geoequity 1.0.2

Spatial Equity Assessment for Machine Learning Models

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/

• Quick Start Guide
• Two-Stage Model
• Example Notebooks

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