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<p>Identifying industrial clusters and tracing the evolution of their internal industrial chains are critical for both cluster theory and regional spatial planning. Existing cluster-identification studies have advanced from statistical classifications and input–output tables to interaction-based industrial networks. However, broad-coverage and cross-jurisdictionally comparable enterprise-level approaches remain limited, especially in metropolitan regions with heterogeneous data regimes. Focusing on the electronic information industry cluster in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study develops an enterprise-level, multi-source identification framework that combines semantic parsing of firm descriptions, neural-network-based industry classification, and industrial linkage network modeling. We dynamically delineate cluster boundaries and construct a cluster network knowledge graph. Comparative validation against prevailing cluster typologies indicates a high overall consistency, while the proposed framework further enriches the representation of value-chain segments and convergent industries that are insufficiently captured by existing classification systems. Empirically, the GBA cluster exhibits pronounced spatial–relational heterogeneity: some knowledge-intensive industry co-agglomerate geographically, whereas others maintain strong functional linkages despite spatial dispersion, suggesting functional complementarity beyond mere geographic proximity. The proposed approach provides a reusable pathway for fine-grained identification and dynamic monitoring of metropolitan clusters, and offers evidence to inform cross-jurisdictional coordination and pre-evaluation in industrial spatial governance.</p>

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Multi-Source Enterprise Data for Fine-Grained Industrial Cluster Delineation: A Spatial–Network Analysis with Policy Implications in the GBA

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Youwei Tan,
  • Jiayun Li,
  • Zhihui Gu

摘要

Identifying industrial clusters and tracing the evolution of their internal industrial chains are critical for both cluster theory and regional spatial planning. Existing cluster-identification studies have advanced from statistical classifications and input–output tables to interaction-based industrial networks. However, broad-coverage and cross-jurisdictionally comparable enterprise-level approaches remain limited, especially in metropolitan regions with heterogeneous data regimes. Focusing on the electronic information industry cluster in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study develops an enterprise-level, multi-source identification framework that combines semantic parsing of firm descriptions, neural-network-based industry classification, and industrial linkage network modeling. We dynamically delineate cluster boundaries and construct a cluster network knowledge graph. Comparative validation against prevailing cluster typologies indicates a high overall consistency, while the proposed framework further enriches the representation of value-chain segments and convergent industries that are insufficiently captured by existing classification systems. Empirically, the GBA cluster exhibits pronounced spatial–relational heterogeneity: some knowledge-intensive industry co-agglomerate geographically, whereas others maintain strong functional linkages despite spatial dispersion, suggesting functional complementarity beyond mere geographic proximity. The proposed approach provides a reusable pathway for fine-grained identification and dynamic monitoring of metropolitan clusters, and offers evidence to inform cross-jurisdictional coordination and pre-evaluation in industrial spatial governance.