  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.
