A novel model has been developed to predict elections on the basis of early results. The electorate is clustered according to their behaviour in previous elections. Early results in the new elections can then be translated into voter behaviour per cluster and extrapolated over the whole electorate. This procedure is of particular value in the South African elections which tend to be highly biased, as early results do not give a proper representation of the overall electorate. In this paper we explain the methodology used to obtain the predictions. In particular, we look at the different clustering techniques that can be used, such as kmeans, fuzzy clustering and k-means in combination with discriminant analysis. We assess the performances of the different approaches by comparing their convergence towards the final results.
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.
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.
Confronted by poverty, income disparities and mounting demands for basic services such as clean water, sanitation and health care, urban planners in developing countries like South Africa, face daunting challenges. This paper explores the role of Integrated land use and transportation modelling in metropolitan planning processes aimed at improving the spatial efficiency of urban form and ensuring that public sector investments in social and economic infrastructure contribute to economic growth and the reduction of persistent poverty and inequality. The value of such models is not in accurately predicting the future but in providing participants in the (often adversarial) planning process with a better understanding of cause and effect between different components of the urban system and in discovering common ground that could lead to compromise. This paper describes how an Urban Simulation Model was developed by adapting one of the leading microsimulation models (UrbanSim) originating from the developed world to South African conditions and how the requirements for microscopic data about the base year of a simulation were satisfied in a sparse data environment by introducing various typologies. A sample of results from three case studies in the cities of Tshwane, Ekurhuleni and Nelson Mandela Bay between 2013 and 2017 are then presented to illustrate how modelling supports the planning process by adding elements of rational analysis and hypothesis testing to the evaluation of proposed policies.
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