Detecting and Evaluating Urban Agglomerations in Mainland China From the Perspective of Spatial Interactions

It has been a long-standing topic to detect the formation of urban agglomerations; however, current studies remain constrained by the definition of criterion or the usage of remote sensing technique, ignoring the impact of spatial interactions. This study aims to develop a methodological framework to detect and evaluate urban agglomerations by fusing human activities and their spatial interactions. To verify our methods, experiments were conducted in Mainland China, where nighttime light imageries and human movement data were used. 1) Urban boundaries of all cities were extracted from nighttime light imageries using the Lorenz curve and the watershed method; 2) they were combined with human movement data to construct the spatial interaction network, and urban agglomerations were then derived using the network community detection method; 3) the development levels of urban agglomerations were evaluated quantitatively according to the National Thirteenth Five-Year Plan (NTFP). Experimental results suggest that a total of 17 urban agglomerations were derived, and compared with those planned in the NTFP, they can be classified into four categories with different levels of development. Our methods can enrich studies on detection of urban agglomerations, while experimental results can provide decision-making support for regional management and development of planning policies.