UID:
edoccha_9958115514302883
Format:
1 online resource (422 p.)
Edition:
2nd ed.
ISBN:
0-12-800408-8
Content:
* Serves as a valuable reference tool for both the student and the law enforcement professional〈br〉* Contains practical information used in real-life law enforcement situations〈br〉* Approach is very user-friendly, conveying sophisticated analyses in practical terms
Note:
Includes index.
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Cover; Title Page; Copyright Page; Dedication; Contents; Foreword; Preface; Digital Assets; For the instructor; Introduction; Skill set; How to use this book; Part 1 - Introductory Section; Chapter 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; 1.5.1 - Infrequent Events; 1.5.2 - "Black Swans"; 1.5.3 - Identifying Appropriate Comparison Groups and Establishing the Denominator; 1.5.4 - Remember the Baseline; 1.5.5 - Where Did Your Data Come From?
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1.5.6 - Magnified or Obscured Effects1.5.7 - Outliers; 1.6 - Overfitting the Model; 1.7 - Generalizability versus Accuracy; 1.8 - Input/Output; Chapter 2 - Domain Expertise; 2.1 - Domain expertise; 2.2 - Domain Expertise for Analysts; 2.3 - The Integrated Model; Chapter 3 - Data Mining and Predictive Analytics; 3.1 - Discovery and Prediction; 3.2 - Confirmation and discovery; 3.3 - Surprise; 3.4 - Characterization; 3.5 - "Volume Challenge"16; 3.6 - Exploratory Graphics and Data Exploration; 3.7 - Link Analysis21; 3.8 - Non-Obvious Relationship Analysis (NORA)24; 3.9 - Text Mining
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3.10 - Closing ThoughtsPart 2 - Methods; Chapter 4 - Process Models for Data Mining and Predictive Analysis; 4.1 - CIA Intelligence Process7; 4.1.1 - Requirements; 4.1.2 - Collection; 4.1.3 - Processing and Exploitation; 4.1.4 - Analysis and Production; 4.1.5 - Dissemination; 4.1.6 - Feedback; 4.1.7 - Summary; 4.2 - Cross-industry Standard Process for Data Mining; 4.2.1 - Business Understanding; 4.2.2 - Data Understanding; 4.2.3 - Data Preparation; 4.2.4 - Modeling; 4.2.5 - Evaluation; 4.2.6 - Deployment; 4.7.1 - Summary
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4.8 - Actionable Mining and Predictive Analysis for Public Safety and Security4.8.1 - Question or Challenge; 4.8.2 - Data Collection and Fusion; 4.8.3 - Operationally Relevant Preprocessing; 4.8.3.1 - Recoding; 4.8.3.2 - Variable Selection; 4.8.3.3 - Operational Value; 4.8.3.4 - Availability and Timeliness; 4.8.4 - Identification, Characterization, and Modeling; 4.8.5 - Public Safety and Security-Specific Evaluation; 4.8.6 - Operationally Actionable Output; 4.8.7 - Additional Considerations; 4.8.7.1 - Privacy; 4.8.7.2 - Security; 4.8.7.3 - Other Hazards; 4.8.8 - Summary; Chapter 5 - Data
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5.1 - Getting started5.2 - Types of data; 5.3 - Data5; 5.3.1 - Big Data; 5.3.1.1 - Volume; 5.3.1.2 - Velocity; 5.3.1.3 - Variety; 5.4 - Types of Data Resources; 5.4.1 - Data Sources; 5.4.1.1 - Records Management Systems; 5.4.1.2 - Calls for Service; 5.4.2 - Relational Data; 5.4.3 - Revisiting the INTs; 5.4.4 - Geoint19; 5.4.4.1 - Physical Geography; 5.4.4.2 - Human Geography; 5.4.5 - Ad Hoc, Self-Generated, and Other Specialized Databases22; 5.4.6 - Nontraditional Sources (e.g., Weather); 5.5 - Data Challenges; 5.5.1 - Reliability and Validity; 5.5.2 - Data Entry Errors
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5.5.3 - Misrepresentation, Fabrication, and Poor Recall
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English
Additional Edition:
ISBN 0-12-800229-8
Language:
English
