<b>Geoxgboost library demo </b> to accompany paper: Grekousis G. (2025). Geographical-XGBoost: A new ensemble model for spatially local regression based on gradient-boosted trees. <i>Journal of Geographical Systems, </i>https://doi.org/10.1007/s10109

Geoxgboost is a Python library that implements the Geographical-XGBoost (G-XGBoost) algorithm for spatially local regression as published in Grekousis G. (2025). Geographical-XGBoost: A new ensemble model for spatially local regression based on gradient-boosted trees. <i>Journal of Geographical Systems,</i> https://doi.org/10.1007/s10109-025-00465-4The following files are needed to run the algorithm on the Demo Dataset:<br>1. Coords.csv: includes the coordinates of the spatial units2. Data.csv: dependent and independent variables3. DataDescription.xlsx: data description4. GXGB_call_demo.py: code to run demo for analyzing the Boston housing dataset5. PredictCoords.csv: contains the coordinates of the spatial units where the prediction will occur6. PredictData.csv: contains the values of the independent variables referring to the spatial units in which the prediction will occur7. Tutorial geoxgboost.pdf: guidelines for the demo (an updated version may exist in github, see link below)<br><b>Start with Tutorial geoxgboost.pdf</b><br>For updated versions of the geoxgboost library and related files visit:https://pypi.org/project/geoxgboost/https://geoxgboost.readthedocs.io/en/latest/https://github.com/geogreko/DemoGXGBoost<br>