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Do People Shape Cities, or Do Cities Shape People? THe Co-evolution of Physical, Social and Economic Change in Five Major U.S. Cities

Author

Listed:
  • Naik, Nikhil

    (MIT)

  • Kominers, Scott Duke

    (Harvard University)

  • Raskar, Ramesh

    (MIT)

  • Glaeser, Edward L.

    (Harvard University)

  • Hidalgo, Cesar A.

    (MIT)

Abstract

Urban change involves transformations in the physical appearance and the social composition of neighborhoods. Yet, the relationship between the physical and social components of urban change is not well understood due to the lack of comprehensive measures of neighborhood appearance. Here, we introduce a computer vision method to quantify change in physical appearance of streetscapes and generate a dataset of physical change for five large American cities. We combine this dataset with socioeconomic indicators to explore whether demographic and economic changes precede, follow, or co-occur with changes in physical appearance. We find that the strongest predictors of improvement in a neighborhood's physical appearance are population density and share of college-educated adults. Other socioeconomic characteristics, like median income, share of vacant homes, and monthly rent, do not predict improvement in physical appearance. We also find that neighborhood appearances converge to the initial appearances of bordering areas, supporting the Burgess "invasion" theory. In addition, physical appearance is more likely to improve in neighborhoods proximal to the central business district. Finally, we find modest support for "tipping" and "filtering" theories of urban change.

Suggested Citation

  • Naik, Nikhil & Kominers, Scott Duke & Raskar, Ramesh & Glaeser, Edward L. & Hidalgo, Cesar A., 2015. "Do People Shape Cities, or Do Cities Shape People? THe Co-evolution of Physical, Social and Economic Change in Five Major U.S. Cities," Working Paper Series 15-061, Harvard University, John F. Kennedy School of Government.
  • Handle: RePEc:ecl:harjfk:15-061
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    References listed on IDEAS

    as
    1. Edward L. Glaeser & Matthew E. Kahn & Jordan Rappaport, 2000. "Why Do The Poor Live In Cities?," Harvard Institute of Economic Research Working Papers 1891, Harvard - Institute of Economic Research.
    2. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    3. repec:hoo:wpaper:e-95-4 is not listed on IDEAS
    4. Margolis, Stephen E, 1982. "Depreciation of Housing: An Empirical Consideration of the Filtering Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 64(1), pages 90-96, February.
    5. Glaeser, Edward L. & Kahn, Matthew E. & Rappaport, Jordan, 2008. "Why do the poor live in cities The role of public transportation," Journal of Urban Economics, Elsevier, vol. 63(1), pages 1-24, January.
    6. Glaeser, Edward L. & Scheinkman, JoseA. & Shleifer, Andrei, 1995. "Economic growth in a cross-section of cities," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 117-143, August.
    7. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    8. Bond, Eric W. & Coulson, N. Edward, 1989. "Externalities, filtering, and neighborhood change," Journal of Urban Economics, Elsevier, vol. 26(2), pages 231-249, September.
    Full references (including those not matched with items on IDEAS)

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Do People Shape Cities, or Do Cities Shape People? THe Co-evolution of Physical, Social and Economic Change in Five Major U.S. Cities
      by maximorossi in NEP-LTV blog on 2016-10-06 00:05:27

    Citations

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    Cited by:

    1. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    2. Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
    3. Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
    4. Liang, Cong & Hui, Eddie C.M. & Yip, Tsz Leung & Huang, Yaoxuan, 2020. "Private land use for public housing projects: The Influence of a Government Announcement on Housing Markets in Hong Kong," Land Use Policy, Elsevier, vol. 99(C).
    5. Matthew E. Kahn, 2017. "Will Climate Change Cause Enormous Social Costs for Poor Asian Cities?," Asian Development Review, MIT Press, vol. 34(2), pages 229-248, September.
    6. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    7. Nikhil Naik & Ramesh Raskar & César A. Hidalgo, 2016. "Cities Are Physical Too: Using Computer Vision to Measure the Quality and Impact of Urban Appearance," American Economic Review, American Economic Association, vol. 106(5), pages 128-132, May.
    8. Liang, Cong & Huang, Yaoxuan & Yip, Tsz Leung & Li, Victor Jing, 2022. "Does rail transit development gentrify neighborhoods? Evidence from Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 354-372.
    9. Yencha, Christopher, 2019. "Valuing walkability: New evidence from computer vision methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 689-709.

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    More about this item

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General
    • Y10 - Miscellaneous Categories - - Data: Tables and Charts - - - Data: Tables and Charts

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