Big Data Meets Survey Science: A Collection of Innovative Methods

This book offers a comprehensive view of the utility and place for survey data within the broader Big Data ecosystem. 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. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented, and 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, and using surveys to build and assess RBS religious flags. The book is an excellent resource for both the survey and social science communities, as well as the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Format: Hardback
Length: 784 pages
Publication date: 26 November 2020
Publisher: John Wiley & Sons Inc

Big Data Meets Survey Science: A Collection of Innovative Methods offers a comprehensive view of the utility and place of survey data within the broader Big Data ecosystem. This book presents a collection of snapshots from two sides of the Big Data perspective, assembling 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 showcases how survey data and Big Data are used together for the benefit of one or more sources of data. Numerous chapters provide 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: This chapter discusses the development of total error frameworks for found data, which are used to assess the accuracy and reliability of survey data. It explores the use of various statistical methods and models to estimate the total error in survey estimates and provides insights into how to reduce the error and improve the accuracy of survey data.

Performance and Sensitivities of Home Detection on Mobile Phone Data: This chapter examines the performance and sensitivities of home detection on mobile phone data, which is a key component of many Big Data projects. It explores the use of machine learning methods to identify homes and assess their characteristics, such as size, location, and construction type. It also discusses the challenges and limitations of using mobile phone data for home detection and provides recommendations for improving the accuracy and reliability of the results.

Assessing Community Wellbeing Using Google Street View and Satellite Imagery: This chapter explores the use of Google Street View and satellite imagery to assess community wellbeing. It discusses the development of methods and tools for analyzing these data sources to identify areas of concern, such as poverty, homelessness, and environmental degradation. It also provides examples of how these data sources have been used to inform policy decisions and interventions in various communities.

Using Surveys to Build and Assess RBS Religious Flag: This chapter presents a novel method for using surveys to build and assess the Religious Flag (RBS). The RBS is a measure of religious diversity and tolerance that is used to assess the level of religious diversity and tolerance in a given community. The chapter discusses the development of the RBS and the use of surveys to assess its validity and reliability. It also provides examples of how the RBS has been used to inform policy decisions and interventions in various communities.

Other topics covered in the book

Big Data Meets Survey Science: A Collection of Innovative Methods provides groundbreaking survey methods being utilized today in the field of Big Data. It 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, the book covers methods and applications used in combining Big Data with survey statistics. It also examines regulations as well as ethical and privacy issues.

In conclusion, Big Data Meets Survey Science: A Collection of Innovative Methods is a valuable resource for researchers, practitioners, and policymakers interested in understanding the utility and place of survey data within the broader Big Data ecosystem. It offers a comprehensive view of the current state-of-the-art in survey data and Big Data research, providing a wealth of practical insights and examples that can be applied to a wide range of fields. By combining survey data with Big Data, researchers and practitioners can gain a deeper understanding of human populations, improve official statistics and estimates, and develop more effective policies and interventions.

Weight: 1048g
Dimension: 236 x 160 x 35 (mm)
ISBN-13: 9781118976326