What is it about?
Predicting Human Mobility During COVID-19 Using Graph Neural Networks
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Why is it important?
This research is unique and timely because it addresses the changes in human mobility due to COVID-19, which are crucial for infrastructure redesign and emergency management. Our model, using Cross- and Context-Aware Attention based Spatial-Temporal Graph Convolutional Networks (CCAAT-GCN), accurately predicts mobility patterns by capturing the relationship between COVID-19 cases and human movement. This can significantly aid planners and policymakers in future pandemic preparedness and response.
Perspectives
Most existing studies are autoregressive, relying solely on historical mobility data to predict future trends. This paper aims to fill this research gap by incorporating additional features, such as COVID-19 case rates and demographic data. Our findings demonstrate that these additional factors play a significant role in influencing mobility patterns.
Zhaobin Mo
Columbia University
Read the Original
This page is a summary of: Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction, ACM Transactions on Spatial Algorithms and Systems, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3673227.
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