Visualizing Geographic Classifications Using Color

This paper presents the cartographic elements of a system for classifying and visualizing highdimensional geographic datasets. The system has been developed as part of the Land Ocean Interactions in the Coastal Zone [LOICZ] project. The goal of the system is to develop regional and global typologies of coastal zones using large multi-variable datasets. Our implementation brings together statistical clustering algorithms with visualization capabilities to allow easy analysis and comprehension of the results. The two main tasks of the visualization are to allow for discrimination of multiple classes and to show relationships between those classes. These are accomplished in two different visual presentations. In both cases, the system selects colors appropriate to the purpose. In the latter case--showing relationships--the system uses a novel iterative refinement algorithm to select the colors. The results show that the system is successful at both generating the classes and visualizing the relationships between them.