Identification of Urban Clusters Based on Multisource Data—An Example of Three Major Urban Agglomerations in China

Abstract

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

An Improved Method to Identify Built-Up Areas of Urban Agglomerations in Eastern and Western China Based on Multi-source Data Fusion

“…By verifying algorithm accuracy on high-precision datasets, our fused method achieved an overall accuracy of 91.35% with a kappa coefficient of 0.75. This precision result is higher than the overall accuracy of 85.34% and the kappa coefficient of 0.7394 of the built-up areas of the three major urban agglomerations studied by Wang et al [37].…”

“…The results of this study align with previous research findings. Wang et al [37] discovered that the average kappa coefficient of the results of multi-method fusion can reach 0.7394, which accurately reflects the geographical scope of the three major urban agglomerations in China. He et al [38] found that fusing multi-source big data can help accurately evaluate polycentric spatial structure within urban agglomeration.…”

Spatial Planning Strategies for Wetlands Based on a Multimethod Approach: The Example of Tianjin in China

“…The research approach employed in this paper encompasses two main components, analysis and strategy, which correspond to problem analysis and problem-solving, respectively, conforming to the essential steps of spatial planning [40,41]. Expanding on the foundation laid by the analysis and strategy, this study introduces a validation element aimed at ensuring the scientific rigor of the planning strategies.…”