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Economic outcomes predicted by diversity in cities

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  • Chong, Shi Kai
  • Bahrami, Mohsen
  • Chen, Hao
  • balcisoy, Selim
  • Bozkaya, Burcin
  • Pentland, Alex 'Sandy'

Abstract

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.

Suggested Citation

  • Chong, Shi Kai & Bahrami, Mohsen & Chen, Hao & balcisoy, Selim & Bozkaya, Burcin & Pentland, Alex 'Sandy', 2020. "Economic outcomes predicted by diversity in cities," OSF Preprints j59u3, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:j59u3
    DOI: 10.31219/osf.io/j59u3
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    References listed on IDEAS

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    Cited by:

    1. Eloy Solis & Kayvan Karimi & Irene Garcia & Inmaculada Mohino, 2022. "Knowledge Economy Clustering at the Intrametropolitan Level: Evidence from Madrid," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 1268-1299, June.
    2. Raghu Bir Bista & Surendra Paneru, 2021. "Does Road Traffic Congestion Increase Fuel Consumption of Households in Kathmandu City?," Journal of Economic Impact, Science Impact Publishers, vol. 3(2), pages 67-79.
    3. Takahiro Yabe & Bernardo García Bulle Bueno & Xiaowen Dong & Alex Pentland & Esteban Moro, 2023. "Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Xiangfei Yuan & Haijing Hao & Chenghua Guan & Alex Pentland, 2021. "What are the key components of an entrepreneurial ecosystem in a developing economy? A longitudinal empirical study on technology business incubators in China," Papers 2103.08131, arXiv.org.

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