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Data Mining and Predictive Analysis
Intelligence Gathering and Crime Analysis
2nd Edition - December 30, 2014
Author: Colleen McCue
Language: English
Paperback ISBN:9780128002292
9 7 8 - 0 - 1 2 - 8 0 0 2 2 9 - 2
eBook ISBN:9780128004081
9 7 8 - 0 - 1 2 - 8 0 0 4 0 8 - 1
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 d…Read more
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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.
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
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.
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
No. of pages: 422
Language: English
Edition: 2
Published: December 30, 2014
Imprint: Butterworth-Heinemann
Paperback ISBN: 9780128002292
eBook ISBN: 9780128004081
CM
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
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