Code and Data for "Quantifying Uncertainty in Geospatial Explanations from Explainable AI"

Code and Data for "Quantifying Uncertainty in Geospatial Explanations from Explainable AI"This repository contains the code and data used in our submitted work “Quantifying Uncertainty in Geospatial Explanations from Explainable AI.”In this study, we propose GeoXCP, a model-agnostic framework for quantifying uncertainty in geospatial explanations derived from explainable AI methods.We validate the effectiveness of GeoXCP through a case study on housing price prediction. The provided files are organized as follows:📁 1-modelContains the core implementation of GeoXCP, including the following modules:GeoConformalGeoConformalizedExplainer📁 2-data_Housing_priceIncludes the housing price dataset for Seattle:seattle_sample_3k.csv📁 3-codes_Housing_priceContains code used for applying GeoXCP to the housing price prediction task:SeattleHomeSaleUncertaintyGeoX.ipynb: End-to-end implementation for uncertainty quantification using GeoXCP. All results from the main experiment can be reproduced here.seattle_house_price_coverage_bootstrap.ipynb: Demonstrates how GeoXCP achieves reliable coverage through bootstrap-based validation.