Research on Population Spatialization Method Based on PMST-SRCNN

How to improve the accuracy of population spatialization by using downscaling technology has always been a difficult issue in academic research. The population spatialization model constructed from the global or local perspective alone has its own limitations that cannot capture the local and global characteristics of the population distribution. Based on the counties of Chongqing municipality in 2010, this paper uses the two steps of “removing-rough” rasterizationof partitioned multivariate statistical regression and the “getting-accuracy” of super-resolution convolutional neural network to construct a coupling model of population spatialization to complete global and local Feature learning and compare and analyze with other four schemes. The results show that the mean square error and root mean square error of the coupled model of partitioned multivariate statistical regression and super-resolution convolutional neural network are the lowest, especially in densely populated areas. Studies have shown that although super-resolution convolutional neural network has a good ability to downscale learning, it still does not reflect the heterogeneity of population spatial patterns well, and the coupling of multilevel global feature learning models and super-resolution convolutional neural network models can make up for this to a certain extent.