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https://github.com/benedekrozemberczki/spatiotemporal_datasets
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
https://github.com/benedekrozemberczki/spatiotemporal_datasets
analytics benchmark data-science dataset deep-learning deepwalk epidemiology gcn gnn machine-learning node2vec pytorch pytorch-geometric spatial-analysis spatial-data spatial-data-analysis time-series time-series-analysis vector-autoregression
Last synced: 3 months ago
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Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
- Host: GitHub
- URL: https://github.com/benedekrozemberczki/spatiotemporal_datasets
- Owner: benedekrozemberczki
- License: apache-2.0
- Created: 2021-02-03T20:17:27.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-16T21:15:10.000Z (11 months ago)
- Last Synced: 2024-11-12T12:47:31.090Z (3 months ago)
- Topics: analytics, benchmark, data-science, dataset, deep-learning, deepwalk, epidemiology, gcn, gnn, machine-learning, node2vec, pytorch, pytorch-geometric, spatial-analysis, spatial-data, spatial-data-analysis, time-series, time-series-analysis, vector-autoregression
- Homepage:
- Size: 72.3 KB
- Stars: 55
- Watchers: 4
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Spatiotemporal Datasets  [](https://github.com/benedekrozemberczki/spatiotemporal_datasets/archive/master.zip)
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
-------------------------
##### Contents
1. [Chickenpox Cases in Hungary](#chickenpox-cases-in-hungary)
2. [PedalMe London Bicycle Deliveries](#pedalme-london-bicycle-deliveries)
## Chickenpox Cases in Hungary
### Description
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.
### Links
- [Chickenpox Cases in Hungary Edges](https://graphai-datasets.web.app/hungary_county_edges.csv)
- [Chickenpox Cases in Hungary Time Series](https://graphai-datasets.web.app/hungary_chickenpox.csv)
### Properties
- **Directed:** No.
- **Node features:** Yes.
- **Temporal:** Yes.
| | **Hungarian Counties** |
|---|---|
| **Nodes** |20 |
| **Edges** |61 |
| **Density** | 0.3211 |
| **Transitvity** | 0.4134|
### Possible tasks
- **Count data regression**
### Citing
```bibtex
@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}
}
```
## PedalMe London Bicycle Deliveries
### Description
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.
### Links
- [PedalMe London Bicycle Deliveries Edges](https://graphai-datasets.web.app/pedalme_edges.csv)
- [PedalMe London Bicycle Deliveries Time Series](https://graphai-datasets.web.app/pedalme_features.csv)
### Properties
- **Directed:** No.
- **Node features:** Yes.
- **Temporal:** Yes.
| | **London Regions** |
|---|---|
| **Nodes** |15 |
| **Edges** |225 |
### Possible tasks
- **Count data regression**
### Citing
```bibtex
@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},
}
```
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