Data Mining and Predictive Analysis - 2nd Edition - ISBN: 9780128002292, 9780128004081

Data Mining and Predictive Analysis

2nd Edition

Intelligence Gathering and Crime Analysis

Authors: Colleen McCue
eBook ISBN: 9780128004081
Paperback ISBN: 9780128002292
Imprint: Butterworth-Heinemann
Published Date: 6th January 2015
Page Count: 422
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Description

Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security analysis, and operational law enforcement settings.

This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics in intelligence and security analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment.

Key Features

  • Discusses new and emerging technologies and techniques, including up-to-date information on predictive policing, a key capability in law enforcement and security
  • Demonstrates the importance of analytic context beyond software
  • Covers new models for effective delivery of advanced analytics to the operational environment, which have increased access to even the most powerful capabilities
  • Includes terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis

Readership

Government agencies and institutions, crime and security analysts, managers and command staff making data mining purchasing decisions, data mining and artificial intelligence developers, private security consultants, legislators and policy makers.

Table of Contents

  • Dedication
  • Foreword
  • Preface
  • Digital Assets
  • Introduction
  • Part 1: Introductory Section
    • Chapter 1: Basics
      • Abstract
      • 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
      • Abstract
      • 2.1. Domain expertise
      • 2.2. Domain expertise for analysts
      • 2.3. The integrated model
    • Chapter 3: Data Mining and Predictive Analytics
      • Abstract
      • 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. Non-Obvious Relationship Analysis (NORA)
      • 3.9. Text mining
      • 3.10. Closing thoughts
  • Part 2: Methods
    • Chapter 4: Process Models for Data Mining and Predictive Analysis
      • Abstract
      • 4.1. CIA Intelligence Process
      • 4.2. Cross-industry Standard Process for Data Mining
      • 4.3. Sample
      • 4.4. Explore
      • 4.5. Modify
      • 4.6. Model
      • 4.7. Assess
      • 4.8. Actionable Mining and Predictive Analysis for Public Safety and Security
    • Chapter 5: Data
      • Abstract
      • 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?
    • Chapter 6: Operationally Relevant Preprocessing
      • Abstract
      • 6.1. Operationally relevant recoding
      • 6.2. When, where, what?
      • 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: Identification, Characterization, and Modeling
      • Abstract
      • 7.1. Predictive analytics
      • 7.2. How to select a modeling algorithm, part I
      • 7.3. Examples
      • 7.4. How to select a modeling algorithm, part II
      • 7.5. General considerations and some expert options
    • Chapter 8: Public-Safety-Specific Evaluation
      • Abstract
      • 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. There are no free lunches
    • Chapter 9: Operationally Actionable Output
      • Abstract
      • 9.1. Actionable output
      • 9.2. Geospatial capabilities and tools
      • 9.3. Other approaches
  • Part 3: Applications
    • Chapter 10: Normal Crime
      • Abstract
      • 10.1. Internal norms
      • 10.2. Knowing normal
      • 10.3. “Normal” criminal behavior
      • 10.4. Get to know “normal” crime trends and patterns
      • 10.5. Staged crime
    • Chapter 11: Behavioral Analysis of Violent Crime
      • Abstract
      • 11.1. Behavior 101
      • 11.2. Motive determination
      • 11.3. Behavioral segmentation
      • 11.4. Victimology
      • 11.5. Violent crimes
      • 11.6. Challenges
      • 11.7. Moving from investigation to prevention
    • Chapter 12: Risk and Threat Assessment
      • Abstract
      • 12.1. Basic concepts
      • 12.2. Vulnerable locations
      • 12.3. Process model considerations
      • 12.4. Examples
      • 12.5. Novel approaches to risk and threat assessment
  • Part 4: Case Examples
    • Chapter 13: Deployment
      • Abstract
      • 13.1. Risk-based deployment
      • 13.2. General concepts
      • 13.3. How to
      • 13.4. Risk-based deployment case studies
    • Chapter 14: Surveillance Detection
      • Abstract
      • 14.1. Surveillance detection and other suspicious situations
      • 14.2. General concepts
      • 14.3. How to
      • 14.4. Surveillance detection case studies
      • 14.5. Summary
  • Part 5: Advanced Concepts and Future Trends
    • Chapter 15: Advanced Topics
      • Abstract
      • 15.1. Additional “expert options”
      • 15.2. Unstructured data
      • 15.3. Geospatial capabilities and tools
      • 15.4. Social media
      • 15.5. Social network analysis
      • 15.6. Fraud detection
      • 15.7. Cyber
      • 15.8. Application to other/adjacent functional domains
      • 15.9. Summary
    • Chapter 16: Future Trends
      • Abstract
      • 16.1. [Really] big data
      • 16.2. Analysis
      • 16.3. Other uses
      • 16.4. Technology and tools
      • 16.5. Potential challenges and constraints
      • 16.6. Closing thoughts
  • Index

Details

No. of pages:
422
Language:
English
Copyright:
© Butterworth-Heinemann 2015
Published:
Imprint:
Butterworth-Heinemann
eBook ISBN:
9780128004081
Paperback ISBN:
9780128002292

About the Author

Colleen McCue

Colleen McCue

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.

Affiliations and Expertise

Program Manager, Richmond Police Department, Richmond, VA, USA

Reviews

"...presents a comprehensive overview of the field of predictive analysis as it is primarily applied to crime analysis, where it is an established law enforcement intelligence practice with great success…" --Journal of Counterterrorism and Homeland Security International

"[Data Mining and Predictive Analysis, First Edition] is a must-read..., blending analytical horsepower with real-life operational examples...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