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Data Mining and Predictive Analysis : Intelligence Gathering and Crime Analysis - McCue

Data Mining and Predictive Analysis

Intelligence Gathering and Crime Analysis

By: McCue

Paperback

Published: 15th September 2006
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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

"[Data Mining and Predictive Analysis] is a must-read..., blending analytical horsepower with real-life operational examples. Operators owe it to themselves to dig in and make tactical decisions more efficiently, and learn the language that sells good tactics to leadership. Analysts, intell support, and leaders owe it to themselves to learn a new way to attack the problem in support of law enforcement, security, and intelligence operations. Not just a dilettante academic, Dr. McCue is passionate about getting the best tactical solution in the most efficient way-and she uses data mining to do it. Understandable yet detailed, [Data Mining and Predictive Analysis] puts forth a solid argument for integrating predictive analytics into action. Not just for analysts!" - Tim King (Director, Special Programs and Global Business Development, ArmorGroup International Training)

Forewordp. xiii
Prefacep. xv
Introductionp. xxv
Introductory Sectionp. 1
Basicsp. 3
Basic Statisticsp. 3
Inferential versus Descriptive Statistics and Data Miningp. 4
Population versus Samplesp. 4
Modelingp. 6
Errorsp. 7
Overfitting the Modelp. 14
Generalizability versus Accuracyp. 14
Input/Outputp. 17
Bibliographyp. 18
Domain Expertisep. 1
Domain Expertisep. 19
Domain Expertise for Analystsp. 20
Compromisep. 22
Analyze Your Own Datap. 24
Bibliographyp. 24
Data Miningp. 25
Discovery and Predictionp. 27
Confirmation and Discoveryp. 28
Surprisep. 30
Characterizationp. 31
"Volume Challenge"p. 32
Exploratory Graphics and Data Explorationp. 33
Link Analysisp. 37
Nonobvious Relationship Analysis (NORA)p. 37
Text Miningp. 39
Future Trendsp. 40
Bibliographyp. 40
Methodsp. 43
Process Models for Data Mining and Analysisp. 45
CIA Intelligence Processp. 47
CRISP-DMp. 49
Actionable Mining and Predictive Analysis for Public Safety and Securityp. 53
Bibliographyp. 65
Datap. 67
Getting Startedp. 69
Types of Datap. 69
Datap. 70
Types of Data Resourcesp. 71
Data Challengesp. 82
How Do We Overcome These Potential Barriers?p. 87
Duplicationp. 88
Merging Data Resourcesp. 89
Public Health Datap. 90
Weather and Crime Datap. 90
Bibliographyp. 91
Operationally Relevant Preprocessingp. 93
Operationally Relevant Recodingp. 93
Trinity Sightp. 94
Duplicationp. 100
Data Imputationp. 100
Telephone Datap. 101
Conference Call Examplep. 103
Internet Datap. 110
Operationally Relevant Variable Selectionp. 111
Bibliographyp. 114
Predictive Analyticsp. 117
How to Select a Modeling Algorithm, Part Ip. 117
Generalizability versus Accuracyp. 118
Link Analysisp. 119
Supervised versus Unsupervised Learning Techniquesp. 119
Discriminant Analysisp. 121
Unsupervised Learning Algorithmsp. 122
Neural Networksp. 123
Kohonan Network Modelsp. 125
How to Select a Modeling Algorithm, Part IIp. 125
Combining Algorithmsp. 126
Anomaly Detectionp. 127
Internal Normsp. 127
Defining "Normal"p. 128
Deviations from Normal Patternsp. 130
Deviations from Normal Behaviorp. 130
Warning! Screening versus Diagnosticp. 132
A Perfect World Scenariop. 133
Tools of the Tradep. 135
General Considerations and Some Expert Optionsp. 137
Variable Entryp. 138
Prior Probabilitiesp. 138
Costsp. 139
Bibliographyp. 141
Public Safety-Specific Evaluationp. 143
Outcome Measuresp. 144
Think Bigp. 149
Training and Test Samplesp. 153
Evaluating the Modelp. 158
Updating or Refreshing the Modelp. 161
Caveat Emptorp. 162
Bibliographyp. 163
Operationally Actionable Outputp. 165
Actionable Outputp. 165
Applicationsp. 175
Normal Crimep. 177
Knowing Normalp. 178
"Normal" Criminal Behaviorp. 181
Get to Know "Normal" Crime Trends and Patternsp. 182
Staged Crimep. 183
Bibliographyp. 184
Behavioral Analysis of Violent Crimep. 187
Case-Based Reasoningp. 193
Homicidep. 196
Strategic Characterizationp. 199
Automated Motive Determinationp. 203
Drug-Related Violencep. 205
Aggravated Assaultp. 205
Sexual Assaultp. 206
Victimologyp. 208
Moving from Investigation to Preventionp. 211
Bibliographyp. 211
Risk and Threat Assessmentp. 215
Risk-Based Deploymentp. 217
Experts versus Expert Systemsp. 218
"Normal" Crimep. 219
Surveillance Detectionp. 219
Strategic Characterizationp. 220
Vulnerable Locationsp. 222
Schoolsp. 223
Datap. 227
Accuracy versus Generalizabilityp. 228
"Cost" Analysisp. 229
Evaluationp. 229
Outputp. 231
Novel Approaches to Risk and Threat Assessmentp. 232
Bibliographyp. 234
Case Examplesp. 237
Deploymentp. 239
Patrol Servicesp. 240
Structuring Patrol Deploymentp. 240
Datap. 241
How Top. 246
Tactical Deploymentp. 250
Risk-Based Deployment Overviewp. 251
Operationally Actionable Outputp. 252
Risk-Based Deployment Case Studiesp. 259
Bibliographyp. 265
Surveillance Detectionp. 267
Surveillance Detection and Other Suspicious Situationsp. 267
Natural Surveillancep. 270
Location, Location, Locationp. 275
More Complex Surveillance Detectionp. 282
Internet Surveillance Detectionp. 289
How Top. 294
Summaryp. 296
Bibliographyp. 297
Advanced Concepts and Future Trendsp. 299
Advanced Topicsp. 301
Intrusion Detectionp. 301
Identify Theftp. 302
Syndromic Surveillancep. 303
Data Collection, Fusion and Preprocessingp. 303
Text Miningp. 306
Fraud Detectionp. 308
Consensus Opinionsp. 310
Expert Optionsp. 311
Bibliographyp. 312
Future Trendsp. 315
Text Miningp. 315
Fusion Centersp. 317
"Functional" Interoperabilityp. 318
"Virtual" Warehousesp. 318
Domain-Specific Toolsp. 319
Closing Thoughtsp. 319
Bibliographyp. 321
Indexp. 323
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780750677967
ISBN-10: 0750677961
Audience: Professional
Format: Paperback
Language: English
Number Of Pages: 368
Published: 15th September 2006
Publisher: Elsevier Science & Technology
Country of Publication: GB
Dimensions (cm): 23.5 x 19.1  x 1.91
Weight (kg): 0.87