Fred and Eleanor Glimp Professor of Economics, FAS
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 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 for Urban Life." HKS Faculty Research Working Paper Series RWP15-075, December 2015.