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Cities Are Physical Too: Using Computer Vision to Measure the Quality and Impact of Urban Appearance

Author

Listed:
  • Nikhil Naik
  • Ramesh Raskar
  • César A. Hidalgo

Abstract

For social scientists, developing an empirical connection between the physical appearance of a city and the behavior and health of its inhabitants has proved challenging due to a lack of data on urban appearance. Can we use computers to quantify urban appearance from street-level imagery? We describe Streetscore: a computer vision algorithm that measures the perceived safety of streetscapes. Using Streetscore to evaluate 19 American cities, we find that the average perceived safety has a strong positive correlation with population density and household income; and the variation in perceived safety has a strong positive correlation with income inequality.

Suggested Citation

  • 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.
    • Handle: RePEc:aea:aecrev:v:106:y:2016:i:5:p:128-32
    Note: DOI: 10.1257/aer.p20161030
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Been, Vicki & Ellen, Ingrid Gould & Gedal, Michael & Glaeser, Edward & McCabe, Brian J., 2016. "Preserving history or restricting development? The heterogeneous effects of historic districts on local housing markets in New York City," Journal of Urban Economics, Elsevier, vol. 92(C), pages 16-30.
    4. 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.
    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. Urban Umami or Urban Appakukan?: The Psychology of Streetscapes
      by Jason Barr in Skynomics Blog on 2020-10-22 12:34:19

    Citations

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    Cited by:

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    3. Zichen Zhao & Zhiqiang Wu & Shiqi Zhou & Wen Dong & Wei Gan & Yixuan Zou & Mo Wang, 2023. "Resident Effect Perception in Urban Spaces to Inform Urban Design Strategies," Land, MDPI, vol. 12(10), pages 1-24, October.
    4. Ka Shing Cheung & Chung Yim Yiu, 2022. "The economics of architectural aesthetics: Identifying price effect of urban ambiences by different house cohorts," Environment and Planning B, , vol. 49(6), pages 1741-1756, July.
    5. Zhang, Yonglin & Li, Shanlin & Dong, Rencai & Deng, Hongbing & Fu, Xiao & Wang, Chenxing & Yu, Tianshu & Jia, Tianxia & Zhao, Jingzhu, 2021. "Quantifying physical and psychological perceptions of urban scenes using deep learning," Land Use Policy, Elsevier, vol. 111(C).
    6. , 2019. "A roundtable discussion: Defining urban data science," Environment and Planning B, , vol. 46(9), pages 1756-1768, November.
    7. Mingshu Wang & Floris Vermeulen, 2021. "Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality?," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3118-3139, November.
    8. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    9. 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.
    10. Abhishek Nagaraj & Scott Stern, 2020. "The Economics of Maps," Journal of Economic Perspectives, American Economic Association, vol. 34(1), pages 196-221, Winter.
    11. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
    12. Mohamed R Ibrahim & James Haworth & Tao Cheng, 2021. "URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision," Environment and Planning B, , vol. 48(1), pages 76-93, January.
    13. Guan‐Yuan Wang, 2023. "The effect of environment on housing prices: Evidence from the Google Street View," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 288-311, March.
    14. Suss, Joel, 2023. "Measuring local, salient economic inequality in the UK," LSE Research Online Documents on Economics 117884, London School of Economics and Political Science, LSE Library.
    15. 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.
    16. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.
    17. Jeremy Gabe & Spenser Robinson & Andrew Sanderford, 2022. "Willingness to Pay for Attributes of Location Efficiency," The Journal of Real Estate Finance and Economics, Springer, vol. 65(3), pages 384-418, October.
    18. Wan, Wayne Xinwei & Lindenthal, Thies, 2022. "Towards accountability in machine learning applications: A system-testing approach," ZEW Discussion Papers 22-001, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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