College of Geodesy and Geomatics, Shandong University of Science and Technology, Shandong, China.
Shandong Provincial Institute of Land Surveying and mapping, Shandong, China.
PLoS One. 2022 May 26;17(5):e0269100. doi: 10.1371/journal.pone.0269100. eCollection 2022.
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 R2 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.
人口空间化研究为城市规划、发展、环境等问题提供了重要的基础数据。随着城市化的发展，城市住宅建筑越来越高，城乡人口密度差异越来越大。目前，大多数人口空间研究采用网格尺度，将建筑物中的人口平均分配到各个网格中，这将导致对垂直空间人口分布的忽视，真实性不强。为了提高人口分布的准确性，本文研究了建筑物尺度的人口空间分布，结合数字表面模型（DSM）和数字高程模型（DEM）计算建筑物的楼层，并提出了一个基于住宅建筑面积的新指标，称为住宅人口指数（RPI）。RPI 与建筑物可容纳的人数直接相关，因此即使城乡建筑物的结构非常不同，也可以有效地估计城乡人口。此外，本文结合遥感监测数据和地理大数据，采用主成分回归（PCR）方法构建 RPI 预测模型，获得 2018 年青岛市的建筑物尺度人口分布数据，为人口空间化研究提供了思路。通过现场抽样调查和综合评估，结果与实际居住情况基本一致。与现场调查样本的平均误差为 14.5%。R2 为 0.643，城市化率为 69.7%，均高于 WorldPop 数据集。因此，该方法可以反映城市常住人口的具体分布，增强人口分布的异质性和复杂性，估计结果对城市管理、城市资源配置、环境保护等领域具有重要的参考意义。
