MULTIVARIATE MAPPING IN HIGH STANDARD ATLASES
S. Huber, R. Sieber, M. Ruegsegger, L. Hurni
ETH Zurich, Institute of Cartography, Zurich, Switzerland
huber@karto.baug.ethz.ch
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