Abstract
High-resolution population distribution data are critical for successfully addressing important issues ranging from socio-environmental research to public health to homeland security, since scientific analyses, operational activities, and policy decisions are significantly influenced by the number of impacted people. Dasymetric modeling has been a well-recognized approach for spatial decomposition of census data to increase the spatial resolution of population distribution. However, enhancing the temporal resolution of population distribution poses a greater challenge. In this paper, we discuss the development of LandScan USA, a multi-dimensional dasymetric modeling approach, which has allowed the creation of a very high-resolution population distribution data both over space and time. At a spatial resolution of 3 arc seconds (∼90 m), the initial LandScan USA database contains both a nighttime residential as well as a baseline daytime population distribution that incorporates movement of workers and students. Challenging research issues of disparate and misaligned spatial data and modeling to develop a database at a national scale, as well as model verification and validation approaches are illustrated and discussed. Initial analyses indicate a high degree of locational accuracy for LandScan USA distribution model and data. High-resolution population data such as LandScan USA, which describes both distribution and dynamics of human population, clearly has the potential to profoundly impact multiple domain applications of national and global priority.
| Original language | English |
|---|---|
| Pages (from-to) | 103-117 |
| Number of pages | 15 |
| Journal | GeoJournal |
| Volume | 69 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - Jun 2007 |
Funding
Acknowledgements The authors would like to acknowledge the ongoing financial support for the development of LandScan and LandScan USA models and databases from the Department of Defense and the Department of Homeland Security and past financial support from the Department of Energy, the US Environmental Protection Agency and the National Cancer Institute. Our efforts continue to benefit from significant contributions from some of the best and brightest student research associates, whose efforts in data search, acquisition, modeling, and validation allow us to develop the LandScan USA database. Such tireless contributions from Nagendra Singh, Lauren Patterson, Aaron Myers, Pamela Dalal, Neal Feierabend, Aarthy Sabesan, Patrick Hagge, and Allan Jolly are thankfully acknowledged. We would also like to thank other members of the Geographic Information Science and Technology group for their periodic insights and contributions to this work. This manuscript has benefited through comments from a number of internal and external reviewers and the authors acknowledge their valuable insights.
| Funders | Funder number |
|---|---|
| U.S. Department of Defense | |
| U.S. Department of Energy | |
| National Cancer Institute | |
| U.S. Environmental Protection Agency | |
| U.S. Department of Homeland Security |
Keywords
- Census
- Dasymetric modeling
- Daytime population distribution
- High-resolution population
- LandScan
- Population dynamics