Massachusetts Institute of Technology
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NBER Working Papers and Publications
|December 2015||Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life|
with Edward L. Glaeser, Scott Duke Kominers, Michael Luca: w21778
New, “big” data sources allow measurement of city characteristics and outcome variables higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar image data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can b...
Published: Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2016. "BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE," Economic Inquiry, .
|October 2015||Do People Shape Cities, or Do Cities Shape People? The Co-evolution of Physical, Social, and Economic Change in Five Major U.S. Cities|
with Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, César A. Hidalgo: w21620
Urban change involves transformations in the physical appearance and the social composition of neighborhoods. Yet, the relationship between the physical and social components of urban change is not well understood due to the lack of comprehensive measures of neighborhood appearance. Here, we introduce a computer vision method to quantify change in physical appearance of streetscapes and generate a dataset of physical change for five large American cities. We combine this dataset with socioeconomic indicators to explore whether demographic and economic changes precede, follow, or co-occur with changes in physical appearance. We find that the strongest predictors of improvement in a neighborhood’s physical appearance are population density and share of college-educated adults. Other socioeco...