  Deep learning approaches for spatio-temporal prediction problems such as
crowd-flow prediction assumes data to be of fixed and regular shaped tensor and
face challenges of handling irregular, sparse data tensor. This poses
limitations in use-case scenarios such as predicting visit counts of
individuals' for a given spatial area at a particular temporal resolution using
raster/image format representation of the geographical region, since the
movement patterns of an individual can be largely restricted and localized to a
certain part of the raster. Additionally, current deep-learning approaches for
solving such problem doesn't account for the geographical awareness of a region
while modelling the spatio-temporal movement patterns of an individual. To
address these limitations, there is a need to develop a novel strategy and
modeling approach that can handle both sparse, irregular data while
incorporating geo-awareness in the model. In this paper, we make use of
quadtree as the data structure for representing the image and introduce a novel
geo-aware enabled deep learning layer, GA-ConvLSTM that performs the
convolution operation based on a novel geo-aware module based on quadtree data
structure for incorporating spatial dependencies while maintaining the
recurrent mechanism for accounting for temporal dependencies. We present this
approach in the context of the problem of predicting spatial behaviors of an
individual (e.g., frequent visits to specific locations) through deep-learning
based predictive model, GADST-Predict. Experimental results on two GPS based
trace data shows that the proposed method is effective in handling frequency
visits over different use-cases with considerable high accuracy.
