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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19217481 |
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Table of Contents:
- <p># Processed dataset and source code for STGNN‑HTR: Hand trauma risk prediction across 14 cities in Hunan, China (2011–2023)</p> <p>## Overview<br>This repository contains the complete source code, preprocessing scripts, and processed dataset for STGNN‑HTR, a reproducible spatio-temporal graph neural network framework for multi-source health risk forecasting.</p> <p>## Study Area<br>14 prefecture-level administrative divisions in Hunan Province, China: Changsha, Zhuzhou, Xiangtan, Yueyang, Changde, Hengyang, Chenzhou, Yiyang, Loudi, Yongzhou, Shaoyang, Huaihua, Xiangxi, Zhangjiajie</p> <p>## Temporal Coverage<br>2011–2023 (13 years, annual data)</p> <p>## Variables<br>**Target variable:** Hand trauma incidence rate (per 100,000 population)</p> <p>**Feature variables (9 total):** Resident population, population density, road network density, manufacturing/construction employee ratio, secondary industry GDP share, GDP per capita, physician density (per 1,000 population), college education proportion, and hand trauma incidence rate from previous year (autoregressive)</p> <p>## Data Preprocessing<br>- Missing value imputation: Linear interpolation for college education proportion<br>- Normalization: Min-max scaling to [0,1]</p> <p>## Data Split (Chronological)<br>- Training: 2011–2019 (9 years)<br>- Validation: 2020–2021 (2 years)<br>- Testing: 2022–2023 (2 years)</p> <p>## Model Architecture<br>- Hybrid graph construction (geographic adjacency + economic similarity)<br>- 2-layer Graph Convolutional Network (GCN) for spatial dependencies<br>- Gated Recurrent Unit (GRU) for temporal dynamics<br>- Multi-head attention (4 heads) for adaptive feature fusion</p> <p>## Usage<br>Please see the GitHub repository for complete installation and usage instructions: https://github.com/CIAM-Lab/stgnn-hand-injury</p> <p>## Citation<br>If you use this code or dataset, please cite:<br>Chen Y, Qin X, Chen S. (2026). STGNN‑HTR: A reproducible spatio-temporal graph neural network framework for multi-source hand trauma risk prediction. PeerJ Computer Science.</p> <p>## License<br>MIT License</p>