Geoxgboost library demo to accompany paper: Grekousis G. (2025). Geographical-XGBoost: A new ensemble model for spatially local regression based on gradient-boosted trees. Journal of Geographical Systems, https://doi.org/10.1007/s10109-025-00465-4
Year: 2025
Type: article
Abstract: 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. Journal of Geographical Systems, https://doi.org/10.1007/s10109-025-00465-4The following files are needed to run the algorithm on the Demo Dataset: 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) Start with Tutorial geoxgboost.pdf 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
Source: Figshare
Author: Grekousis, George
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6902164664
FWCI:
Cites:
Cited by:
Related to: