MULTIVARIATE MAPPING IN HIGH STANDARD ATLASES
S. Huber, R. Sieber, M. Ruegsegger, L. Hurni
ETH Zurich, Institute of Cartography, Zurich, Switzerland
Multimedia cartography and especially thematic interactive atlases are evolving into spatial knowledge management systems. Increasing amounts of thematic and topographical data have to be prepared for visualization and interaction. In fact, there are several complementary ways to visualize and interact with the richness of data. On the one hand, different aspects of a thematic issue can be shown on different maps. Usually, these univariate analytic maps are well worked out and allow distinctive interactive manipulations like combination of different map layers or map comparison. On the other hand, there is a strong trend in Multimedia cartography towards complex visualization. Multivariate mapping, i.e. depicting multiple phenomena in a single map, is – besides animation – one of the most promising techniques. As it is often difficult to visually interpret the resulting synthesis map, an interactive and explorative environment efficiently supports the interpretation process.
Thus a new concept of multivariate mapping in high standard atlases will be introduced. As a keystone, this approach supports the consistent handling of different map types and visual variables as developed by Bertin. An atlas specific classification of these variables leads to an optimized visual differentiation of the map elements – important in a highly interactive environment. Each map type has its particular set of visual variables. If a map type has more than one visual variable the symbolization can be considered “multivariate” in principle. A visual variable may be data driven (variable) or constant. To improve readability an attribute can be assigned to more than one visual variable. In general this “multivariate” approach treats each visual variable equally. Consequently, all visual variables work on all measurement scales. For each visual variable there is an extended set of parameters including useful default values. Moreover, adaptive zooming techniques as well as special values like missing data will be supported.
This “multivariate” approach leads to an open and
non-restrictive technical solution with consistent implementation, and
application (parameterisation) and provides a basis for automatically created,
adapted legend and analysis tools. The concept of multivariate mapping will be
illustrated by example of “Atlas of Switzerland