Computer vision uncovers predictors of physical urban change
▻http://www.pnas.org/content/114/29/7571.full
Fig. 1.
Computing Streetchange: (A) We calculate Streetscore, a metric for perceived safety of a streetscape, using a regression model based on two image features: GIST and texton maps. We calculate those features from pixels of four object categories—ground, buildings, trees, and sky—which are inferred using semantic segmentation. (B–D) We calculate the Streetchange of a street block as the difference between the Streetscores of a pair of images captured in 2007 and 2014. (B) The Streetchange metric is not affected by seasonal and weather changes. (C) Large positive Streetchange is typically associated with major construction. (D) Large negative Streetchange is associated with urban decay. Insets courtesy of Google, Inc.
Significance
We develop a computer vision method to measure changes in the physical appearances of neighborhoods from street-level imagery. We correlate the measured changes with neighborhood characteristics to determine which characteristics predict neighborhood improvement. We find that both education and population density predict improvements in neighborhood infrastructure, in support of theories of human capital agglomeration. Neighborhoods with better initial appearances experience more substantial upgrading, as predicted by the tipping theory of urban change. Finally, we observe more improvement in neighborhoods closer to both city centers and other physically attractive neighborhoods, in agreement with the invasion theory of urban sociology. Our results show how computer vision techniques, in combination with traditional methods, can be used to explore the dynamics of urban change.
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.