Estimating local daytime population density from census and payroll data
Bibliographic Data
| ID | 7366674 |
|---|---|
| Authors | Geoff Boeing (0000-0003-1851-6411, corresponding author) |
| Year | 2018 |
| Volume | 5 |
| Issue | 1 |
| Pages | 179-182 |
| Publication date | 2018-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Regional Studies Regional Science (JOURNAL) |
| Journal identifiers | ISSN: 2168-1376 • E-ISSN: 2168-1376 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/21681376.2018.1455535 |
| OpenAlex | W2805831783 |
| Language | EN |
| Citations received | 5 |
| References cited | 5 |
Daytime population density reflects where people commute and spend their\nwaking hours. It carries significant weight as urban planners and engineers\nsite transportation infrastructure and utilities, plan for disaster recovery,\nand assess urban vitality. Various methods with various drawbacks exist to\nestimate daytime population density across a metropolitan area, such as using\ncensus data, travel diaries, GPS traces, or publicly available payroll data.\nThis study estimates the San Francisco Bay Area's tract-level daytime\npopulation density from US Census and LEHD LODES data. Estimated daytime\ndensities are substantially more concentrated than corresponding nighttime\npopulation densities, reflecting regional land use patterns. We conclude with a\ndiscussion of biases, limitations, and implications of this methodology.\n
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Open to whom? Open-access publishing and global knowledge networks
| Citations per year | 0.63 |
|---|---|
| Citation span | 2018 - 2024 (7) |
| Highly cited | No |