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About this paper
Zhe Jiang
This survey by Zhe Jiang provides a systematic review of spatial and spatiotemporal prediction methods, proposing a challenge-driven taxonomy that organizes diverse techniques across disciplines. It elucidates the underlying assumptions, advantages, and disadvantages of various approaches, and identifies critical future research opportunities.
Key insights
- Spatial data uniquely presents challenges like autocorrelation, heterogeneity, limited ground truth, and multiple scales, which require specialized prediction models beyond traditional approaches.
- Existing prediction methods can be effectively categorized by how they address these specific spatial challenges, providing a structured framework for understanding and comparison across disciplines.
- The field offers significant future research opportunities in areas such as anisotropic spatial dependency, model transfer across heterogeneous regions, multi-scale data fusion, deep learning for spatial prediction, and scalable spatial big data prediction.
Summary by alphaXiv.
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