Research on the Spatial Clustering of Chinese Cities Based on Complex Network Analysis

Dataset Description TranslationThis dataset is exclusively for the research project titled "Study on the Division of China's Urban Spatial Clusters Based on Complex Network Analysis". It covers core data supporting the identification and division of spatial clusters for over 360 cities in China, including both raw data and processed data. By leveraging remote sensing technology and complex network methods, the data aims to objectively present the geographical proximity and connection characteristics of cities. It provides data support for urban agglomeration classification (optimization and upgrading, growth and expansion, cultivation and development) and policy formulation, and also serves as basic data reference for subsequent urbanization research and regional planning-related analyses.Urban built-up area data in ChinaThis data is based on 30m-resolution remote sensing data acquired by the Operational Land Imager (OLI) of the Landsat 8 satellite, including raw image files of Band 5, Band 4, and Band 3. The process to obtain the built-up area data involves the following steps: first, generating standard false-color images through band fusion; second, conducting land-use classification using the Support Vector Machine (SVM) method; finally, extracting built-up areas by combining administrative boundaries, with accuracy verified via a confusion matrix.Road raw dataRoad data of all class-level and above highways in China was obtained from the OpenStreetMap platform. The processing of this raw road network includes data cleaning and deduplication. A distance measurement tool is used to calculate multi-route distances between cities, and the shortest path between each pair of cities is selected to form the Processed road network. Subsequently, cities are abstracted as nodes and the shortest-path road network as edges. The max spanning tree is constructed based on the Kruskal algorithm, and pruning is performed by setting a threshold using betweenness centrality. Finally, the spatial scope of urban clusters is determined in combination with the convex hull algorithm.