探索使用在线食品配送数据进行城市内工作和住房流动性检测和表征
Exploring the Usage of Online Food Delivery Data for Intra-Urban Job and Housing Mobility Detection and Characterization
摘要：
人口流动在城市规划和决策中发挥着关键作用。然而，在某些空间和时间分辨率下，跟踪例如工作和住房流动性是非常具有挑战性的。在这项研究中，我们探讨了一种新的数据集模式，在线食品配送数据，检测工作和住房流动性的使用。通过利用中国北京一家流行的在线食品订购和配送服务的数百万份餐饮订单，我们能够以比使用传统数据源高得多的空间和时间分辨率检测工作和住房移动。可以很好地识别热门的移动季节和起点/目的地。更重要的是，我们将检测到的移动与宏观和微观因素相匹配，以表征工作和住房动态。我们的研究结果表明，通勤距离是工作和住房流动性的主要因素。我们还注意到：（1）对于搬家者来说，在城市空间结构下，住房成本降低和通勤距离缩短之间存在权衡;（2）对于跳槽者来说，经常加班的人更有可能通过换工作来减少工作时间。虽然这种新的数据集模式有其局限性，但我们认为集成方法将是有前途的，其中具有不同特征限制的多个数据集的混搭可以提供更全面的就业和住房动态。我们的工作证明了利用食品配送数据来检测和分析工作和住房流动性的有效性，并有助于实现基于整体的方法的全部潜力。
英文摘要：
Human mobility plays a critical role in urban planning and policy-making. However, at certain spatial and temporal resolutions, it is very challenging to track, for example, job and housing mobility. In this study, we explore the usage of a new modality of dataset, online food delivery data, to detect job and housing mobility. By leveraging millions of meal orders from a popular online food ordering and delivery service in Beijing, China, we are able to detect job and housing moves at much higher spatial and temporal resolutions than using traditional data sources. Popular moving seasons and origins/destinations can be well identified. More importantly, we match the detected moves to both macro- and micro-level factors so as to characterize job and housing dynamics. Our findings suggest that commuting distance is a major factor for job and housing mobility. We also observe that: (1) For home movers, there is a trade-off between lower housing cost and shorter commuting distance given the urban spatial structure; (2) For job hoppers, those who frequently work overtime are more likely to reduce their working hours by switching jobs. While this new modality of dataset has its limitations, we believe that ensemble approaches would be promising, where a mash-up of multiple datasets with different characteristic limitations can provide a more comprehensive picture of job and housing dynamics. Our work demonstrates the effectiveness of utilizing food delivery data to detect and analyze job and housing mobility, and contributes to realizing the full potential of ensemble-based approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
