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In search of art : rapid estimates of gallery and museum visits using Google Trends
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Botta, Federico, Preis, Tobias and Moat, Helen Susannah (2020) In search of art : rapid estimates of gallery and museum visits using Google Trends. EPJ Data Science, 9 (1). 14 . doi:10.1140/epjds/s13688-020-00232-z ISSN 2193-1127.
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WRAP-in-search-art-rapid-estimate-museum-Google-Moat-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1298Kb) | Preview |
Official URL: https://doi.org/10.1140/epjds/s13688-020-00232-z
Abstract
Measuring collective human behaviour has traditionally been a time-consuming and expensive process, impairing the speed at which data can be made available to decision makers in policy. Can data generated through widespread use of online services help provide faster insights? Here, we consider an example relating to policymaking for culture and the arts: publicly funded museums and galleries in the UK. We show that data on Google searches for museums and galleries can be used to generate estimates of their visitor numbers. Crucially, we find that these estimates can be generated faster than traditional measurements, thus offering policymakers early insights into changes in cultural participation supported by public funds. Our findings provide further evidence that data on our use of online services can help generate timely indicators of changes in society, so that decision makers can focus on the present rather than the past.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | A General Works > AM Museums (General). Collectors and collecting (General) C Auxiliary Sciences of History > CB History of civilization D History General and Old World > D History (General) H Social Sciences > HM Sociology |
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Divisions: | Faculty of Social Sciences > Warwick Business School | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Forecasting, Culture, Art museums, Museums | |||||||||||||||
Journal or Publication Title: | EPJ Data Science | |||||||||||||||
Publisher: | Springer | |||||||||||||||
ISSN: | 2193-1127 | |||||||||||||||
Official Date: | 5 June 2020 | |||||||||||||||
Dates: |
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Volume: | 9 | |||||||||||||||
Number: | 1 | |||||||||||||||
Article Number: | 14 | |||||||||||||||
DOI: | 10.1140/epjds/s13688-020-00232-z | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | ** Article version: VoR ** From Crossref via Jisc Publications Router ** History: epub 05-06-2020; issued 05-06-2020. ** Licence for VoR version of this article starting on 05-06-2020: https://creativecommons.org/licenses/by/4.0 | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 19 June 2020 | |||||||||||||||
Date of first compliant Open Access: | 19 June 2020 | |||||||||||||||
RIOXX Funder/Project Grant: |
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