Abstract
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.
Highlights
Population spatialization is a technique that inverts the population data at a certain point in the study area into a region distribution close to the real population, and solves the spatial limitation of census data
The results show that, under the combined action of various natural and social factors, after super resolution convolutional neural network (SRCNN) "gettingaccuracy", the five schemes all show good simulation results
The main conclusions are as follows: (1) Under the combined effects of natural environmental factors and socio-economic factors, SRCNN has a good ability to learn downscaling, but according to the results of scheme 1 and 2, it can be seen that the 9 × 9 convolution kernel used by SRCNN Extracted are very local features, which are affected by complex topography, causing overestimation in some areas with a relatively small population, failing to grasp the overall characteristics of population distribution
Summary
Population spatialization is a technique that inverts the population data at a certain point in the study area into a region distribution close to the real population, and solves the spatial limitation of census data. With the development of remote sensing and GIS technology, multi-source heterogeneous data such as land cover data[10], buildings[11], residential areas[1], and night lights are used in spatial interpolation models to make the population spatialization results more realistic, the most common of which is the method of partition density mapping[12,13]. In view of the global model's difficulty in characterizing the heterogeneity of population spatial distribution, local models such as geographic weighted regression[23,24] and super resolution convolutional neural network (SRCNN) have recently been introduced into population spatialization practice. Based on the commonality of single-image super-resolution and downscaling technology[25], Zong introduced the SRCNN model for the first time to Shanghai's population spatialization research, and obtained better results[26].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
