Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2833-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2833-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Jun 2022
Data description paper |
| 23 Jun 2022
| 23 Jun 2022
Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Anne Neale
Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Torrin Hultgren
EPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA
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Cited
20 citations as recorded by crossref.
- Incorporating spatial autocorrelation in dasymetric mapping: A hierarchical Poisson spatial disaggregation regression model B. He et al. 10.1016/j.apgeog.2024.103333
- Measuring access to and availability of outdoor recreational opportunities: One pixel at a time A. Killea et al. 10.1016/j.landurbplan.2025.105445
- Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity J. Zhang & X. Zhao 10.1016/j.scitotenv.2023.169499
- Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019 X. Yan et al. 10.1038/s41597-024-03684-4
- Multi-source tri-environmental conceptual framework for fire impact analysis Z. Li et al. 10.1007/s44212-024-00063-7
- A three-weight surface modeling approach for optimizing small-scale population disaggregation Y. Zhou et al. 10.1007/s11707-024-1150-5
- Dasymetric Mapping of Population Using Land Cover Data in JBNERR, Puerto Rico during 1990–2010 M. Cartagena-Colón et al. 10.3390/land11122301
- A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data Y. Chen et al. 10.5194/essd-16-3705-2024
- Net zero targets in science and policy J. Rogelj 10.1088/1748-9326/acb4ae
- Flood injustice in 500-year floodplains A. Farshid & S. Null 10.3389/fhumd.2025.1496686
- The SESAME Human-Earth Atlas A. Faisal et al. 10.1038/s41597-025-05087-5
- Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul J. Bersabe & B. Jun 10.3390/ijgi14070262
- Future fire risk under climate change and deforestation scenarios in tropical Borneo T. Davies-Barnard et al. 10.1088/1748-9326/acb225
- Infrastructure as Environmental Health Policy: Lessons from the Clean School Bus Program’s Challenges and Innovations U. Osia et al. 10.3390/ijerph22081232
- An ANN-based method for population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021 W. Lu & Q. Weng 10.1016/j.compenvurbsys.2024.102072
- Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020 J. Ji et al. 10.3390/systems11100500
- Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making G. Ashiagbor et al. 10.1080/15715124.2024.2411237
- Federally-overlooked flood risk inequities in the conterminous United States A. Flores et al. 10.1038/s41598-025-95120-9
- Analysis of the differences and influencing factors of rural population aging in Gansu Province—based on panel data from 2000 to 2022 Y. Liu et al. 10.3389/fpubh.2025.1634712
- A dataset of US precinct votes allocated to Census geographies with precision A. Fekrazad 10.1038/s41597-025-05140-3
20 citations as recorded by crossref.
- Incorporating spatial autocorrelation in dasymetric mapping: A hierarchical Poisson spatial disaggregation regression model B. He et al. 10.1016/j.apgeog.2024.103333
- Measuring access to and availability of outdoor recreational opportunities: One pixel at a time A. Killea et al. 10.1016/j.landurbplan.2025.105445
- Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity J. Zhang & X. Zhao 10.1016/j.scitotenv.2023.169499
- Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019 X. Yan et al. 10.1038/s41597-024-03684-4
- Multi-source tri-environmental conceptual framework for fire impact analysis Z. Li et al. 10.1007/s44212-024-00063-7
- A three-weight surface modeling approach for optimizing small-scale population disaggregation Y. Zhou et al. 10.1007/s11707-024-1150-5
- Dasymetric Mapping of Population Using Land Cover Data in JBNERR, Puerto Rico during 1990–2010 M. Cartagena-Colón et al. 10.3390/land11122301
- A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data Y. Chen et al. 10.5194/essd-16-3705-2024
- Net zero targets in science and policy J. Rogelj 10.1088/1748-9326/acb4ae
- Flood injustice in 500-year floodplains A. Farshid & S. Null 10.3389/fhumd.2025.1496686
- The SESAME Human-Earth Atlas A. Faisal et al. 10.1038/s41597-025-05087-5
- Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul J. Bersabe & B. Jun 10.3390/ijgi14070262
- Future fire risk under climate change and deforestation scenarios in tropical Borneo T. Davies-Barnard et al. 10.1088/1748-9326/acb225
- Infrastructure as Environmental Health Policy: Lessons from the Clean School Bus Program’s Challenges and Innovations U. Osia et al. 10.3390/ijerph22081232
- An ANN-based method for population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021 W. Lu & Q. Weng 10.1016/j.compenvurbsys.2024.102072
- Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020 J. Ji et al. 10.3390/systems11100500
- Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making G. Ashiagbor et al. 10.1080/15715124.2024.2411237
- Federally-overlooked flood risk inequities in the conterminous United States A. Flores et al. 10.1038/s41598-025-95120-9
- Analysis of the differences and influencing factors of rural population aging in Gansu Province—based on panel data from 2000 to 2022 Y. Liu et al. 10.3389/fpubh.2025.1634712
- A dataset of US precinct votes allocated to Census geographies with precision A. Fekrazad 10.1038/s41597-025-05140-3
Latest update: 12 Aug 2025
Short summary
Census data are typically provided in irregularly shaped spatial units. To get a more refined estimate of population density, we downscaled population counts from United States (US) census blocks to a 30 m grid using intelligent dasymetric mapping. Furthermore, we improved our density estimates by using multiple spatial datasets to identify and mask uninhabited areas. Masking these uninhabited areas improved density estimates for every state in the conterminous US.
Census data are typically provided in irregularly shaped spatial units. To get a more refined...
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