  Fine population distribution both in space and in time is crucial for
epidemic management, disaster prevention,urban planning and more. Human
mobility data have a great potential for mapping population distribution at a
high level of spatiotemporal resolution. Power law models are the most popular
ones for mapping mobility data to population. However,they fail to provide
consistent estimations under different spatial and temporal resolutions, i.e.
they have to be recalibrated whenever the spatial or temporal partitioning
scheme changes. We propose a Bayesian model for dynamic population estimation
using static census data and anonymized mobility data. Our model gives
consistent population estimations under different spatial and temporal
resolutions.
