A composite cluster map displays a fuzzy categorisation of geographic areas. It combines information from several sources to provide a visualisation of the significance of cluster borders. The basic technique renders the chance that two neighbouring locations are members of different clusters as the darkness of the border that is drawn between those two locations. Adding noise to the clustering process is one way to obtain an estimate about how fixed a border is. We verify the reliability of our technique by comparing a composite cluster map with results obtained using multi-dimensional scaling. Projecting Classifications Geographically A large variety of applications (ranging from image segmentation to data mining) have made use of clustering techniques [1]. Clusters may be visualised as an aid in identifying similar attributes, as well as to identify significant classes of individuals, the task we focus on here. Visualisation of geographic information is extensively studied by Be...