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Quantifying scenic areas using crowdsourced data
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Seresinhe, Chanuki Illushka, Moat, Helen Susannah and Preis, Tobias. (2017) Quantifying scenic areas using crowdsourced data. Environment and Planning B : Urban Analytics and City Science . 026581351668730. ISSN 2399-8083 (In Press)
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Official URL: http://dx.doi.org/10.1177/0265813516687302
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 | ||||
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| Subjects: | G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| 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: | 23 January 2017 | ||||
| Dates: |
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| Article Number: | 026581351668730 | ||||
| Identification Number: | 10.1177/0265813516687302 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | In Press | ||||
| 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) | ||||
| URI: | http://wrap.warwick.ac.uk/id/eprint/87375 |
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