Tracing Footsteps of Similar Cities: Modeling Urban Economic Vitality with Dynamic Inter-City Graph Embeddings

• URL: http://arxiv.org/abs/2511.22325v1
• Date: Thu, 27 Nov 2025 10:55:11 GMT
• Title: Tracing Footsteps of Similar Cities: Modeling Urban Economic Vitality with Dynamic Inter-City Graph Embeddings
• Authors: Xiaofeng Li, Xiangyi Xiao, Xiaocong Du, Ying Zhang, Haipeng Zhang,
• Abstract summary: ECO-GROW is a framework to generate urban embeddings that model urban economic vitality.<n>It integrates industrial linkages, POI similarities, migration similarities and temporal network evolution over 15 years.<n> Experimental results demonstrate ECO-GROW's superior accuracy in predicting entrepreneurial activities and employment trends.
• Score: 10.847787808008023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban economic vitality is a crucial indicator of a city's long-term growth potential, comprising key metrics such as the annual number of new companies and the population employed. However, modeling urban economic vitality remains challenging. This study develops ECO-GROW, a multi-graph framework modeling China's inter-city networks (2005-2021) to generate urban embeddings that model urban economic vitality. Traditional approaches relying on static city-level aggregates fail to capture a fundamental dynamic: the developmental trajectory of one city today may mirror that of its structurally similar counterparts tomorrow. ECO-GROW overcomes this limitation by integrating industrial linkages, POI similarities, migration similarities and temporal network evolution over 15 years. The framework combines a Dynamic Top-K GCN to adaptively select influential inter-city connections and an adaptive Graph Scorer mechanism to dynamically weight cross-regional impacts. Additionally, the model incorporates a link prediction task based on Barabasi Proximity, optimizing the graph representation. Experimental results demonstrate ECO-GROW's superior accuracy in predicting entrepreneurial activities and employment trends compared to conventional models. By open-sourcing our code, we enable government agencies and public sector organizations to leverage big data analytics for evidence-based urban planning, economic policy formulation, and resource allocation decisions that benefit society at large.

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