Rank-size relationship of built-up patches in China from the perspective of urban science
From the perspective of urban science,this study assumed that the development of built-up areas follows certain natural law.Using the conventional and reconstructed scaling expression of rank-size distribution models,this study analyzed built-up area and population size distributions,and examined whether the size of built-up areas follow the Gibrat's law.The conventional expression of rank-size distribution is from empirical patterns while the reconstructed one is mathematically derived from a hierarchical fractal system.Thus,the reconstructing process itself is to connect the empirical phenomenon to the hidden order,which is consistent with the perspective of urban science.Specifically,to match the area and population datasets,this study first applied city cluster algorithm to delineate all built-up areas in China,using the global impervious area data.Then the boundaries of built-up areas were used to aggregate population size from the LandScan world population product.The study found that the area size of built-up areas follows the rank-size distribution under both models,while the population size follows that only under the reconstructed model.Moreover,the Gibrat's law is not suitable for explaining the rank-size patterns of the built-up area system.The research results reveal the structural differences in the dynamic mechanism of urban development and population mobility within built-up area systems in China.Urban development shows a tendency of centralized management,and area expansion is easier/quicker than population growth.The potential contribution of this research is two-fold:first,the reconstructed scaling model can filter data noises algorithmically and thus can be used as an optimization algorithm for large sample size sequence-scale analysis;second,the reconstructed scaling model has a macroscopic structure that provides an explanation for rank-size distribution,that is,the rank-scale schema is a macroscopic pattern and hidden order,which needs to be rediscovered through reconstructing the empirical data.
万方数据
维普
