Data mining and predictive analysis : intelligence gathering and crime analysis / Colleen McCue.

Format:

Book

Author/Creator:

McCue, Colleen.

Language:

English

Subjects (All):

Crime analysis.

Data mining.

Law enforcement--Data processing.

Law enforcement.

Criminal behavior, Prediction of.

Physical Description:

XXVII, 393 p.; 24 cm.

Edition:

1st ed.

Other Title:

Intelligence gathering and crime analysis

Place of Publication:

Amsterdam ; Boston : Butterworth-Heinemann, c2007.

Summary:

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 executi

Contents:

Front Cover

Title page

Copyright Page

Table of Contents

Foreword

Preface

Introduction

How To Use This Book

Bibliography

Introductory Section

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

1.9 Bibliography

2 Domain Expertise

2.1 Domain Expertise

2.2 Domain Expertise for Analysts

2.3 Compromise

2.4 Analyze Your Own Data

2.5 Bibliography

3 Data Mining

3.1 Discovery and Prediction

3.2 Confirmation and Discovery

3.3 Surprise

3.4 Characterization

3.5 "Volume Challenge"

3.6 Exploratory Graphics and Data Exploration

3.7 Link Analysis

3.8 Nonobvious Relationship Analysis (NORA)

3.9 Text Mining

3.10 Future Trends

3.11 Bibliography

Methods

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

4.4 Bibliography

5 Data

5.1 Getting Started

5.2 Types of Data

5.3 Data

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

5.11 Bibliography

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

6.9 Bibliography

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 Techniques

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

7.23 Bibliography

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

8.7 Bibliography

9 Operationally Actionable Output

9.1 Actionable Output

Applications

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

10.5 Bibliography

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

11.10 Bibliography

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.

12.14 Bibliography

Case Examples

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 Studies

13.9 Bibliography

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

14.8 Bibliography

Advanced Concepts and Future Trends

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

15.9 Bibliography

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

16.7 Bibliography

Index.

Notes:

Includes bibliographical references and index.

ISBN:

9786610636501

9781280636509

1280636505

9780080464626

0080464629

OCLC:

271426919

Publisher Number:

9780750677967