Big Data Meets Survey Science

A Collection of Innovative Methods

AvLars E. Lyberg,Stas Kolenikov

E-bok

Engelska, 2020

1 562 kr

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Beskrivning

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem

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.