Us population density map 20183/31/2024 ![]() ![]() The advancement of remote sensing and geographic information system (GIS) technology has opened up new possibilities for calculating spatial population distribution weights 19. Itâs difficult to overlay census data with environmental data due to a lack of defined spatial references and consistent data units, which makes interdisciplinary study on human-environment systems limited 9.Ä®arly research used the population density model 10, 11, 12, 13 and different mathematical techniques of interpolation 14, 15, 16, 17, 18 to mimic the population distribution inside a census data unit. The genuine population data originates from official census data, however there are several limitations in practical applications, such as difficult to achieve scale conversion, a long update time, and the inability to provide specifics about the populationâs geographical distribution within administrative divisions 8. Human services and health 1, 2, disaster assessment 3, 4, global change 5, infrastructure construction and urban planning 6, human-environment coupling system 7 and other applications rely heavily on population spatial data. ![]() This research supplemented in the refined spatial distribution data of people between census years, as well as presenting the application technique of big data in ambient population estimation and zoning mapping. Extracting street-level (lower than county-level) statistics for accuracy testing, we found that POP2018 has the best fit with the actual permanent population (R 2â=â0.91), and the error is the smallest (MSE POP2018â=â22.48 log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latter to a 0.01° grid, and the ambient population data of mainland China was obtained. The county-level statistical population data in 2018 was used as the assigned input data. We take the average of Tencent user location big data as a measure of ambient population. Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. ![]()
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