通过融合邻近区域数据，提升疫情下城市移动预测精度
Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru
- 用相邻网格信息增强每个格子的特征，解决数据稀疏问题
- 在三种模型上测试，最高降低9.85%的预测误差
- 适合做公共卫生危机中时空预测的研究者参考
准确建模人类移动对理解疫情传播和及时干预至关重要。本文利用秘鲁全国数字接触追踪（DCT）应用在新冠疫情期间收集的大规模时空数据，预测城市区域间的移动流量。主要挑战在于小时级移动数量在六边形网格单元中存在空间稀疏性，限制了传统时间序列模型的预测能力。为此，我们提出一种轻量、与模型无关的邻域融合方法（SPN），将每个单元的特征与其周围H3邻居的聚合信号相结合。我们在NLinear、PatchTST和K-U-Net三种预测骨干模型上评估该策略，采用不同历史输入长度。实验表明，SPN在所有设置下均显著提升性能，测试集均方误差最高降低9.85%。结果证明，对稀疏移动信号进行空间平滑是提升公共卫生危机中时空预测鲁棒性的简单而有效路径。
原文摘要 · Abstract (English)
Accurate modeling of human mobility is critical for understanding epidemic spread and deploying timely interventions. In this work, we leverage a large-scale spatio-temporal dataset collected from Peru's national Digital Contact Tracing (DCT) application during the COVID-19 pandemic to forecast mobility flows across urban regions. A key challenge lies in the spatial sparsity of hourly mobility counts across hexagonal grid cells, which limits the predictive power of conventional time series models. To address this, we propose a lightweight and model-agnostic Spatial Neighbourhood Fusion (SPN) technique that augments each cell's features with aggregated signals from its immediate H3 neighbors. We evaluate this strategy on three forecasting backbones: NLinear, PatchTST, and K-U-Net, under various historical input lengths. Experimental results show that SPN consistently improves forecasting performance, achieving up to 9.85 percent reduction in test MSE. Our findings demonstrate that spatial smoothing of sparse mobility signals provides a simple yet effective path toward robust spatio-temporal forecasting during public health crises.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。