GeoXCP: uncertainty quantification of spatial explanations in explainable AI
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GeoXCP uncertainty quantification of spatial explanations in explainable AI.pdf
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3.36 MB
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Author(s)
Lou, Xiayin
Luo, Peng
Li, Ziqi
Gao, Song
Meng, Liqiu
Date Issued
October 27, 2025
Journal
International Journal of Geographical Information
Publisher
Taylor & Francis
Citation
Lou, X., Luo, P., Li, Z., Gao, S., & Meng, L. (2025). GeoXCP: uncertainty quantification of spatial explanations in explainable AI. International Journal of Geographical Information Science, 1–31.
Version
Final published version
Abstract
Understanding and explaining complex geographic phenomena—ranging from climate change to socioeconomic disparities—is a central focus in both geography and the broader scientific community. Various methods have been developed to elucidate relationships between variables, from coefficient estimates in linear regression models to the increasingly dominant use of feature attribution scores in Explainable AI (XAI) techniques. However, explanations generated by XAI methods often carry uncertainty, stemming from the model itself and the data used to train the model. Despite the critical importance of accounting for such uncertainty, this issue remains largely overlooked in the geospatial domain. In this study, we developed an uncertainty quantification framework for XAI explanations based on conformal prediction, termed Geospatial eXplanation Conformal Prediction (GeoXCP). By incorporating spatial dependence into the modeling process, GeoXCP produced spatially adaptive explanations with calibrated uncertainty estimates. We validated the effectiveness of GeoXCP through extensive simulation experiments and real-world datasets. The results demonstrated that GeoXCP provided reliable explanations while effectively quantifying uncertainty across diverse geospatial scenarios. Our approach represented a significant advancement in explainable geospatial machine learning, enabling decision-makers to better assess the trustworthiness of model-driven insights. The proposed framework was implemented in a python package, named GeoXCP.
MIT Department
Senseable City Laboratory
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/13658816.2025.2574900
