TL;DR
This paper explores the integration of explainable AI techniques, especially Shapley values, into spatial analysis to improve understanding of machine learning models applied to spatial data, demonstrated through voting behavior analysis.
Contribution
It introduces the application of Shapley value-based XAI methods in spatial analysis and compares them with traditional spatial models like geographically weighted regression.
Findings
Shapley values effectively explain machine learning model outputs in spatial contexts.
XAI methods enhance transparency and model diagnosis in spatial data science.
Comparison shows strengths and limitations of XAI versus traditional spatial models.
Abstract
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…
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