Using DPC-GNN to Exploit the Driving Effect of Cultural Industry Agglomeration on Regional Economic Growth from Multi-Source Data
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Abstract
In multi-source heterogeneous data fusion, traditional clustering methods often struggle to identify density peaks, distinguish blurred cluster boundaries, and quantify regional economic driving effects because of uneven distributions and inconsistent spatiotemporal scales. This paper proposes a DPC-GAT framework combining Density Peak Clustering with a multi-head Graph Attention Network. Landsat remote-sensing imagery, points of interest, and provincial statistical data are first integrated into unified spatiotemporal node representations, and DPC is used to automatically initialize cluster centers. Location encoding and Time2Vec temporal embedding are then introduced so that the multi-head GAT can adaptively adjust neighborhood weights and transmit multi-scale spatiotemporal information. Regional representations are constructed through intra-cluster weighted aggregation, and the regression head quantifies the contribution of cultural industry agglomeration to annualized GDP growth. Gradient-attention extraction is further used to identify key propagation paths and improve interpretability. Empirical results for 31 provinces in mainland China from 2020 to 2024 show a silhouette coefficient of 0.61, R2 of 0.86, and mining efficiency of 92.5%. The Beijing-related cluster contributes the most, at 12.46%, and forms a significant propagation path with the Fujian cluster.
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