GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping

• URL: http://arxiv.org/abs/2412.15353v1
• Date: Thu, 19 Dec 2024 19:39:16 GMT
• Title: GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
• Authors: Bang An, Xun Zhou, Zirui Zhou, Ronilo Ragodos, Zenglin Xu, Jun Luo,
• Abstract summary: We propose GeoPro-Net to bridge the gap between deep learning and multi-sourcetemporal forecasting. GeoPro-Net learns different sets of concepts inherently, and interprets them to real-world cases.
• Score: 36.33309805132091
• License:
• Abstract: The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requirement for spatiotemporal forecasting models to justify the decisions. Interpretation of the spatiotemporal forecasting mechanism is, however, challenging due to the complexity of multi-source spatiotemporal features, the non-intuitive nature of spatiotemporal patterns for non-expert users, and the presence of spatial heterogeneity in the data. Currently, no existing deep learning model intrinsically interprets the complex predictive process learned from multi-source spatiotemporal features. To bridge the gap, we propose GeoPro-Net, an intrinsically interpretable spatiotemporal model for spatiotemporal event forecasting problems. GeoPro-Net introduces a novel Geo-concept convolution operation, which employs statistical tests to extract predictive patterns in the input as Geo-concepts, and condenses the Geo-concept-encoded input through interpretable channel fusion and geographic-based pooling. In addition, GeoPro-Net learns different sets of prototypes of concepts inherently, and projects them to real-world cases for interpretation. Comprehensive experiments and case studies on four real-world datasets demonstrate that GeoPro-Net provides better interpretability while still achieving competitive prediction performance compared with state-of-the-art baselines.

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