PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters

TL;DR

This paper introduces PyGRF, an improved Python implementation of the geographical random forest model, enhancing hyperparameter tuning, local training, and prediction accuracy, demonstrated through case studies in public health and natural disasters.

Contribution

The work presents a new Python package for GRF with theory-informed hyperparameters, local training expansion, and spatially-weighted predictions, addressing previous limitations.

Findings

PyGRF improves prediction accuracy over existing models.

The package facilitates wider adoption of GRF in Python.

Case studies demonstrate practical applications in health and disaster management.

Abstract

Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory-informed hyperparameter determination, local training sample expansion, and spatially-weighted local prediction. We also develop a Python-based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.