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SECT: a spatiotemporal explicit causal transformer for path-faithful spatiotemporal attribution
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DOI：10.1080/13658816.2026.2656266.png)
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Abstract
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Spatiotemporal prediction remains a central challenge in geographical information science, particularly when both accurate forecasts and causally grounded explanations are required. Existing sequence and graph models capture dependencies across time and space but typically aggregate spatial context into latent representations and provide coarse or non-conserved attributions, limiting traceability to specific neighbors, variables, and lags. This paper introduces the Spatiotemporal Explicit Causal Transformer (SECT), a dual-stream architecture that encodes self and neighbor sequences with causal convolutions and temporal transformers, integrates spatial self-information (SSI) as a localized anomaly metric, and fuses the streams with attention-based pooling. Interpretability is achieved through layer-wise relevance propagation with conservation checks, ensuring attribution is preserved and comparable at the neighbor–variable–lag (NVL) level. In a winter storm outage case study, SECT achieves competitive predictive performance relative to strong temporal and spatial baselines while preserving explicit neighbor identity and lag structure. Multi-seed ablation, knockout, and falsification experiments demonstrate that the model’s performance degrades systematically when temporal order, neighbor alignment, or anomaly structure are perturbed, supporting the structural validity of its learned pathways. These results position SECT as a framework that integrates predictive modeling with experimentally testable, pathway-level spatiotemporal attribution.
Keywords:
Spatiotemporal forecasting
geospatial artificial intelligence
explainable artificial intelligence
spatial dependence
attribution modeling
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