Data Mining and Predictive Analysis

Intelligence Gathering and Crime Analysis

1st Edition - September 15, 2006
Author: Colleen McCue
Language: English
Paperback ISBN:
9 7 8 - 0 - 7 5 0 6 - 7 7 9 6 - 7
eBook ISBN:
9 7 8 - 0 - 0 8 - 0 4 6 4 6 2 - 6

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… Read more

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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.

Dedication
Foreword
Preface
Introduction
Introductory Section
Chapter 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

Chapter 2: Domain Expertise

2.1 Domain Expertise
2.2 Domain Expertise for Analysts
2.3 Compromise
2.4 Analyze Your Own Data

Chapter 3: Data Mining

3.1 Discovery and Prediction
3.2 Confirmation and Discovery
3.3 Surprise
3.4 Characterization
3.5 “Volume Challenge”6
3.6 Exploratory Graphics and Data Exploration
3.7 Link Analysis12
3.8 Nonobvious Relationship Analysis (NORA)13
3.9 Text Mining
3.10 Future Trends
Methods

Chapter 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

Chapter 5: Data

5.1 Getting Started
5.2 Types of Data
5.3 Data2
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

Chapter 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

Chapter 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 Techniques2
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

Chapter 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

Chapter 9: Operationally Actionable Output

9.1 Actionable Output
Applications

Chapter 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

Chapter 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

Chapter 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
Case Examples

Chapter 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 Studies7

Chapter 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
Advanced Concepts and Future Trends

Chapter 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

Chapter 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

Index