优化地理数据模型的计算效率与可解释性，提升预测速度与空间效应识别能力。
Improving the Computational Efficiency and Explainability of GeoAggregator
- 改进数据加载与前向传播流程，提升计算效率
- 推理速度更快，预测准确率高于原版模型
- 结合几何沙普利法实现空间影响可解释，适合地理分析场景
精准建模与解释地理空间表格数据（GTD）对理解地理现象及其内在过程至关重要。近期提出的基于Transformer的深度学习模型GeoAggregator（GA）已证明其优于传统统计与机器学习方法。本文进一步改进GA：1）设计优化流水线，加速数据加载并简化前向传播，提升计算效率；2）引入模型集成策略与基于GeoShapley框架的后处理解释函数，增强可解释性。通过合成数据集验证，新方法在保持高精度的同时显著提升推理速度，且解释实验表明模型能有效捕捉设计数据中的空间效应。完整代码已开源（https://github.com/ruid7181/GA-sklearn），供社区使用。
原文摘要 · Abstract (English)
Accurate modeling and explaining geospatial tabular data (GTD) are critical for understanding geospatial phenomena and their underlying processes. Recent work has proposed a novel transformer-based deep learning model named GeoAggregator (GA) for this purpose, and has demonstrated that it outperforms other statistical and machine learning approaches. In this short paper, we further improve GA by 1) developing an optimized pipeline that accelerates the dataloading process and streamlines the forward pass of GA to achieve better computational efficiency; and 2) incorporating a model ensembling strategy and a post-hoc model explanation function based on the GeoShapley framework to enhance model explainability. We validate the functionality and efficiency of the proposed strategies by applying the improved GA model to synthetic datasets. Experimental results show that our implementation improves the prediction accuracy and inference speed of GA compared to the original implementation. Moreover, explanation experiments indicate that GA can effectively captures the inherent spatial effects in the designed synthetic dataset. The complete pipeline has been made publicly available for community use (https://github.com/ruid7181/GA-sklearn).
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