![]() However, such imagery data only became available beginning with the launch of the first land satellite, Landsat-1, in 1972. Land use/land cover data, derived from remote sensing images, best reflect population density and have been suggested as an important source for human population mapping 19, 28. Major influencing factors that have been considered in human population mapping include land cover, night lights, topography, urban areas, and roads 25– 27. While the temporal range from HYDE is vast (from 1000 BC to 2005 AD), the 10-km spatial resolution is relatively low, constrained by input census data. A notable exception is the History Database of the Global Environment (HYDE) 24. The high resolution mapping efforts described above were all developed based on modern census data since 1990, and are of limited utility for long-term (e.g., multi-decadal) dynamic analyses 22, 23. Similar challenges from insufficient data availability occur in estimating historical population distributions. The modern revolution in geospatial data availability greatly facilitates dasymetric mapping and the creation of more accurate data on population distribution 13, including the Gridded Population of the World (GPW) 14, the Global Rural Urban Mapping Project (GRUMP) 15, LandScan 16, and WorldPop 17.Ī recent challenge has been to map the human population distribution in low income nations, where detailed updated census data or high resolution geospatial data are lacking 18– 21. ![]() Dasymetric mapping is an areal interpolation to disaggregate data from a set of areal units into finer units using ancillary data 12. These data could provide strong support for improved understanding of human-environment interactions, if more refinement was possible within the administrative units. For the conterminous US, census data are available at the county level from 1790 to 2010 (ref. Therefore, it is vital to create reliable spatially explicit, high-resolution estimates of the human population distribution to advance our understanding of coupled human and natural systems, to provide support to policy decision-making, and to achieve ecological and socioeconomic sustainability 8.Ĭensus data have been routinely collected and applied, however, such data are ascribed to defined administrative units, leading to abrupt changes in population at the administrative boundary, and masking of spatial heterogeneity within administrative units 9, 10. Human population density is considered to be a useful indicator of the type and intensity of the human environment interaction, with higher population density leading to higher levels of impacts 4– 7. Such ecological and societal consequences vary across space. Human actions have caused substantial alterations to the Earth, transforming the landscape, affecting ecosystem patterns and processes, driving biodiversity loss, altering global hydrological and biogeochemical cycles, amplifying resource exploitation and environmental deterioration, and contributing to climate change 1– 3. The generated gridded population datasets and the production and validation methods are described here. Separating urban and rural areas and excluding non-inhabitable areas were the most important factors for improving the overall accuracy. The models were validated with census tract and county subdivision population data in 2000 and were applied to generate five sets of 22 historical population maps from 1790–2010. Five models of increasing complexity were evaluated. This study generated 1-km decadal population maps for the conterminous US from 1790 to 2010 using parsimonious models based on natural suitability, socioeconomic desirability, and inhabitability. However, the complex relationship between population distribution and various influencing factors coupled with limited data availability make it a challenge to reconstruct human population distribution over timescales of centuries. ![]() Where do people live, and how has this changed over timescales of centuries? High-resolution spatial information on historical human population distribution is of great significance to understand human-environment interactions and their temporal dynamics.
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