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Published August 28, 2020 | Version v1.0
Dataset Open

Sequential Vote Results of Swiss Referenda

Contributors

  • 1. EPFL
  • 2. ETHZ

Description

This repo contains the data introduced in

Immer, A.*, Kristof, V.*, Grossglauser, M., Thiran, P., Sub-Matrix Factorization for Real-Time Vote Prediction, KDD 2020

These data have been collected from OpenData.Swiss every two minutes on two different referendum vote days: May 19, 2019, and February 9, 2020. We use these data to make real-time predictions of the referenda outcome on www.predikon.ch. We publish here the raw data, as retrieved in JSON format from the API. We also provide a python script to help scraping the JSON files.

After unzipping the datasets, you can scrape the data by referendum vote day by doing:

from scraper import scrape_referenda

# Scrape the data from February 2, 2020.
data_dir = 'path/to/2020-02-09'
data = scrape_referenda(data_dir)

The data variable will be a list of datum dictionaries of the following structure:

{
  "vote": 6290,
  "municipality": 1,
  "timestamp": "2020-02-09T15:23:10",
  "num_yes": 222,
  "num_no": 482,
  "num_valid": 704,
  "num_total": 709,
  "num_eligible": 1407,
  "yes_percent": 0.3153409090909091,
  "turnout": 0.503909026297086
}

The datum is as follows:

  • vote: vote ID as defined by OpenData.Swiss
  • municipality: municipality ID as defined by OpenData.Swiss
  • timestamp: date and time at which the JSON files has been published on OpenData.Swiss
  • num_yes: number of "yes" in the municipality
  • num_no: number of "no" in the municipality
  • num_valid: number of valid ballots (the ones counting for the results)
  • numb_total: total number of ballots (including invalid ones)
  • num_eligible: number of registered voters
  • yes_percent: percentage of "yes" (computed as `num_yes / num_valid`)
  • turnout: turnout to the vote (computed as `num_total / num_eligible`)

 

Don't hesitate to reach out to us if you have any questions!

 

To cite this dataset:

@inproceedings{immer2020submatrix,
  author = {Immer, Alexander and Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick},
  title = {Sub-Matrix Factorization for Real-Time Vote Prediction},
  year = {2020},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
}

 

Files

2019-05-19.zip

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Additional details

Related works

Is compiled by
Conference paper: 10.1145/3394486.3403277 (DOI)

References

  • Immer, A.*, Kristof, V.*, Grossglauser, M., Thiran, P., Sub-Matrix Factorization for Real-Time Vote Prediction, KDD 2020