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Quantifying scenic areas using crowdsourced data

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Seresinhe, Chanuki Illushka, Moat, Helen Susannah and Preis, Tobias (2018) Quantifying scenic areas using crowdsourced data. Environment and Planning B : Urban Analytics and City Science, 45 (3). pp. 567-582. doi:10.1177/0265813516687302

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Official URL: http://dx.doi.org/10.1177/0265813516687302

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Abstract

For centuries, philosophers, policy-makers and urban planners have debated whether aesthetically pleasing surroundings can improve our wellbeing. To date, quantifying how scenic an area is has proved challenging, due to the difficulty of gathering large-scale measurements of scenicness. In this study we ask whether images uploaded to the website Flickr, combined with crowdsourced geographic data from OpenStreetMap, can help us estimate how scenic people consider an area to be. We validate our findings using crowdsourced data from Scenic-Or-Not, a website where users rate the scenicness of photos from all around Great Britain. We find that models including crowdsourced data from Flickr and OpenStreetMap can generate more accurate estimates of scenicness than models that consider only basic census measurements such as population density or whether an area is urban or rural. Our results provide evidence that by exploiting the vast quantity of data generated on the Internet, scientists and policy-makers may be able to develop a better understanding of people's subjective experience of the environment in which they live.

Item Type: Journal Article
Subjects: G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Landscape assessment -- Mathematical models -- Great Britain, OpenStreetMap (Project), Flickr (Electronic resource)
Journal or Publication Title: Environment and Planning B : Urban Analytics and City Science
Publisher: Sage Publications Ltd.
ISSN: 2399-8083
Official Date: 1 May 2018
Dates:
DateEvent
1 May 2018Published
23 January 2017Available
Volume: 45
Number: 3
Page Range: pp. 567-582
DOI: 10.1177/0265813516687302
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: Research Councils UK (RCUK), Warwick Business School, Alan Turing Institute (ATI), Engineering and Physical Sciences Research Council (EPSRC)
Grant number: EP/K039830/1 (RCUK), EP/N510129/1 (ATI), EP/K000128/1 (EPSRC)

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