Towards Predicting Urban Land Use Changes: A Dynamic Graph Alignment Perspective

Urban land use, intrinsically linked to people’s daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these changes, adopting a novel approach that conceptualizes “community-level” land use shifts as a regression problem and represents citywide changes through dynamic graphs. We harness recent advancements in graph neural networks (GNNs), which, despite their success in various applications, face challenges in directly predicting land use changes due to the temporal mismatch between the slow evolution of urban land and the immediacy of human mobility data. Our research stands out by introducing a temporal skeleton for dynamic GNNs to synchronize human activity graphs with urban land use changes, a dynamic heterogeneous GNN approach for integrating diverse human activity data to capture essential temporal dependencies, and a novel algorithm powered by causal inference to elucidate the primary factors influencing land use predictions at the community level, all of which contribute to a training process informed by the generated causal graph. Empirically validated on three real-world datasets, our model demonstrates a performance leap over state-of-the-art baselines, marking a pivotal step toward understanding and predicting the dynamics of urban land use.

Authors

• Yu Fan SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, P.R.China
• Xinjiang Lu Baidu Research, Beijing, China
• Hao Liu The Hong Kong University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology
• Pengfei Wang
• Liang Liu Tencent
• Huadong Ma The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications
• Jingbo Zhou Baidu Research

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