  Open data is frequently released spatially aggregated, usually to comply with
privacy policies. But coarse, heterogeneous aggregations complicate learning
and integration for downstream AI/ML systems. In this work, we consider models
to disaggregate spatio-temporal data from a low-resolution, irregular partition
(e.g., census tract) to a high-resolution, irregular partition (e.g., city
block). We propose an overarching model named the Structurally-Aware Recurrent
Network (SARN), which integrates structurally-aware spatial attention (SASA)
layers into the Gated Recurrent Unit (GRU) model. The spatial attention layers
capture spatial interactions among regions, while the gated recurrent module
captures the temporal dependencies. Each SASA layer calculates both global and
structural attention -- global attention facilitates comprehensive interactions
between different geographic levels, while structural attention leverages the
containment relationship between different geographic levels (e.g., a city
block being wholly contained within a census tract) to ensure coherent and
consistent results. For scenarios with limited historical training data, we
explore transfer learning and show that a model pre-trained on one city
variable can be fine-tuned for another city variable using only a few hundred
samples. Evaluating these techniques on two mobility datasets, we find that on
both datasets, SARN significantly outperforms other neural models (5% and 1%)
and typical heuristic methods (40% and 14%), enabling us to generate realistic,
high-quality fine-grained data for downstream applications.
