Clustering of housing and household patterns using 2011 population census

Abstract

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.