GeoEquity
Spatial Equity Assessment for Machine Learning Models
GeoEquity diagnoses and visualizes spatial performance disparities in geospatial ML models—identifying where models underperform and predicting accuracy across space.
Installation
pip install geoequity
Quick Start
After training your ML model, use GeoEquity to analyze spatial accuracy patterns:
from geoequity import TwoStageModel
# Your validated predictions (see Required Data below)
ts = TwoStageModel()
ts.fit(df_test, model_name='mymodel')
# Predict accuracy at any location
accuracy = ts.predict(longitude=5.0, latitude=50.0, density=0.001)
# Generate diagnostic report
ts.diagnose(save_dir='diagnostics/')
Required Data
Your DataFrame should contain:
| Column | Description |
|---|---|
longitude, latitude |
Coordinates |
observed |
Ground truth values |
predicted_{model_name} |
Your model's predictions |
density |
Data density at each location |
sufficiency |
Training sample size |
Documentation
📖 Full documentation: https://px39n.github.io/geoequity/
Data
Example datasets are available on Zenodo: https://doi.org/10.5281/zenodo.17915696
Direct download: geoequity.zip
Citation
@article{liang2025geoequity,
title={Countering Local Overfitting for Equitable Spatiotemporal Modeling},
author={Liang, Zhehao and Castruccio, Stefano and Crippa, Paola},
journal={...},
year={2025}
}
License
MIT License
1 maintainer
Metadata
1 maintainer
Release files for geoequity 1.0.2
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