Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering dynamic evolving nature extent USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect characterize evolution We propose a minimum spanning tree (DMST) subgraph partitioning method identify over time, which considers both proximity built-up areas their affiliations with at previous snapshot. China is selected as case for its rapid urbanization process cluster-based economic development strategy. Four DMSTs generated using extracted from DMSP/OLS NTL collected in 2000, 2004, 2008, 2012. Each DMST partitioned into various subtrees connected by same subtree identified potential USC. By inspecting three different types obtained, including newly emerging, single-core, multicore clusters. Using rank-size distribution, we find that large-sized have greater than medium- small-sized A clear directionality heterogeneity observed expansions ten largest Our provides further insight understanding system structures, assists policymakers planning practices national regional scales.