GRU^spa: Gated Recurrent Unit with Spatial Attention for Spatio-Temporal Disaggregation
arxiv(2023)
摘要
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 a model, Gated Recurrent Unit with Spatial Attention
(GRU^spa), where spatial attention layers are integrated into the original
Gated Recurrent Unit (GRU) model. The spatial attention layers capture spatial
interactions among regions, while the gated recurrent module captures the
temporal dependencies. Additionally, we utilize containment relationships
between different geographic levels (e.g., when a given city block is wholly
contained in a given census tract) to constrain the spatial attention layers.
For situations where limited historical training data is available, we study
transfer learning scenarios 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
GRU^spa provides a significant improvement over other neural models as well
as typical heuristic methods, allowing us to synthesize realistic point data
over small regions useful for training downstream models.
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