IDEAS home Printed from https://ideas.repec.org/a/taf/rsrsxx/v5y2018i1p179-182.html
   My bibliography  Save this article

Estimating local daytime population density from census and payroll data

Author

Listed:
  • Geoff Boeing

Abstract

Daytime population density reflects where people commute and spend their waking hours. It carries significant weight as urban planners and engineers site transportation infrastructure and utilities, plan for disaster recovery, and assess urban vitality. Various methods with various drawbacks exist to estimate daytime population density across a metropolitan area, such as using census data, travel diaries, GPS traces, or publicly available payroll data. This study estimates the San Francisco Bay Area's tract-level daytime population density from US Census and LEHD LODES data. Estimated daytime densities are substantially more concentrated than corresponding night-time population densities, reflecting regional land use patterns. We conclude with a discussion of biases, limitations, and implications of this methodology.

Suggested Citation

  • Geoff Boeing, 2018. "Estimating local daytime population density from census and payroll data," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 5(1), pages 179-182, January.
  • Handle: RePEc:taf:rsrsxx:v:5:y:2018:i:1:p:179-182
    DOI: 10.1080/21681376.2018.1455535
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/21681376.2018.1455535
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/21681376.2018.1455535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Boeing, Geoff, 2017. "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings," SocArXiv v54w4, Center for Open Science.
    2. Garrett Dash Nelson & Alasdair Rae, 2016. "An Economic Geography of the United States: From Commutes to Megaregions," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-23, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Geoff Boeing & Yougeng Lu & Clemens Pilgram, 2023. "Local inequities in the relative production of and exposure to vehicular air pollution in Los Angeles," Urban Studies, Urban Studies Journal Limited, vol. 60(12), pages 2351-2368, September.
    2. Areum Jo & Sang-Kyeong Lee & Jaecheol Kim, 2020. "Gender Gaps in the Use of Urban Space in Seoul: Analyzing Spatial Patterns of Temporary Populations Using Mobile Phone Data," Sustainability, MDPI, vol. 12(16), pages 1-22, August.
    3. Matthew Hall & John Iceland & Youngmin Yi, 2019. "Racial Separation at Home and Work: Segregation in Residential and Workplace Settings," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(5), pages 671-694, October.
    4. Fox, Sean & Wolf, Levi John, 2022. "What makes a place urban?," SocArXiv qfvry, Center for Open Science.
    5. Radoslaw Panczak & Elin Charles-Edwards & Jonathan Corcoran, 2020. "Estimating temporary populations: a systematic review of the empirical literature," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-10, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikhil Kaza & Katherine Nesse, 2021. "Characterizing the Regional Structure in the United States: A County-based Analysis of Labor Market Centrality," International Regional Science Review, , vol. 44(5), pages 560-581, September.
    2. Giuseppe Arbia & Vincenzo Nardelli, 2024. "Using Web-Data to Estimate Spatial Regression Models," International Regional Science Review, , vol. 47(2), pages 204-226, March.
    3. Mark He & Joseph Glasser & Nathaniel Pritchard & Shankar Bhamidi & Nikhil Kaza, 2020. "Demarcating geographic regions using community detection in commuting networks with significant self-loops," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-31, April.
    4. Geoff Boeing, 2020. "Online rental housing market representation and the digital reproduction of urban inequality," Environment and Planning A, , vol. 52(2), pages 449-468, March.
    5. Ben Derudder, 2021. "Network Analysis of ‘Urban Systems’: Potential, Challenges, and Pitfalls," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 112(4), pages 404-420, September.
    6. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    7. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.
    8. Boeing, Geoff & Wegmann, Jake & Jiao, Junfeng, 2020. "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data," SocArXiv phgqt, Center for Open Science.
    9. Ruth Hamilton & Alasdair Rae, 2020. "Regions from the ground up: a network partitioning approach to regional delineation," Environment and Planning B, , vol. 47(5), pages 775-789, June.
    10. Wenqian Ke & Wei Chen & Zhaoyuan Yu, 2017. "Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    11. Neal, Zachary P. & Derudder, Ben & van Meeteren, Michiel, 2022. "When is a matrix a geographical network?," OSF Preprints 6jhzm, Center for Open Science.
    12. Garrett Dash Nelson, 2021. "Communities, Complexity, and the ‘Conchoration’: Network Analysis and the Ontology of Geographic Units," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 112(4), pages 351-369, September.
    13. Marcińczak, Szymon & Bartosiewicz, Bartosz, 2018. "Commuting patterns and urban form: Evidence from Poland," Journal of Transport Geography, Elsevier, vol. 70(C), pages 31-39.
    14. Guillaume Chapelle & Jean Benoît Eyméoud, 2022. "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    15. Georg von Graevenitz & Stuart J. H. Graham & Amanda F. Myers, 2022. "Distance (still) hampers diffusion of innovations," Regional Studies, Taylor & Francis Journals, vol. 56(2), pages 227-241, February.
    16. Agovino, Massimiliano & Crociata, Alessandro & Sacco, Pier Luigi, 2019. "Proximity effects in obesity rates in the US: A Spatial Markov Chains approach," Social Science & Medicine, Elsevier, vol. 220(C), pages 301-311.
    17. Geoff Boeing & Max Besbris & Ariela Schachter & John Kuk, 2021. "Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?," Housing Policy Debate, Taylor & Francis Journals, vol. 31(1), pages 112-126, January.
    18. Ziqi Liu & Ming Zhang & Liwen Liu, 2021. "Benchmark of the Trends of Spatial Inequality in World Megaregions," Sustainability, MDPI, vol. 13(11), pages 1-21, June.
    19. Xiaoyan Mu & Anthony Gar-On Yeh, 2020. "Regional delineation of China based on commuting flows," Environment and Planning A, , vol. 52(3), pages 478-482, May.
    20. Wangbao Liu & Quan Hou & Zhihao Xie & Xin Mai, 2020. "Urban Network and Regions in China: An Analysis of Daily Migration with Complex Networks Model," Sustainability, MDPI, vol. 12(8), pages 1-12, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:rsrsxx:v:5:y:2018:i:1:p:179-182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rsrs .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.