Toshiyuki Asahi *, David Turo and Ben Shneiderman
Human-Computer Interaction Laboratory
Dept. of Computer Science &
Institute for Systems Research
University of Maryland, College Park, MD 20742 USA
* Current address: Kansai C&C; Research Lab., NEC Corporation, 4-24, Shiromi 1-Chome, Chuo-Ku, Osaka 540, Japan, Tel: 81-6-945-3214, email: asahi@cobp.cl.nec.co.jp
AHP was developed to promote improved decision-making for a specific class of problems that involve
prioritization of
potential alternate solutions through evaluation of a set of criteria elements. These elements may be divided
into sub-elements
and so on, thus forming a hierarchical decision tree. Once the hierarchical problem definition has been
established, these
criteria are weighted individually at every level relative to each other; prioritization of the alternate
solutions can then be
obtained via evaluation of these weights.
The treemap can represent both hierarchical structure and each elements' quantitative information
simultaneously in a two-
dimensional rectangular space; 100% of the designated screen area is utilized. Application arenas for
treemaps have included
computer directory browsing, stock market portfolio visualizations, an NBA player statistical browser, and
a US budget viewer.
Treemaps are generated using a straightforward algorithm known as "slice-and-dice." The root node of a
hierarchy is
represented by the entire screen area. For the root node's children, the screen area is sliced (either
horizontally or vertically) to
create smaller rectangles with area dependent upon the value of a particular weighting attribute.
Each node is
then processed recursively, with the direction of the slicing switched by 90 degrees for each level.
Since the decision-making processes are represented by hierarchical trees in AHP, these trees translate
directly to the treemap
visualization method. Figure 1 is an example of a treemap generated with our prototype AHP application.
A base rectangle
representing the goal of decision-making is divided into small rectangular areas proportional to their
relative importances.
Users can identify any criterion by labels displayed in the offset areas (offset areas are also helpful for users
to recognize the
hierarchical structure). The hook and pump tools (upper right in FIGURE 1) enable users to adjust the size
of areas by pulling
on a boundary or by pumping up an area. Since areas represent preferences among the alternatives, the
users can quickly grasp
the relative impact of each component and understand which components most influence the outcome. On
the bottom of the
display, a horizontal histogram shows the aggregate result, and as users hook or pump areas the histogram
changes within a
few hundred milliseconds. This dynamic approach enables users to explore many alternatives in seconds as
opposed the many
minutes required to input a fresh set of preferences using the current keyboard entry approach. The
treemap, which till now
has been used as a way of displaying large amounts of data, now becomes a powerful input strategy.
Figure 1: Screen design for treemap representation of
Analytic Hierarchy
Process with user interface tools for adjusting the treemap.
FIGURE 2: A 15 second extract of the video.
A usability test was conducted with six business or management majors who were already familiar with the
AHP. They
performed five tasks and then rated the interface highly on all 12 criteria. Improvements were suggested,
but the basic concept
was strongly supported [6].
Abstract
The Analytic Hierarchy Process (AHP), a decision-making method based upon division of problem spaces
into hierarchies, is
visualized through the use of treemaps, which pack large amounts of hierarchical information into small
screen spaces. Two
direct manipulation tools, presented metaphorically as a "pump" and a "hook," were developed and applied
to the treemap to
support AHP sensitivity analysis. The problem of construction site selection is considered in this video.
Apart from its
traditional use for problem/ information space visualization, the treemap also serves as a potent visual tool
for "what if" type
analysis.
Keywords
Visualization, treemap, analytic hierarchy process, AHP, decision support
Introduction
Treemaps graphically represent hierarchical information via a two-dimensional rectangular map, providing
compact visual
representations of complex data spaces through both area and color [2-5]. Their efficiency for particular
data searching tasks
has been tested through controlled studies [4,5] with primary benefits seen for two types of tasks: location
of outliers in mass
hierarchies and identification of cause-effect relationships within hierarchies. By extending the treemap
into a "read/write"
graphic through direct manipulation tools, the user is given the capability to massage the data and perform
the outlier and
cause-effect tasks much more effectively. Analytic Hierarchy Process (AHP) [1], given its decision tree
hierarchy and inherent
need for large-scale data visualization and user manipulation, is an appropriate choice for treemap
visualization.