  In response to the soaring needs of human mobility data, especially during
disaster events such as the COVID-19 pandemic, and the associated big data
challenges, we develop a scalable online platform for extracting, analyzing,
and sharing multi-source multi-scale human mobility flows. Within the platform,
an origin-destination-time (ODT) data model is proposed to work with scalable
query engines to handle heterogenous mobility data in large volumes with
extensive spatial coverage, which allows for efficient extraction, query, and
aggregation of billion-level origin-destination (OD) flows in parallel at the
server-side. An interactive spatial web portal, ODT Flow Explorer, is developed
to allow users to explore multi-source mobility datasets with user-defined
spatiotemporal scales. To promote reproducibility and replicability, we further
develop ODT Flow REST APIs that provide researchers with the flexibility to
access the data programmatically via workflows, codes, and programs.
Demonstrations are provided to illustrate the potential of the APIs integrating
with scientific workflows and with the Jupyter Notebook environment. We believe
the platform coupled with the derived multi-scale mobility data can assist
human mobility monitoring and analysis during disaster events such as the
ongoing COVID-19 pandemic and benefit both scientific communities and the
general public in understanding human mobility dynamics.
