Population spatialization at building scale based on residential population index—A case study of Qingdao city

The study of population spatialization has provided important basic data for urban planning, development, environment and other issues. With the development of urbanization, urban residential buildings are getting higher and higher, and the difference between urban and rural population density is getting larger and larger. At present, most population spatial studies adopt the grid scale, and the population in buildings is evenly divided into various grids, which will lead to the neglect of the population distribution in vertical space, and the authenticity is not strong. In order to improve the accuracy of the population distribution, this paper studied the spatial distribution of population at the building scale, combined the digital surface model (DSM) and the digital elevation model (DEM) to calculate the floor of buildings, and proposed a new index based on the total floor area of residential buildings, called residential population index (RPI). RPI is directly related to the number of people a building can accommodate, so it can effectively estimate the population of both urban and rural areas even if the structure of urban and rural buildings is very different. In addition, this paper combined remote sensing monitoring data with geographic big data and adopted principal component regression (PCR) method to construct RPI prediction model to obtain building-scale population distribution data of Qingdao in 2018, providing ideas for population spatialization research. Through field sampling survey and overall assessment, the results were basically consistent with the actual residential situation. The average error with field survey samples is 14.5%. The R(2) is 0.643 and the urbanization rate is 69.7%, which are all higher than WorldPop data set. Therefore, this method can reflect the specific distribution of urban resident population, enhance the heterogeneity and complexity of population distribution, and the estimated results have important reference significance for urban management, urban resource allocation, environmental protection and other fields.