کار با داده بزرگ با استفاده از علم پیمایش

عنوان فارسی:       لحاظ داده بزرگ با علم پیمایش: مجموعه ای از روش های ابداعی

عنوان انگلیسی:    Big Data Meets Survey Science: A Collection of Innovative Methods

This book presents a collection of snapshots from two sides of the Big Data perspective.  It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations.  It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data.

Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources.  Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more.

• Presents groundbreaking survey methods being utilized today in the field of Big Data
• Explores how machine learning methods can be applied to the design, collection, and analysis of social science data
• Filled with examples and illustrations that show how survey data benefits Big Data evaluation
• Covers methods and applications used in combining Big Data with survey statistics
• Examines regulations as well as ethical and privacy issues

Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Introduction
Section 1:  The New Survey Landscape
1- Why Machines Matter for Survey and Social Science Researchers: Exploring Applications of Machine Learning Methods for Design, Data Collection, and Analysis
2- The Future Is Now: How Surveys Can Harness Social Media To Address 21st Century Challenges
3- Linking Survey Data with Commercial or Administrative Data for Data Quality Assessment

Section 2:  Total Error and Data Quality
4- Total Error Frameworks for Hybrid Estimation and Their Applications
5- Measuring the Strength of Attitudes in Social Media Data
6- Attention to Campaign Events: Do Twitter and Self-Report Metrics Tell the Same Story?
7- Improving Quality of Administrative Data: A Case Study with FBI’s National Incident-Based Reporting System Data
8- Performance and Sensitivities of Home Detection on Mobile Phone Data

Section 3:  Big Data in Official Statistics
9- Big Data Initiatives in Official Statistics
10- Big Data in Official Statistics: A Perspective from Statistics Netherlands
11- Mining the New Oil for Official Statistics
12- Investigating Alternative Data Sources to Reduce Respondent Burden in United States Census Bureau Retail Economic Data Products

Section 4:  Combining Big Data with Survey Statistics: Methods and Applications
13- Effects of Incentives in Smartphone Data Collection
14- Using Machine Learning Models to Predict Attrition in a Survey Panel
15- Assessing Community Well-being using Google Street-View and Satellite Imagery
16- Nonparametric Bootstrap and Small Area Estimation to Mitigate Bias in Crowdsourced Data: Simulation Study and Application to Perceived Safety
17- Using Big Data to Improve Sample Efficiency

Section 5:  Combining Big Data with Survey Statistics: Tools
18- Feedback Loop: Using Surveys to Build and Assess Registration-Based Sample Religious Flags for Survey Research
19- Artificial Intelligence and Machine Learning Derived Efficiencies for Large-Scale Survey Estimation Efforts
20- Worldwide Population Estimates for Small Geographic Areas: Can We Do a Better Job?

Section 6:  The Fourth Paradigm, Regulations, Ethics, Privacy
21- Reproducibility in the Era of Big Data: Lessons for Developing Robust Data Management and Data Analysis Procedures
22- Combining Active and Passive Mobile Data Collection: A Survey of Concerns
23- Attitudes Toward Data Linkage: Privacy, Ethics, and the Potential for Harm
24- Moving Social Science into the Fourth Paradigm: The Data Life Cycle