Crowdsourcing architectural beauty: Online photo frequency predicts building aesthetic ratings
Name
journal.pone.0194369.pdf
Size
2.44 MB
Format
Adobe PDF
Checksum (MD5)
e34181cb86957794c66088418e76105e
Author(s)
Bernard, James
Saiz, Albert
Salazar Miranda, Ariana
Date Issued
July 2018
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Saiz, Albert et al. “Crowdsourcing Architectural Beauty: Online Photo Frequency Predicts Building Aesthetic Ratings.” Edited by Tobias Preis. PLOS ONE 13, 7 (July 2018): e0194369 © 2018 Saiz et al
Version
Final published version
Abstract
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The aesthetic quality of the built environment is of paramount importance to the quality of life of an increasingly urbanizing population. However, a lack of data has hindered the development of comprehensive measures of perceived architectural beauty. In this paper, we demonstrate that the local frequency of geotagged photos posted by internet users in two photo-sharing websites strongly predict the beauty ratings of buildings. We conduct an independent beauty survey with respondents rating proprietary stock photos of 1,000 buildings across the United States. Buildings with higher ratings were found more likely to be geotagged with user-uploaded photos in both Google Maps and Flickr. This correlation also holds for the beauty rankings of raters who seldom upload materials to the internet. Objective architectural characteristics that predict higher average beauty ratings of buildings also positively covary with their internet photo frequency. These results validate the use of localized user-generated image uploads in photo-sharing sites to measure the aesthetic appeal of the urban environment in the study of architecture, real estate, urbanism, planning, and environmental psychology.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0194369
