Metadata-Version: 2.1
Name: geoxgboost
Version: 1.0.9
Summary: Geographically Weighted XGBoost
Home-page: https://geoxgboost.readthedocs.io/
Author: George Grekousis
Author-email: geograik@gmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: pandas>=2.1.4
Requires-Dist: numpy>=1.26.4
Requires-Dist: scikit-learn>=1.4.2
Requires-Dist: scipy>=1.12.0
Requires-Dist: xgboost>=2.0
Requires-Dist: openpyxl>=3.0.9

# Geographical XGBoost

An implementation of XGBoost designed for geographical analysis.



# Installation

pip install geoxgboost



# Tutorial

A comprehensive tutorial is available on GitHub that demonstrates how to:

1.   Create a project in PyCharm

2.   Install geoxgboost

3.   Run the code using demo data



Here's the link to the tutorial:

https://github.com/geogreko/DemoGXGBoost/tree/main 



# Demo data

Boston housing dataset



The following files are included in the GitHub repository:



1.    Coords.csv: Coordinates of the spatial units.

2.    Data.csv: Dependent and independent variables.

3.    DataDescription.xlsx: Data description.

4.    GXGB_call_demo.py: Python script to analyze the Boston housing dataset.

5.    PredictCoords.csv: Coordinates of the spatial units for prediction.

6.    PredictData.csv: Values of the independent variables for the spatial units where predictions will be made.

7.    Tutorial_geoxgboost.pdf: A guide for using the demo.



# How to cite

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





