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Computer Vision Uncovers Predictors of Physical Urban Change

Nikhil Naik, Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, and César A. Hidalgo
Proceedings of the National Academy of Sciences, 2017

We introduce Streetchange, a new way of measuring changes in the physical appearances of neighborhoods using a computer vision algorithm. We calculate Streetchange by algorithmically comparing Google Street View images of the same location captured in different years.

Physical urban change—the evolution of cities’ public and private infrastructure—has been of interest to policymakers, as well as scholars in economics, sociology, and urban planning for decades. However, it has been difficult to measure urban change in ways that are quantitative, robust, and scalable. In the present work, we developed a computer vision method that allows us to quantify urban change at high spatial resolutions using image time-series from Google Street View. We used this method to compute urban change for more than 1.5 million street blocks from five major American cities.

We aggregated Streetchange data from street blocks at the census tract level for the five American cities (Baltimore, Boston, Detroit, New York, and Washington DC). In our paper, we linked Streetchange to demographic and economic characteristics of neighborhoods, in order to understand which neighborhoods experience physical change. We found that physical growth occurs in geographically and physically attractive neighborhoods with dense, highly-educated populations.

Contact: Nikhil Naik,


Nikhil Naik – Prize Fellow, Harvard University

Scott Kominers- Associate Professor, Harvard Business School

Ramesh Raskar- Associate Professor, MIT Media Lab

Edward Glaeser- Fred and Elanor Glimp Professor of Economics, Harvard University

César Hidalgo- Associate Professor, MIT Media Lab