Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 1
Proc. Int. Cartogr. Assoc., 1, 100, 2018
https://doi.org/10.5194/ica-proc-1-100-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 1, 100, 2018
https://doi.org/10.5194/ica-proc-1-100-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 May 2018

16 May 2018

Could the outcome of the 2016 US elections have been predicted from past voting patterns?

Peter M. U. Schmitz1,2,3, Jennifer P. Holloway1, Nontembeko Dudeni-Tlhone1, Mbulelo B. Ntlangu4, and Renee Koen1 Peter M. U. Schmitz et al.
  • 1CSIR Built Environment, Meiring Naude Rd, Brummeria, Pretoria, South Africa
  • 2Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria, South Africa
  • 3Fakultät für Vermessung, Informatik und Mathematik, Hochschule für Technik, Stuttgart, Schellingstrasse 24, D-70174, Stuttgart, Germany
  • 4CSIR Modelling and Digital Science, Meiring Naude Rd, Brummeria, Pretoria, South Africa

Keywords: Elections, clustering, predictions, counties

Abstract. In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns to predict final election outcomes, using a small number of released vote counts. With the US presidential elections in November 2016 hitting the global media headlines during the time period directly after successful predictions were done for the South African elections, the team decided to investigate adapting their meth-od to forecast the final outcome in the US elections. In particular, it was felt that the time zone differences between states would affect the time at which results are released and thereby provide a window of opportunity for doing election night prediction using only the early results from the eastern side of the US. Testing the method on the US presidential elections would have two advantages: it would determine whether the core methodology could be generalised, and whether it would work to include a stronger spatial element in the modelling, since the early results released would be spatially biased due to time zone differences. This paper presents a high-level view of the overall methodology and how it was adapted to predict the results of the US presidential elections. A discussion on the clustering of spatial units within the US is also provided and the spatial distribution of results together with the Electoral College prediction results from both a ‘test-run’ and the final 2016 presidential elections are given and analysed.

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