minasora/STGCN-Terrorism-Risk-Prediction

Torch implementation for Spatial-Temporal Multi-Graph Convolutional Network-based Provincial-Day level Terrorism Risk Prediction.

STGCN for Terrorism Risk Prediction

Torch implementation for Spatial-Temporal Multi-Graph Convolutional Network-based Provincial-Day level Terrorism Risk Prediction.

The entire project is divided into two folds: "Preprocessing_part" and "Prediction_part."

Prequirements

pip install -r requirements.txt

Preprocessing_part: Data obtaining and multi-graph structures generation

Due to GTD restrictions (see https://www.start.umd.edu/gtd/terms-of-use/), the authors cannot open source the terrorist attack data here.

If anyone wants to reproduce the dataset, please download the latest GTD "globalterrorismdb_0522dist.xlsx" (see https://www.start.umd.edu/gtd/contact/), and put it in the "Preprocessing_part/AFG_GTD_data/" fold.

Prediction_part: model training and prediction

Usage: run "main.py"(deep learning models) and "mainML.py" (classical machine learning models) You can find pt file in save filefolder