Improving intelligent dasymetric mapping population density estimates at 30-meter resolution for the conterminous United States by excluding uninhabited areas
- 1Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- 2EPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA
- 1Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- 2EPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA
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 EPA’s EnviroAtlas Dasymetric Population Map for 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 U.S Census Bureau blocks improved across all states in CONUS. We also found that IDM performed better in states with larger urban areas than in states that are sparsely populated. Future updates of the Dasymetric Population Map might benefit from stratified (e.g., urban/exurban/rural) multi-state sampling of population density rather than state-specific sampling. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas.
Jeremy Baynes et al.
Status: final response (author comments only)
-
RC1: 'Comment on essd-2021-277', Anonymous Referee #1, 08 Apr 2022
Based on the intelligent dasymetric mapping (IDM) method, the authors generated a set of high resolution population density product by using various datasets. This is interesting and significant since population change impacts almost every aspect of global change. However, it is noteworthy that the method described in this paper is not clear. To make it more persuasive, some suggestions includes:1) To better present how the data was processed and used, a flow chart is recommended; 2) It’s better to list the spatial resolution for the raster data, and the feature types of vector data, such as point, line or polygon.
-
CC1: 'Comment on essd-2021-277', Anna Dmowska, 16 Apr 2022
The paper describes 30m resolution population density maps for the US. The resultant map is a product of disaggregating 2010 US Census block-level data using an NLCD and additional geospatial layers to identify uninhabited areas. The paper describes only EPA EnviroAtlas Dasymetric Population maps and does not mention other high-resolution population maps available for the entire conterminous US. For example, a 30m resolution population density maps for the conterminous US has been already created and it is available via the SocScape project (http://socscape.edu.pl, see paper for further information https://www.sciencedirect.com/science/article/pii/S0198971516301983). The 2010 map available via the SocScape project is a product of disaggregating block-level population data using NLCD2011 and land use map for further identification of uninhabited areas (Theobald 2014) and uses a similar approach to dasymetric modeling (Mennis and Hultgren, 2006).Â
Authors should describe other high-resolution maps available for the US and discuss how their product advances the map that already exists (available via the SocScape project)
-
RC2: 'Comment on essd-2021-277', Anonymous Referee #2, 23 Apr 2022
The authors describe the application of the IDM method to further improve the existing EPA EnviroAtlas Dasymetric Population Map for conterminous United States, mostly through the further expansion of uninhabitable areas. The resulting data product shows decreased estimation error in an assessment using census tract data as input and blocks as test units. The fine resolution of the gridded population density estimates will be very useful for various applications as described in the introduction of the manuscript.
While the value of such an improved dasymetric map is unquestionable given the resolution and the spatial extent (CONUS), there are some reservations that the authors are encouraged to address:
- The result of defining more extensive and spatially expanded uninhabitable areas and using them for dasymetric refinement is very intuitive and expected. This strategy is about using robust constraining variables to reach estimates of higher accuracy.
- This makes this study a rather incremental step to revise and improve an existing data product by including additional data layers that allow to identify more uninhabitable areas.
- IDM is a slightly dated (though still effective) method for population refinement. Given the nature of the data and the statistical frameworks available it is surprising that the authors have not tested different methods (e.g., machine learning) to cross-compare performance and resulting data quality. The given problem at hand seems to be one that could be treated effectively with learning frameworks.
- Estimating the average population density of target zones across a whole state is not effective and represents a well-known limitation of IDM. The authors mention the further refinement of their estimation procedure for urban and rural classes as future work. However, it seems that this would have been an important step already in this data product revision. The estimation of population densities for different urban classes (intensity of development) will result in much more accurate data layers. This would allow the model to proceed at the scale at which variation is observed across counties and states.
General: The different data layers and processing steps need more precise descriptions. The authors are also encouraged to provide a broader background on dasymetric modeling including uncertainty concerns and typical limitations reported in the literature. Also, there are other fine-resolution population datasets, and it is recommended that the authors make sure their data layers are compared to those existing products.
Jeremy Baynes et al.
Data sets
2010 Dasymetric Population for the Conterminous United States v3 Baynes, J., Neale, A., Hultgren, T. https://doi.org/10.23719/1522948
Model code and software
USEPA / Dasymetric-Toolbox-OpenSource Khan, A., Baynes, J. https://github.com/USEPA/Dasymetric-Toolbox-OpenSource
Jeremy Baynes et al.
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
298 | 89 | 16 | 403 | 9 | 9 |
- HTML: 298
- PDF: 89
- XML: 16
- Total: 403
- BibTeX: 9
- EndNote: 9
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1