用手机传感器数据预测1平方公里内空气质量，精度远超传统方法。
Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data
- 基于时空图神经网络融合移动传感器数据
- 在未见坐标上实现79%的误差降低
- 发现短周期重复模式与动态空间关系
空气污染已成为发展中国家的重大健康风险。尽管政府定期发布空气质量指数（AQI）数据，但因传感器分布稀疏，难以反映局部真实情况。本文以AirDelhi数据集为例，通过时空图神经网络（Spatio-temporal GNNs）实现1平方公里尺度的精细化AQI预测，相比现有方法，均方误差（MSE）降低71.654，误差减少达79%，即使在未见坐标上仍表现优异。研究还发现了AQI中强重复性短期模式及变化的空间关联关系。代码已开源。
原文摘要 · Abstract (English)
Air pollution has become a significant health risk in developing countries. While governments routinely publish air-quality index (AQI) data to track pollution, these values fail to capture the local reality, as sensors are often very sparse. In this paper, we address this gap by predicting AQI in 1 km^2 neighborhoods, using the example of AirDelhi dataset. Using Spatio-temporal GNNs we surpass existing works by 71.654 MSE a 79% reduction, even on unseen coordinates. New insights about AQI such as the existence of strong repetitive short-term patterns and changing spatial relations are also discovered. The code is available on GitHub.
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