标题： 通过通勤网络的视角进行城市界定：利用图嵌入区分城市中的社会经济群体 显示英文标题

标题： Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities

摘要： 界定大都市区域内的区域是城市研究人员的重要焦点，有助于揭示由不断变化的人口动态塑造的城市边界。 在城市科学中的应用众多，从促进界定的区域与行政划分之间的比较，到向政策制定者提供不断变化的经济和劳动力景观的信息。 在本研究中，我们提出使用来自人口普查的通勤网络用于城市界定，通过用图神经网络（GNN）架构对其进行建模。 我们使用GNN获取细致城市区域（节点）的低维表示。 随后，对节点的嵌入进行聚类，以识别城市区域中的空间一致社区。 我们在全美范围内的实验表明，网络嵌入在捕捉不同城市社区之间显著的社会经济差异方面是有效的，特别是在中位数家庭收入等因素上。 还注意到人口普查移动数据在区域界定中的作用，并确立了GNN在城市社区检测中的实用性，作为该领域现有方法的强大替代方案。 结果为通勤网络的更广泛影响及其在构建城市区域有意义表示中的应用提供了见解。 显示英文摘要

摘要： Delineating areas within metropolitan regions stands as an important focus among urban researchers, shedding light on the urban perimeters shaped by evolving population dynamics. Applications to urban science are numerous, from facilitating comparisons between delineated districts and administrative divisions to informing policymakers of the shifting economic and labor landscapes. In this study, we propose using commute networks sourced from the census for the purpose of urban delineation, by modeling them with a Graph Neural Network (GNN) architecture. We derive low-dimensional representations of granular urban areas (nodes) using GNNs. Subsequently, nodes' embeddings are clustered to identify spatially cohesive communities in urban areas. Our experiments across the U.S. demonstrate the effectiveness of network embeddings in capturing significant socioeconomic disparities between communities in various cities, particularly in factors such as median household income. The role of census mobility data in regional delineation is also noted, and we establish the utility of GNNs in urban community detection, as a powerful alternative to existing methods in this domain. The results offer insights into the wider effects of commute networks and their use in building meaningful representations of urban regions.