  Timely and high-resolution estimates of the home locations of a sufficiently
large subset of the population are critical for effective disaster response and
public health intervention, but this is still an open problem. Conventional
data sources, such as census and surveys, have a substantial time lag and
cannot capture seasonal trends. Recently, social media data has been exploited
to address this problem by leveraging its large user-base and real-time nature.
However, inherent sparsity and noise, along with large estimation uncertainty
in home locations, have limited their effectiveness. Consequently, much of
previous research has aimed only at a coarse spatial resolution, with accuracy
being limited for high-resolution methods. In this paper, we develop a
deep-learning solution that uses a two-phase dynamic structure to deal with
sparse and noisy social media data. In the first phase, high recall is achieved
using a random forest, producing more balanced home location candidates. Then
two deep neural networks are used to detect home locations with high accuracy.
We obtained over 90% accuracy for large subsets on a commonly used dataset.
Compared to other high-resolution methods, our approach yields up to 60% error
reduction by reducing high-resolution home prediction error from over 21% to
less than 8%. Systematic comparisons show that our method gives the highest
accuracy both for the entire sample and for subsets. Evaluation on a real-world
public health problem further validates the effectiveness of our approach.
