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696 27. Visualization
Determining the most appropriate spatial position of the views themselves
with respect to each other can be as significant a problem as determining the
spatial position of marks within a single view. In some systems, the location of the
views is arbitrary and left up to the window system or the user. Aligning the views
allows precise comparison between them, either vertically, horizontally, or with
an array for both directions. Just as items can be sorted within a view, views can
be sorted within a display, typically with respect to a derived variable measuring
some aspect of the entire view as opposed to an individual item within it.
Figure 27.16 shows a visualization of census data that uses many views. In
addition to geographic information, the demographic information for each county
includes population, density, gender, median age, percent change since 1990,
and proportions of major ethnic groups. The visual encodings used include ge-
ographic, scatterplot, parallel coordinate, tabular, and matrix views. The same
color encoding is used across all the views, with a legend in the bottom mid-
dle. The scatterplot matrix shows linked highlighting across all views, where
the blue items are close together in some views and scattered in others. The
map in the upper-left corner is an overview for the large detail map in the cen-
ter. The tabular views allow direct sorting by and selection within a dimension
of interest.
27.7 Data Reduction
The visual encoding techniques that we have discussed so far show all of the items
in a dataset. However, many datasets are so large that showing everything simul-
taneously would result in so much visual clutter that the visual representation
would be difficult or impossible for a viewer to understand. The main strategies
to reduce the amount of data shown are overviews and aggregation, filtering and
navigation, the focus+context techniques, and dimensionality reduction.
27.7.1 Overviews and Aggregation
With tiny datasets, a visual encoding can easily show all data dimensions for all
items. For datasets of medium size, an overview that shows information about
all items can be constructed by showing less detail for each item. Many datasets
have internal or derivable structure at multiple scales. In these cases, a multiscale
visual representation can provide manylevels of overview, rather than just a single
level. Overviews are typically used as a starting point to give users clues about
where to drill down to inspect in more detail.