Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
A spatio-temporal dataset of weekly chickenpox (childhood disease) cases from Hungary. The dataset consists of a county-level adjacency matrix and time series of the county-level reported cases between 2005 and 2015. There are 2 specific related tasks:
- County level case count prediction.
- National level case count prediction.
- Directed: No.
- Node features: Yes.
- Temporal: Yes.
| Hungarian Counties | |
|---|---|
| Nodes | 20 |
| Edges | 61 |
| Density | 0.3211 |
| Transitvity | 0.4134 |
- Count data regression
@misc{rozemberczki2021chickenpox,
title={{Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks}},
author={Benedek Rozemberczki and Paul Scherer and Oliver Kiss and Rik Sarkar and Tamas Ferenci},
year={2021},
eprint={2102.08100},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
A spatio-temporal dataset of weekly PedalMe bicycle deliveries in London. The dataset consists of a proximity based weighted adjacency matrix and time series of the weekly demands in 2020 and 2021. There are 2 specific related tasks:
- Locality level demand prediction.
- London level demand prediction.
- Directed: No.
- Node features: Yes.
- Temporal: Yes.
| London Regions | |
|---|---|
| Nodes | 15 |
| Edges | 225 |
- Count data regression
@inproceedings{rozemberczki2021pytorch,
author = {Benedek Rozemberczki and Paul Scherer and Yixuan He and George Panagopoulos and Alexander Riedel and Maria Astefanoaei and Oliver Kiss and Ferenc Beres and Guzman Lopez and Nicolas Collignon and Rik Sarkar},
title = {{PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models}},
year = {2021},
booktitle={Proceedings of the 30th ACM International Conference on Information and Knowledge Management},
}