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Analyzing Population Density Disparity in China with GIS-automated Regionalization: The Hu Line Revisited 被引量:3
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作者 WANG Fahui LIU Cuiling XU Yaping 《Chinese Geographical Science》 SCIE CSCD 2019年第4期541-552,共12页
The famous ’Hu Line’, proposed by Hu Huanyong in 1935, divided China into two regions(southeast and northwest) of comparable area size but drastically different in population. However, the classic Hu Line was derive... The famous ’Hu Line’, proposed by Hu Huanyong in 1935, divided China into two regions(southeast and northwest) of comparable area size but drastically different in population. However, the classic Hu Line was derived manually in absence of reliable census data and computational technologies of modern days. It has been subject to criticism of lack of scientific rigor and accuracy. This research uses a GIS-automated regionalization method, termed REDCAP(Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), to reconstruct the demarcation line based on the 2010 county-level census data in China. The results show that the logarithmic transformation of population density is a better measure of attributive homogeneity in derived regions than density itself, and produces two regions of nearly identical area size and greater contrast in population. Specifically, the revised Hu Line by Hu Huanyong in 1990 had the southeast region with 94.4% of total population and 42.9% of total land, and our delineation line yields a southeast region with 97.4% population and 50.8% land. Therefore, the population density ratio of the two regions is 27.1 by our line, much higher than the ratio of 22.4 by the Hu Line, and thus outperforms the Hu Line in deriving regions of maximum density contrast with comparable area size. Furthermore, more regions are delineated to further advance our understanding of population distribution disparity in China. 展开更多
关键词 HU LINE regional population density DISPARITY GIS-automated regionalization redcap (regionalization with dynamically constrained agglomerative clustering and partitioning) China
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