Abstract
Spatial distribution and population density are important parameters in studies on urban development, resource allocation, emergency management, and risk analysis. High-resolution height data can be used to estimate the total or spatial pattern of the urban population for small study areas, e.g., the downtown area of a city or a community. However, there has been no case of population estimation for large areas. This paper tries to estimate the urban population of prefectural cities in China using building height data. Building height in urban population settlement (Mdiffs) was first extracted using the digital surface model (DSM), digital elevation model (DEM), and land use data. Then, the relationships between the census-based urban population density (CPD) and the Mdiffs density (MDD) for different regions were regressed. Using these results, the urban population for prefectural cities of China was finally estimated. The results showed that a good linear correlation was found between Mdiffs and the census data in each type of region, as all the adjusted R2 values were above 0.9 and all the models passed the significance test (95% confidence level). The ratio of the estimated population to the census population (PER) was between 0.7 and 1.3 for 76% of the cities in China. This is the first attempt to estimate the urban population using building height data for prefectural cities in China. This method produced reasonable results and can be effectively used for spatial distribution estimates of the urban population in large scale areas.
Highlights
The spatial pattern of regional populations is closely related to the geographical, societal, and economic environments, as well as to natural resources
The elevation difference between the digital surface model (DSM) and digital elevation model (DEM) data was first calculated, and the Mdiffs was extracted through land use data
Based on the Mdiffs density (MDD) and census-based urban population density (CPD) of the prefectural regions, China was divided into six region types using the adjusted box plot algorithm based on the K index; the regression functions through the origin between the MDD and CPD of the cities were fitted
Summary
The spatial pattern of regional populations is closely related to the geographical, societal, and economic environments, as well as to natural resources. Census data are relatively accurate; they are labor intensive and time consuming to obtain and update. They are based on the administrative unit and have low spatial resolution, and do not reflect dynamic changes in population in a timely manner [2,3]. These limitations make it necessary to develop alternative techniques and methods to improve the accuracy, time resolution, and spatial resolution
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