Edward L. Glaeser, Fred and Eleanor Glimp Professor of Economics, Harvard University; Advisor, The HUMAN Project
Scott Duke Kominers, Junior Fellow,Society of Fellows, Harvard; Research Scientist, Harvard Program for Evolutionary Dynamic, Asssociate, Harvard Center for Research on Computation and Society.
Michael Luca, Assistant Professor of Business Administration, Harvard
Nikhil Naik, PH.D Student, Massachusetts Institute of Technology
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.
The combination of metrics, such as spending patterns, psychological data, and movement throughout the city, to name a few—as embodied by The HUMAN Project—seems like a promising means of advancing urban measurement with the objective of improving governance and citizen outcomes.