用通勤网络预测城市经济，不依赖人口数据也能精准建模。
Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models
- 用普查通勤数据构建城市交通网络，作为经济预测核心输入。
- 模型在12个大城市中超越传统机器学习方法，仅靠网络结构即实现高精度预测。
- 适合城市规划者与政策制定者，揭示交通网络对社会经济的影响。
城市经济社会建模长期依赖位置和社区特征，忽视了城市系统中的网络效应。本文提出利用普查通勤数据构建跨城市的移动网络，作为全面且可靠的输入。通过深度学习架构，我们在美国12个主要都市区使用这些通勤网络进行经济社会指标建模。结果表明，仅基于网络结构即可实现显著预测性能，无需任何节点特征。我们构建了一个监督学习框架，结合图神经网络（Graph Neural Network）与全连接神经网络（Vanilla Neural Network），在单一学习流程中联合优化所有参数。实验显示，该模型优于以往的常规机器学习方法。本研究为城市研究人员提供了纳入网络效应的建模方法，并为政策制定者提供了更广泛的网络影响视角。
原文摘要 · Abstract (English)
Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.
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