geoxgboost 1.0.9

Geographically Weighted XGBoost

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