Fred and Eleanor Glimp Professor of Economics, FAS
New, 'big data' sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban 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 imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services.
Glaeser, Edward L., Scott Duke Kominers, Michael Luca, and Nikhil Naik. "Big Data and Big Cities: the Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry 56.1 (January 2018): 114-137.