(left) provides a variety of controls for
filtering visualized data: checkboxes
and radio buttons filter categorical
variables, while range sliders filter numerical values; on the right, Google
Hotel Search provides widgets for geographic, date, and price ranges. Query
controls can be further augmented
with visualizations of their own: Figure 3 includes a range slider for dates
augmented with a histogram of underlying values.
Expert analysts also benefit from
more advanced functionality. For example, a search box might support
sophisticated query mechanisms,
ranging in complexity from simple
keyword search to a full-fledged que-
figure 5. Querying time-series by slope in
timeSearcher. 12
204.2
153
102
51.0
4
7
10
13
cylinders
9
displacement
456 sq in
ry language. Filtering also interacts
with other operations: filtering widgets may operate over data sorted in
a user-specified manner (see the next
section), or users might create derived
values (as we will discuss) and filter
based on the results.
Sort. Ordering (or sorting) is another fundamental operation within
a visualization. A proper ordering can
effectively surface trends and clusters
of values or organize the data according to a familiar unit of analysis (days
of the week, financial quarters, and
so on). The most common method of
ordering is to sort records according
to the value of one or more variables.
Ordering becomes more complicated
in the case of multiple view displays, in
which both entire plots and the values
they contain may be sorted to reveal
patterns or anomalies. Sorting values
consistently across plots (for example,
by their marginal mean or median values) can reveal patterns while facilitating comparison among plots.
Some data types (for example,
multivariate tables, networks) do not
lend themselves to simple sorting by
value. Such data may require more so-
phisticated seriation methods24 that
minimize a distance measure among
items. The goal is to reveal underly-
ing structure within the data. Figure 4
shows a matrix-based visualization of
a social network. On the left, a matrix
plot of a social network conveys little
structure when the rows and columns
(representing people) are sorted al-
phabetically. Interactively reorder-
ing the matrix by node degree reveals
more structure (center). Permuting
the matrix by network connectivity re-
veals underlying clusters of commu-
nities (right).
weight
horsepower
231 hp
acceleration (0-60 mph)
26 sec
mileage
47 mpg
year
83
3
68 sq in
1613 lbs
46 hp
8 sec
70
figure 6. Selection queries in parallel coordinates.
