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Human mobility plays a critical role in urban planning and policy-making.However, at certain spatial and temporal resolutions, it is very challenging totrack, for example, job and housing mobility. In this study, we explore theusage of a new modality of dataset, online food delivery data, to detect joband housing mobility. By leveraging millions of meal orders from a popularonline food ordering and delivery service in Beijing, China, we are able todetect job and housing moves at much higher spatial and temporal resolutionsthan using traditional data sources. Popular moving seasons andorigins/destinations can be well identified. More importantly, we match thedetected moves to both macro- and micro-level factors so as to characterize joband housing dynamics. Our findings suggest that commuting distance is a majorfactor 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 distancegiven the urban spatial structure; (2) For job hoppers, those who frequentlywork overtime are more likely to reduce their working hours by switching jobs.While this new modality of dataset has its limitations, we believe thatensemble approaches would be promising, where a mash-up of multiple datasetswith different characteristic limitations can provide a more comprehensivepicture of job and housing dynamics. Our work demonstrates the effectiveness ofutilizing food delivery data to detect and analyze job and housing mobility,and contributes to realizing the full potential of ensemble-based approaches.

Exploring the Usage of Online Food Delivery Data for Intra-Urban Job and Housing Mobility Detection and Characterization