Big data and big cities: the promises and limitations of improved measures of urban life

Edward L. Glaeser Harvard University Scott Kominers Harvard University CMSA Michael Luca Harvard University Nikhil Naik Massachusetts Institute of Technology

Publications of CMSA of Harvard mathscidoc:1702.38024

New, “big data” sources allow measurement of city characteristics and outcomevariables at higher collection frequencies and more granular geographic scales thanever before. However, big data will not solve large urban social science questionson its own. Big urban data has the most value for the study of cities when it allowsmeasurement of the previously opaque, or when it can be coupled with exogenous shocksto people or place. We describe a number of new urban data sources and illustrate howthey can be used to improve the study and function of cities. We rst show how GoogleStreet View images can be used to predict income in New York City, suggesting thatsimilar imagery data can be used to map wealth and poverty in previously unmeasuredareas of the developing world. We then discuss how survey techniques can be improved tobetter measure willingness to pay for urban amenities. Finally, we explain how Internetdata is being used to improve the quality of city services.
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@inproceedings{edwardbig,
  title={BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE},
  author={Edward L. Glaeser, Scott Kominers, Michael Luca, and Nikhil Naik},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170207024819581113224},
}
Edward L. Glaeser, Scott Kominers, Michael Luca, and Nikhil Naik. BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170207024819581113224.
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