Creation and Collaboration: Engaging New Audiences for Information Visualization - Information Visualization: Human-Centered Issues and Perspectives - page 95

The recent trend toward visual analytics [91] is driven by the increasing need
to support open-ended management and exploration of large, loosely-connected,
and often unstructured information sources as well as the smaller, isolated, struc-
tured data sets typical of information visualization applications. Information col-
lection often involves assembling “shoeboxes” of loosely related nuggets and data
sets [107]. Visual analysis of information occurs by following chains of evidence,
evaluating formal hypotheses [27], testing competing explanations [86], or telling
stories [37] using visual metaphors to convey relationships and dynamics. These
activities are particularly challenging in intelligence analysis, emergency man-
agement, epidemiology, and other critical areas that involve high-dimensional
abstract information [83] and large geospatial datastores [36]. However, the het-
erogeneous and idiosyncratic nature of the data sets and analysis activities in
these endeavors are similar to those in everyday domains, making it likely that
the outcomes of visual analytics research will translate readily into visualization
approaches that will help to engage broad audiences.
2.2 Skills
Novice Users: By novice users we mean users who have experience operating
a computer, but no experience with programming in general, let alone program-
ming visualization techniques. The vast majority of novice visualization users
act as consumers: they will interact with the visualization within the possibil-
ities offered but will rarely extend existing functionality to suit their analysis
needs. If we want these users to be able to produce visualizations, we have to
take care to make this process as easy as possible. Some points of consideration
when designing visualizations for novice users are:
Data Input: We cannot expect a novice user to write their own data parser,
write database queries that export data to a particular format or understand
the file formats for more complex data types. Most novice users seem to take to
using spreadsheet programs such as Microsoft Excel to store and analyze their
data. One useful input format then, is a simple tab delimited input file, as this
format is both human readable and can be directly copied from the spreadsheet
editor.
Automatic Selection of Visualization Type: Novice users have no experience
designing visual mappings and may even choose mappings that produce non-
sensical visualizations. Recurring examples include the use of line charts over
categorical data dimensions, for which a bar chart would be a better choice, and
using a pie chart for data that do not form part of a whole. For this reason, visu-
alization techniques geared towards novice users should at least partly automate
the selection of visual metaphors. This may involve analyzing the data dimen-
sions to see if there are any ordinal attributes, check for aggregated variables
and totals, and examine values in dimensions for possible hierarchical structure
[59,60].