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E-raamat: Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis

(Program Manager, Richmond Police Department, Richmond, VA, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 30-Dec-2014
  • Kirjastus: Butterworth-Heinemann Inc
  • Keel: eng
  • ISBN-13: 9780128004081
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 30-Dec-2014
  • Kirjastus: Butterworth-Heinemann Inc
  • Keel: eng
  • ISBN-13: 9780128004081
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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

      Arvustused

      "...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

      Muu info

      Cuts through technical language to provide an ideal primer for those looking to use data mining in crime and intelligence analysis.
      Foreword xi
      Preface xiii
      Digital Assets xxi
      Introduction xxiii
      Part 1 Introductory Section
      Chapter 1 Basics
      3(22)
      1.1 Basic statistics
      3(1)
      1.2 Inferential versus descriptive statistics and data mining
      4(1)
      1.3 Population versus samples
      4(3)
      1.4 Modeling
      7(1)
      1.5 Errors
      8(9)
      1.6 Overfitting the model
      17(1)
      1.7 Generalizability versus accuracy
      18(2)
      1.8 Input/output
      20(5)
      Chapter 2 Domain expertise
      25(6)
      2.1 Domain expertise
      25(1)
      2.2 Domain expertise for analysts
      26(2)
      2.3 The integrated model
      28(3)
      Chapter 3 Data mining and predictive analytics
      31(20)
      3.1 Discovery and prediction
      33(1)
      3.2 Confirmation and discovery
      34(1)
      3.3 Surprise
      35(1)
      3.4 Characterization
      36(1)
      3.5 "Volume challenge"
      37(1)
      3.6 Exploratory graphics and data exploration
      38(5)
      3.7 Link analysis
      43(2)
      3.8 Non-obvious relationship analysis (NORA)
      45(1)
      3.9 Text mining
      45(1)
      3.10 Closing thoughts
      46(5)
      Part 2 Methods
      Chapter 4 Process models for data mining and predictive analysis
      51(24)
      4.1 CIA intelligence process
      53(2)
      4.2 Cross-industry standard process for data mining
      55(3)
      4.3 Sample
      58(1)
      4.4 Explore
      58(1)
      4.5 Modify
      58(1)
      4.6 Model
      58(1)
      4.7 Assess
      58(1)
      4.8 Actionable mining and predictive analysis for public safety and security
      59(16)
      Chapter 5 Data
      75(32)
      5.1 Getting started
      77(1)
      5.2 Types of data
      77(1)
      5.3 Data
      77(4)
      5.4 Types of data resources
      81(16)
      5.5 Data challenges
      97(6)
      5.6 How do we overcome these potential barriers?
      103(4)
      Chapter 6 Operationally relevant preprocessing
      107(30)
      6.1 Operationally relevant recoding
      109(1)
      6.2 When, where, what?
      110(9)
      6.3 Duplication
      119(1)
      6.4 Data imputation
      120(1)
      6.5 Telephone data
      120(2)
      6.6 Conference call example
      122(7)
      6.7 Internet data
      129(2)
      6.8 Operationally relevant variable selection
      131(6)
      Chapter 7 Identification, characterization, and modeling
      137(20)
      7.1 Predictive analytics
      137(2)
      7.2 How to select a modeling algorithm, part I
      139(3)
      7.3 Examples
      142(3)
      7.4 How to select a modeling algorithm, part II
      145(4)
      7.5 General considerations and some expert options
      149(8)
      Chapter 8 Public-safety-specific evaluation
      157(28)
      8.1 Outcome measures
      160(9)
      8.2 Think big
      169(4)
      8.3 Training and test samples
      173(4)
      8.4 Evaluating the model
      177(4)
      8.5 Updating or refreshing the model
      181(1)
      8.6 There are no free lunches
      181(4)
      Chapter 9 Operationally actionable output
      185(26)
      9.1 Actionable output
      186(7)
      9.2 Geospatial capabilities and tools
      193(5)
      9.3 Other approaches
      198(13)
      Part 3 Applications
      Chapter 10 Normal crime
      211(12)
      10.1 Internal norms
      212(1)
      10.2 Knowing normal
      213(2)
      10.3 "Normal" criminal behavior
      215(1)
      10.4 Get to know "norma" crime trends and patterns
      216(3)
      10.5 Staged crime
      219(4)
      Chapter 11 Behavioral analysis of violent crime
      223(34)
      11.1 Behavior 101
      227(5)
      11.2 Motive determination
      232(4)
      11.3 Behavioral segmentation
      236(5)
      11.4 Victimology
      241(2)
      11.5 Violent crimes
      243(7)
      11.6 Challenges
      250(3)
      11.7 Moving from investigation to prevention
      253(4)
      Chapter 12 Risk and threat assessment
      257(28)
      12.1 Basic concepts
      259(3)
      12.2 Vulnerable locations
      262(4)
      12.3 Process model considerations
      266(5)
      12.4 Examples
      271(8)
      12.5 Novel approaches to risk and threat assessment
      279(6)
      Part 4 Case Examples
      Chapter 13 Deployment
      285(28)
      13.1 Risk-based deployment
      286(1)
      13.2 General concepts
      286(4)
      13.3 How to
      290(5)
      13.4 Risk-based deployment case studies
      295(18)
      Chapter 14 Surveillance detection
      313(36)
      14.1 Surveillance detection and other suspicious situations
      314(1)
      14.2 General concepts
      315(2)
      14.3 How to
      317(11)
      14.4 Surveillance detection case studies
      328(15)
      14.5 Summary
      343(6)
      Part 5 Advanced Concepts and Future Trends
      Chapter 15 Advanced topics
      349(18)
      15.1 Additional "expert options"
      349(1)
      15.2 Unstructured data
      350(1)
      15.3 Geospatial capabilities and tools
      351(2)
      15.4 Social media
      353(2)
      15.5 Social network analysis
      355(1)
      15.6 Fraud detection
      356(2)
      15.7 Cyber
      358(2)
      15.8 Application to other/adjacent functional domains
      360(3)
      15.9 Summary
      363(4)
      Chapter 16 Future trends
      367(18)
      16.1 [ Really] big data
      367(2)
      16.2 Analysis
      369(2)
      16.3 Other uses
      371(1)
      16.4 Technology and tools
      372(1)
      16.5 Potential challenges and constraints
      373(8)
      16.6 Closing thoughts
      381(4)
      Index 385
      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.