Population spatialization based on geographically weighted regression model considering spatial stability of parameters
XIAO Dongsheng1,2,3(), LIAN Hong1,2()
1. School of Civil Engineering and Surveying and Mapping, Southwest Petroleum University, Chengdu 610500, China 2. Disaster Prevention and Emergency Research Center of Mapping and Remote Sensing Geographic Information of Southwest Petroleum University, Chengdu 610500, China 3. Public Security and Emergency Research Institute, Sichuan Normal University, Chengdu 610068, China
The theories on population spatialization tend to be mature in recent years. However, the spatial stability of the variables and parameters used in population spatialization modeling has been scarcely focused on. With the land use data, night-time light data, and demographic data as the data sources, this study proposed a novel precise population spatialization method based on a semi-parametric geographically weighted regression model (S-GWR). Then a permanent population spatialization model on a county scale was built using the method proposed in this study and then was verified using the Sichuan Province as the study area. In this study, the spatial stability of parameters and variables were obtained using the S-GWR model while the characteristics of the variables were analyzed, in order to improve the accuracy of population estimation. Finally, the population spatial distribution map (SDP) with a resolution of 1 km of Sichuan Province in 2010 was formed. The results show that the coefficient of determination coefficient of the S-GWR model was 0.903, which is higher than that of traditional regression models and indicates better fitting effects. The S-GWR model was verified using two commonly used population datasets, and the verification results are as follows. At a county level, the overall average error of the study area and the relative error of each district and county in the SDP all approximated to 0, and thus the SDP was more precise than the other two datasets. At a township level, the mean relative error, mean absolute error, and root mean square error of SDP were 34.54%, 5 715.703, and 12 085.932, respectively, which were all lower than those of the other two datasets. Meanwhile, the SDP showed more favorable dispersion effects than the other datasets. Furthermore, the number of the towns whose population was accurately estimated was 185 in the SDP, which was higher than that in the other two datasets. Therefore, the accuracy of population spatialization can be improved by considering the spatial stability of parameters.
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