Modelling Voting Patterns at the Ward and Municipal Levels in South Africa Using Remote Sensing and Machine Learning

Abstract

This study investigates whether voting patterns in South Africa can be modelled using open-source data, satellite imagery, and Google Street View images. It responds to a context of declining voter turnout, growing political disillusionment, and a research gap in understanding how physical living conditions influence electoral behaviour. Existing studies largely rely on aggregated census or survey data and overlook the spatial and infrastructural environments that shape daily life and political decision-making. To address this gap, the study collected over 157 000 Google Street View images and satellite-derived land use data at both ward and municipal levels. Using computer vision, it extracted features such as potholes, illegal dumping, people, and vehicles. These were supplemented with open-source crime and financial data, and modelled using machine learning and statistical regression techniques. The models performed strongly, with classification F1 scores reaching 0.93 and regression R-2 values up to 0.86, demonstrating a measurable relationship between living conditions and voting outcomes. The findings reveal that no single voting theory can fully explain voter behaviour in South Africa. While rational choice theory accounts for non-voting in areas of poor service delivery, social structure and psychological theories better explain support for parties like uMkhonto we Sizwe, where regional identity and historical loyalty dominate. Ultimately, the study shows that voting behaviour emerges from a complex mix of infrastructure, identity, and lived experience. These insights have implications for political modelling, service delivery accountability, and understanding democratic participation in contexts marked by inequality and transition.