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dc.contributor.authorChong, SK
dc.contributor.authorBahrami, M
dc.contributor.authorChen, H
dc.contributor.authorBalcisoy, S
dc.contributor.authorBozkaya, B
dc.contributor.authorPentland, A
dc.date.accessioned2021-11-02T14:15:18Z
dc.date.available2021-11-02T14:15:18Z
dc.date.issued2020-06
dc.identifier.urihttps://hdl.handle.net/1721.1/137086
dc.description.abstract© 2020, The Author(s). Much recent work has illuminated the growth, innovation, and prosperity of entire cities, but there is relatively less evidence concerning the growth and prosperity of individual neighborhoods. In this paper we show that diversity of amenities within a city neighborhood, computed from openly available points of interest on digital maps, accurately predicts human mobility (“flows”) between city neighborhoods and that these flows accurately predict neighborhood economic productivity. Additionally, the diversity of consumption behaviour or the diversity of flows together with geographic centrality and population density accurately predicts neighborhood economic growth, even after controlling for standard factors such as population, etc. We develop our models using geo-located purchase data from Istanbul, and then validate the relationships using openly available data from Beijing and several U.S. cities. Our results suggest that the diversity of goods and services within a city neighborhood is the largest single factor driving both human mobility and economic growth.en_US
dc.language.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1140/epjds/s13688-020-00234-xen_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceEPJ Data Scienceen_US
dc.titleEconomic outcomes predicted by diversity in citiesen_US
dc.typeArticleen_US
dc.identifier.citationChong, SK, Bahrami, M, Chen, H, Balcisoy, S, Bozkaya, B et al. 2020. "Economic outcomes predicted by diversity in cities." EPJ Data Science, 9 (1).
dc.contributor.departmentMIT Connection Science (Research institute)
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.relation.journalEPJ Data Scienceen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-06-30T18:51:15Z
dspace.orderedauthorsChong, SK; Bahrami, M; Chen, H; Balcisoy, S; Bozkaya, B; Pentland, Aen_US
dspace.date.submission2021-06-30T18:51:17Z
mit.journal.volume9en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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