A model for election night forecasting applied to the 2004 South African elections

Greben, Jan M.; Elphinstone, Chris; Holloway, Jenny

doi:10.5784/22-1-35

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…As indicated in the previous section, this model was developed for the South African elections and has been applied during the 1999, 2004, 2009, 2014 general elections and the 2000, 2006, 2011, 2016 municipal elections. The mathematical details of this forecasting model can be obtained in (Greben et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

“…This team represents a successful collaboration of computer scientists, GIS practitioners and statisticians who have developed a model to produce real-time updates to forecasts as the "live" information on vote counts is released. The method involves using clusters based on past voting patterns to predict final election outcomes during election nights, once a sample of between 7 to 10 percent of voting districts have been declared, and has been used successfully in a number of South African elections as illustrated in Greben et al (2005) and Greben et al (2006). After the success achieved in the 2016 South African elections, in which the forecasts correctly predicted final outcomes which were not generally expected in the run-up to the elections, the question arose as to whether the method could be generally applicable to elections carried out in other countries.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Schmitz

Holloway

Dudeni-Tlhone

et al. 2018

Proc. Int. Cartogr. Assoc.

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Schmitz

Holloway

Dudeni-Tlhone

et al. 2018

Proc. Int. Cartogr. Assoc.

View full text Add to dashboard Cite

show abstract

“…The foundation of election night forecasting lies in ecological regression models [2,5,6,7,4]. The performance of a party at a current election is considered to be a linear combination of the performances of all parties at a past election.…”

Section: Introductionmentioning

confidence: 99%

Evolved election forecasts

Hochreiter

Waldhauser

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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In this paper, we apply a genetic algorithm to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be taken in seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provide an argument to tighten the intersection between computer science and social science, especially political science, further. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.

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“…The aim of this paper is not to describe the model since a detailed outline of this model is given by Greben et al [12] as well as by Greben et al [13]. Further, Ittmann [22] gives some personal perspectives and experiences of interacting with the media in sharing the forecasts produced by this model.…”

Section: Model and Assumptionsmentioning

confidence: 99%

“…With appropriate samples sizes a forecast of the outcome is presented almost as soon as the election booths close. In South Africa a forecasting model was developed which endeavours to forecast the final outcome based on the first results as they become available [12,13]. The model is thus used after the event to forecast the final outcome.…”

Section: Literature Reviewmentioning

confidence: 99%

2014 Election forecast - a post-election analysis

Ittmann

Holloway²,

Dudeni-Tlhone³

2017

ORiON

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General elections are held every five years in South Africa. During the 12 to 24 hour period after the close of the voting booths, the expected final results are of huge interest to the electorate and politicians. In the past, the Council for Scientific and Industrial Research (CSIR) has developed an election forecasting model in order to provide the media and political analysts with forecasts of the final results during this period of peak interest. In formulating this model, which forecasts the election results as the results from voting districts (VDs) become available, some assumptions had to be made. In particular, assumptions were made about the clustering of previous voting patterns as well as the order in which VD results are released. This election forecasting model had been used successfully for a number of elections in the past and in these previous elections, with around 5%-10% of the results available, the predictions produced by the model were very close to the final outcome, particularly for the ANC, being the largest party. For the 2014 national election, however, the predictions, with close to 50% of the voting district results known (equivalent to an estimated 40% of the total votes), were still not accurate and varied by more than 1% for both the ANC and the EFF. This paper outlines a post-election analysis to determine the reasons for these discrepancies and how they relate directly to the model assumptions. The aim is to highlight how practical realities can affect the assumptions and consequently their impact on the forecasted results. Reference is made to previous election forecasts and the 2014 post-election analysis is presented.

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A model for election night forecasting applied to the 2004 South African elections

Cited by 6 publications

References 11 publications

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

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

Evolved election forecasts

2014 Election forecast - a post-election analysis

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