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Estimating Local Daytime Population Density from Census and Payroll Data

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  • Boeing, Geoff

    (Northeastern University)

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 nighttime population densities, reflecting regional land use patterns. We conclude with a discussion of biases, limitations, and implications of this methodology.

Suggested Citation

  • Boeing, Geoff, 2018. "Estimating Local Daytime Population Density from Census and Payroll Data," SocArXiv ybvzu_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:ybvzu_v1
    DOI: 10.31219/osf.io/ybvzu_v1
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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