  Dynamic high resolution data on human population distribution is of great
importance for a wide spectrum of activities and real-life applications, but is
too difficult and expensive to obtain directly. Therefore, generating
fine-scaled population distributions from coarse population data is of great
significance. However, there are three major challenges: 1) the complexity in
spatial relations between high and low resolution population; 2) the dependence
of population distributions on other external information; 3) the difficulty in
retrieving temporal distribution patterns. In this paper, we first propose the
idea to generate dynamic population distributions in full-time series, then we
design dynamic population mapping via deep neural network(DeepDPM), a model
that describes both spatial and temporal patterns using coarse data and point
of interest information. In DeepDPM, we utilize super-resolution convolutional
neural network(SRCNN) based model to directly map coarse data into higher
resolution data, and a time-embedded long short-term memory model to
effectively capture the periodicity nature to smooth the finer-scaled results
from the previous static SRCNN model. We perform extensive experiments on a
real-life mobile dataset collected from Shanghai. Our results demonstrate that
DeepDPM outperforms previous state-of-the-art methods and a suite of frequent
data-mining approaches. Moreover, DeepDPM breaks through the limitation from
previous works in time dimension so that dynamic predictions in all-day time
slots can be obtained.
