12.12 Output......Page 265
12.13 Novel Approaches to Risk and Threat Assessment......Page 266
12.14 Bibliography......Page 268
Case Examples......Page 271
13 Deployment......Page 273
13.2 Structuring Patrol Deployment......Page 274
13.3 Data......Page 275
13.4 How To......Page 280
13.5 Tactical Deployment......Page 284
13.6 Risk-Based Deployment Overview......Page 285
13.7 Operationally Actionable Output......Page 286
13.8 Risk-Based Deployment Case Studies......Page 293
13.9 Bibliography......Page 299
14.1 Surveillance Detection and Other Suspicious Situations......Page 301
14.2 Natural Surveillance......Page 304
14.3 Location, Location, Location......Page 309
14.4 More Complex Surveillance Detection......Page 316
14.5 Internet Surveillance Detection......Page 323
14.6 How To......Page 328
14.7 Summary......Page 330
14.8 Bibliography......Page 331
Advanced Concepts and Future Trends......Page 333
15.1 Intrusion Detection......Page 335
15.2 Identify Theft......Page 336
15.4 Data Collection, Fusion and Preprocessing......Page 337
15.5 Text Mining......Page 340
15.6 Fraud Detection......Page 342
15.7 Consensus Opinions......Page 344
15.8 Expert Options......Page 345
15.9 Bibliography......Page 346
16.1 Text Mining......Page 349
16.2 Fusion Centers......Page 351
16.4 “Virtual” Warehouses......Page 352
16.6 Closing Thoughts......Page 353
16.7 Bibliography......Page 355
Index......Page 357
