ArticleEarth system science data2022
Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas.
Article in Earth system science data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- A climate region classification for California's warm season: apparent temperature clustering to support heat-health epidemiology.International journal of biometeorology · 2026Article
- A national model for estimating United States public land visitation.Scientific reports · 2025Article
- Measuring access to and availability of outdoor recreational opportunities: One pixel at a time.Landscape and urban planning · 2025Article
- Modeling Traffic-Related Air Pollution Burden of Disease using High Spatial Resolution Data.Journal of transport & health · 2025Article
- Infrastructure as Environmental Health Policy: Lessons from the Clean School Bus Program's Challenges and Innovations.International journal of environmental research and public health · 2025Article
- A dataset of US precinct votes allocated to Census geographies with precision.Scientific data · 2025Article
- The SESAME Human-Earth Atlas.Scientific data · 2025Article
- Federally-overlooked flood risk inequities in the conterminous United States.Scientific reports · 2025Article
- Analysis of the differences and influencing factors of rural population aging in Gansu Province-based on panel data from 2000 to 2022.Frontiers in public health · 2025Article
- Article
- Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019.Scientific data · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high-resolution gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within the United States (US) Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population Map for the conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas dasymetric population map, we found that IDM's population estimates for the US Census Bureau blocks improved across all states in the CONUS. We found that IDM performed better in states with larger urban areas than in states that are sparsely populated. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas. The updated 30 m population density for the CONUS is available via the EPA's Environmental Dataset Gateway (Baynes et al., 2021, https://doi.org/10.23719/1522948) and the EPA's EnviroAtlas https://www.epa.gov/enviroatlas, last access: 15 June 2022; Pickard et al., 2015).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.