标题： 空间分析中的可解释人工智能 显示英文标题

标题： Explainable AI in Spatial Analysis

摘要： 本章讨论了可解释人工智能（XAI）在空间分析领域的机遇。空间分析的一个关键目标是建模空间关系并推断空间过程，从而从空间数据中生成知识，这主要基于空间统计方法。最近，机器学习提供了可扩展且灵活的方法，这些方法补充了传统方法，并越来越多地应用于空间数据科学。尽管有这些优势，但机器学习经常因其作为黑箱模型而受到批评，这限制了我们对模型行为和输出的理解。认识到这一局限性，XAI已成为AI领域中的一个关键领域，它提供了解释机器学习模型输出的方法，以增强透明度和理解。这些方法对于模型诊断、偏差检测以及确保从机器学习模型中获得的结果的可靠性至关重要。本章重点介绍了XAI中的关键概念和方法，特别是基于Shapley值的方法，这可以说是目前最流行的XAI方法，并探讨了它们与空间分析的结合。通过展示2020年总统选举县级投票行为的实证例子，来演示使用Shapley值和空间分析与多尺度地理加权回归的比较。本章最后讨论了当前XAI技术的挑战和局限性，并提出了新的发展方向。 显示英文摘要

摘要： This chapter discusses the opportunities of eXplainable Artificial Intelligence (XAI) within the realm of spatial analysis. A key objective in spatial analysis is to model spatial relationships and infer spatial processes to generate knowledge from spatial data, which has been largely based on spatial statistical methods. More recently, machine learning offers scalable and flexible approaches that complement traditional methods and has been increasingly applied in spatial data science. Despite its advantages, machine learning is often criticized for being a black box, which limits our understanding of model behavior and output. Recognizing this limitation, XAI has emerged as a pivotal field in AI that provides methods to explain the output of machine learning models to enhance transparency and understanding. These methods are crucial for model diagnosis, bias detection, and ensuring the reliability of results obtained from machine learning models. This chapter introduces key concepts and methods in XAI with a focus on Shapley value-based approaches, which is arguably the most popular XAI method, and their integration with spatial analysis. An empirical example of county-level voting behaviors in the 2020 Presidential election is presented to demonstrate the use of Shapley values and spatial analysis with a comparison to multi-scale geographically weighted regression. The chapter concludes with a discussion on the challenges and limitations of current XAI techniques and proposes new directions.