The Visual Scalability of Integrated and Multiple Views for High
Resolution Displays
Many real world datasets involve the integration of spatial and
multidimensional data. Users can gain insight into these datasets using
information visualizations. One aspect of the visualization design
space for integrating these data types is a choice of when to use a
single integrated view and when to use multiple views. Because of the
many tradeoffs involved with this design decision it is not always
clear which design to use. Additionally, as the cost of display
technologies continues to decrease high resolution displays are
increasingly being used for visualization. In the past, the scalability
of different visualization designs has been limited by the size and
corresponding resolution of the display technology. As the
technological limitations lessen and more information can be displayed,
consideration of human limitations becomes increasingly critical.
The purpose of this research is to compare the different information
visualization designs for integrating spatial and multidimensional data
in terms of their visual scalability for high resolution displays.
Toward this goal the design space was articulated and, to establish a
baseline, user performance with an integrated view and multiple views
was compared using low resolution displays. The proposed work deals
specifically with visualizing geospatially-referenced multidimensional
time-series data on high resolution displays. We consider (analytically
and empirically) the visual scalability of integrated and multiple
views. We also consider the benefits of combining these designs and
adding a separate view of the multidimensional data on high resolution
displays. The end product of this work will be design guidelines for
visualizing datasets with spatial and multidimensional information on
high resolution displays based on empirical evaluation of user
performance.