Computer vision uncovers predictors of physical urban change
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7571.full.pdf
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Author(s)
Kominers, Scott Duke
Glaeser, Edward L.
Hidalgo, César A.
Naik, Nikhil Deepak
Raskar, Ramesh
Date Issued
July 2017
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Naik, Nikhil et al. “Computer Vision Uncovers Predictors of Physical Urban Change.” Proceedings of the National Academy of Sciences 114, 29 (July 2017): 7571–7576 © 2017 National Academy of Sciences
Version
Final published version
Abstract
Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements—an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements—an observation that is consistent with “tipping” theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods—an observation that is consistent with the “invasion” theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/PNAS.1619003114
