Explainable artificial intelligence in the spatial domain (X-GeoAI)

Recent advances in artificial intelligence (AI) research, the significant increase in computational power, and the large-scale availability of data have ushered in a new era of data-intensive science. In the context of GIScience, GeoAI aims to employ AI methods to analyze complex geographic phenomena. The majority of GeoAI applications rely on machine learning (ML) algorithms to extract generalizable predictive patterns in the form of mathematical models that provide useful insights about the phenomenon in question. ML excels in efficiency, scalability, and accuracy; however, this comes at the cost of reduced explainability. A clear reasoning path from data to conclusions is not always evident, but is readily available in traditional analysis of geographic phenomena using a combination of conceptual and statistical models. In addition, the integration of ML techniques in the geographic context is not always straightforward.