HEURISTIC EVALUATION: A GUIDE FOR USE IN INFORMATION VISUALIZATION - Information Visualisation: Techniques, Usability and Evaluation - page 189

Based on known theories in perception and cognition from Bertin [33],
Tufte [34], and Ware [35]; Zuk and Carpendale [36] aggregated a set of
heuristics out of a list generated from the three authors and used this new
set in an analysis of a selection of eight representative uncertainty
visualizations. All heuristics were relevant to the visualizations, although
only one was relevant to as many as seven of them. The authors suggested
that the heuristic set should be relevant to other types of visualizations too,
and they used it in another study evaluating a visual decision support
system employed to investigate simulation data [6]. Again, they were
shown to be useful; however there are limitations. Their limitations are
due to the low-level nature of the heuristics. For example, local contrast
affects colour and grey perception ; and pre-attentive benefits increase
with field of view . This affects their applicability for certain evaluations.
Amar and Stasko, on the other hand, presented a set of higher-level
knowledge tasks that should be supported by an information visualization
system and which could be used as heuristics [37]. In their paper, they
discuss:
the notion of analytic gaps, which represent obstacles faced by
visualizations in facilitating higher-level analytic tasks, such as decision-
making and learning. We discuss support for bridging the analytic gap,
propose a framework for design and evaluation of information
visualization systems, and demonstrate its use. [37, p. 143]
The knowledge-tasks presented to be considered for design and
evaluation are: expose uncertainty , concretize relationships , determination
of domain parameters , multivariate explanation , confirm hypothesis and
formulate cause and effect . These heuristics have not been applied and
published in practice, so there is no evidence of their usefulness.
Schneiderman [38] proposed a set of guidelines to aid successful
information seeking, also known as the “Visual Information Seeking
Mantra.” This set consists of seven tasks that should be supported by any
information visualization technique: overview , zoom and filter (enlarge
data and reduce complexity of information by removing unwanted data),
details on demand , view relationships (between the data items, between
linking in multiple views, allowing comparison, etc.), history (of actions to
support undo, redo, save previous states, replays of actions, etc.) and
extract (ability to extract findings for use in other systems). This set
summarizes design guidelines. It has been influential within the
information visualization community, and used as a framework for
information design and evaluation [39].