Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally important to obtain predictions with uncertainty measures to enhance model credibility and support responsible spatial prediction. Although geostatistic methods like Kriging offer some level of uncertainty assessment, such as Kriging variance, these measurements are not always accurate and lack general applicability to other spatial models. To address this issue, we propose a model-agnostic uncertainty assessment method called GeoConformal Prediction, which incorporates geographical weighting into conformal prediction. We applied it to two classic spatial prediction cases, spatial regression and spatial interpolation, to evaluate its reliability. First, in the spatial regression case, we used XGBoost to predict housing prices, followed by GeoConformal to calculate uncertainty. Our results show that GeoConformal achieved a coverage rate of 93.67%, while Bootstrap methods only reached a maximum coverage of 81.00% after 2000 runs. Next, we applied GeoConformal to spatial interpolation models. We found that the uncertainty obtained from GeoConformal aligned closely with the variance in Kriging. Finally, using GeoConformal, we analyzed the sources of uncertainty in spatial prediction. We found that explicitly including local features in AI models can significantly reduce prediction uncertainty, especially in areas with strong local dependence. Our findings suggest that GeoConformal holds potential not only for geographic knowledge discovery but also for guiding the design of future GeoAI models, paving the way for more reliable and interpretable spatial prediction frameworks.

翻译：空间预测是地理学中的一项基本任务。近年来，随着地理空间人工智能（GeoAI）的发展，已开发出众多模型以提高地理变量预测的准确性。除了追求更高的精度外，获取带有不确定性度量的预测结果对于增强模型可信度和支持负责任的空间预测同样至关重要。尽管克里金法等地统计方法提供了某种程度的不确定性评估（如克里金方差），但这些度量并非总是准确的，并且缺乏对其他空间模型的普适性。为解决这一问题，我们提出了一种模型无关的不确定性评估方法，称为GeoConformal预测，该方法将地理加权融入共形预测框架。我们将其应用于两个经典的空间预测案例——空间回归与空间插值——以评估其可靠性。首先，在空间回归案例中，我们使用XGBoost预测房价，随后通过GeoConformal计算不确定性。结果显示，GeoConformal实现了93.67%的覆盖率，而Bootstrap方法在运行2000次后最高仅达到81.00%的覆盖率。接着，我们将GeoConformal应用于空间插值模型。研究发现，GeoConformal获得的不确定性与克里金方差高度吻合。最后，利用GeoConformal，我们分析了空间预测中不确定性的来源。发现将局部特征显式纳入AI模型可显著降低预测不确定性，尤其是在局部依赖性强的区域。我们的研究表明，GeoConformal不仅在地理知识发现方面具有潜力，还能为未来GeoAI模型的设计提供指导，从而为构建更可靠、可解释的空间预测框架铺平道路。