This dataset comprehensively covers the distribution of population density in the Chaohu Lake Basin, the Chu River Basin, the Lishiahe River area and the Yangtze River Delta Demonstration Area at three key time points, 2000, 2010 and 2020, with refined statistics and analysis by elevation bands. Adopting high-precision remote sensing analysis technology with 12.5-meter spatial resolution, the dataset provides detailed statistics on the number of people within each elevation band and the population of the whole basin.
The elevation band delineation method provides a deeper understanding of the complex relationship between population distribution and topographic features, and how this relationship evolves over time. It is of great significance for assessing the impact of topographic conditions on human settlement choices, predicting future population distribution patterns, and formulating targeted regional development plans. In addition, by comparing the data at the three time points, the dataset also demonstrates the dynamic change characteristics of population distribution over time, providing researchers with valuable time series data support. These changing features not only reflect the changes in socio-economic conditions, but also imply the influence of the natural environment on the spatial distribution of human activities.
This dataset provides a detailed data base for understanding the distribution of population density and its relationship with topographic elevation in the Chaohu Lake Basin, the Chu River Basin, the Lishiahe River area and the Yangtze River Delta Demonstration Area, and it is of great value for research and practice in the fields of regional planning, population management and environmental protection.
Population raster data for the Chaohu Lake Basin, the Chu River Basin, the Lishiahe River Region, and the Yangtze River Delta Demonstration Area in 2000, 2010, and 2020 were collected from census data released by the national and local statistical bureaus, economic statistics data, and specialized GIS data sources.
(1) Classify the population raster data according to the preset elevation zones, using the “raster reclassification” tool of ArcGIS to get the population distribution raster of each elevation zone.
(2) Extract the population of each elevation zone: Use the “Regional Analysis” or “Extract by Mask” function of ArcGIS to extract the total population of each elevation zone from the population raster data, using the raster of the elevation zone as a mask. The total population of each elevation zone is extracted from the population raster data using the elevation zone raster as a mask.
This dataset focuses on the population density situation in the Chaohu Lake Basin, the Chu River Basin, the Lishiahe River area, and the Yangtze River Delta Demonstration Area, aiming at dynamically tracking and parsing the changes in the spatial and temporal distribution of the population, the economy, and the important objects of protection in the basin. Combined with the spatial distribution information of topography, polder, embankment, etc., as well as flood inundation and risk information, these data are utilized to assess the changes in exposure and danger of the disaster-bearing body, to identify the main risk points and risk objects in the basin, and to carry out timely investigation.
A variety of authoritative data sources were used, including census data released by national and local statistical bureaus, economic statistics, and specialized GIS data sources to ensure the authority and accuracy of the data. Using GIS software and algorithms, high-precision spatial analyses were carried out, including population density calculations and analysis of changes in spatial and temporal distribution, to ensure that the spatial distribution and accuracy of the data met the requirements of high standards.
| # | number | name | type |
| 1 | 2021YFC3000100 | Lower Yangtze River Flood Disaster Integration and Control and Emergency De-risking Technology and Equipment | National key R & D plan |
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CSTR:11738.11.NCDC.NHRI.DB6775.2025
10.12072/ncdc.nhri.db6775.2025