This study looked at a specific application of cluster analysis using the recently released
population census 2011 data for the Ekurhuleni Metro in the Gauteng Province of South Africa. The
main focus of the clustering was to distinguish housing and household patterns in order to create
homogenous groups with similar demands for infrastructure, facilities and services. The k-means
algorithm was specifically applied to groups of variables (factors) such as the dwelling types, conditions
and location characteristics, socio-economic profiles, as well as demographic factors. These
groups of clusters were later combined in a sequential manner to obtain a final set of meaningful
clusters that could be used as inputs into an urban growth simulation tool.