Data Mining and Predictive Analysis Intelligence Gathering and Crime Analysis

ISBN-10: 0750677961
ISBN-13: 9780750677967
Edition: 2006
Authors: Colleen McCue
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Description: Data Mining and Predictive Analysis contains a comprehensive overview of data mining and predictive analytics in crime and intelligence analysis, and underscores the benefits of increased use of data mining among government and law enforcement  More...

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Book details

List price: $67.95
Copyright year: 2006
Publisher: Elsevier Science & Technology Books
Publication date: 9/15/2006
Binding: Paperback
Pages: 368
Size: 7.25" wide x 9.00" long x 0.75" tall
Weight: 1.958
Language: English

Data Mining and Predictive Analysis contains a comprehensive overview of data mining and predictive analytics in crime and intelligence analysis, and underscores the benefits of increased use of data mining among government and law enforcement agencies. Covering such topics as how to map criminal acts, detect possible patterns, and use predictive tools to prevent future crimes and/or terrorist acts from occurring, the books cuts through the technical language of other books on data mining, making it a useful training tool for new officers and agents entering the field. The book is written as a primer for those with little formal statistical training, but assumes knowledge of crime and intelligence analysis and a desire to learn data mining. By providing a background on data mining and predictive analysis, the book will enable law enforcement managers and command staff to make informed decisions about the potential, acquisition, and use of these products and techniques. The book also demonstrates how data mining and predictive analytics can be applied to a variety of common and emerging questions that crime and intelligence analysts encounter. Finally, the book serves as a valuable companion document to any software-specific training manual or course, should the analyst decide to pursue additional training in this area. - Use of actual cases, briefly summarized and illustrated to emphasize key principles - Numerous illustrations, data examples, and screen shots illustrate key points - A non-technical reference that explains both basic and complex concepts of the underlying fundamentals of data mining use in criminal detection

Dr. Colleen McCue is the Senior Director of Social Science and Quantitative Methods at DigitalGlobe. Her areas of expertise within , in the applied public safety and national security environment include the application of data mining and predictive analytics to the analysis of crime and intelligence data, with particular emphasis on deployment strategies; surveillance detection; threat and vulnerability assessment; geospatial predictive analytics; computational modeling and visualization of human behavior; Human, Social, Culture and Behavior (HSCB) modeling and analysis; crisis and conflict mapping; and the behavioral analysis of violent crime in support of anticipation and influence.

Foreword
Preface
Introduction
Introductory Section
Basics
Basic Statistics
Inferential versus Descriptive Statistics and Data Mining
Population versus Samples
Modeling
Errors
Overfitting the Model
Generalizability versus Accuracy
Input/Output
Bibliography
Domain Expertise
Domain Expertise
Domain Expertise for Analysts
Compromise
Analyze Your Own Data
Bibliography
Data Mining
Discovery and Prediction
Confirmation and Discovery
Surprise
Characterization
"Volume Challenge"
Exploratory Graphics and Data Exploration
Link Analysis
Nonobvious Relationship Analysis (NORA)
Text Mining
Future Trends
Bibliography
Methods
Process Models for Data Mining and Analysis
CIA Intelligence Process
CRISP-DM
Actionable Mining and Predictive Analysis for Public Safety and Security
Bibliography
Data
Getting Started
Types of Data
Data
Types of Data Resources
Data Challenges
How Do We Overcome These Potential Barriers?
Duplication
Merging Data Resources
Public Health Data
Weather and Crime Data
Bibliography
Operationally Relevant Preprocessing
Operationally Relevant Recoding
Trinity Sight
Duplication
Data Imputation
Telephone Data
Conference Call Example
Internet Data
Operationally Relevant Variable Selection
Bibliography
Predictive Analytics
How to Select a Modeling Algorithm, Part I
Generalizability versus Accuracy
Link Analysis
Supervised versus Unsupervised Learning Techniques
Discriminant Analysis
Unsupervised Learning Algorithms
Neural Networks
Kohonan Network Models
How to Select a Modeling Algorithm, Part II
Combining Algorithms
Anomaly Detection
Internal Norms
Defining "Normal"
Deviations from Normal Patterns
Deviations from Normal Behavior
Warning! Screening versus Diagnostic
A Perfect World Scenario
Tools of the Trade
General Considerations and Some Expert Options
Variable Entry
Prior Probabilities
Costs
Bibliography
Public Safety-Specific Evaluation
Outcome Measures
Think Big
Training and Test Samples
Evaluating the Model
Updating or Refreshing the Model
Caveat Emptor
Bibliography
Operationally Actionable Output
Actionable Output
Applications
Normal Crime
Knowing Normal
"Normal" Criminal Behavior
Get to Know "Normal" Crime Trends and Patterns
Staged Crime
Bibliography
Behavioral Analysis of Violent Crime
Case-Based Reasoning
Homicide
Strategic Characterization
Automated Motive Determination
Drug-Related Violence
Aggravated Assault
Sexual Assault
Victimology
Moving from Investigation to Prevention
Bibliography
Risk and Threat Assessment
Risk-Based Deployment
Experts versus Expert Systems
"Normal" Crime
Surveillance Detection
Strategic Characterization
Vulnerable Locations
Schools
Data
Accuracy versus Generalizability
"Cost" Analysis
Evaluation
Output
Novel Approaches to Risk and Threat Assessment
Bibliography
Case Examples
Deployment
Patrol Services
Structuring Patrol Deployment
Data
How To
Tactical Deployment
Risk-Based Deployment Overview
Operationally Actionable Output
Risk-Based Deployment Case Studies
Bibliography
Surveillance Detection
Surveillance Detection and Other Suspicious Situations
Natural Surveillance
Location, Location, Location
More Complex Surveillance Detection
Internet Surveillance Detection
How To
Summary
Bibliography
Advanced Concepts and Future Trends
Advanced Topics
Intrusion Detection
Identify Theft
Syndromic Surveillance
Data Collection, Fusion and Preprocessing
Text Mining
Fraud Detection
Consensus Opinions
Expert Options
Bibliography
Future Trends
Text Mining
Fusion Centers
"Functional" Interoperability
"Virtual" Warehouses
Domain-Specific Tools
Closing Thoughts
Bibliography
Index

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