Creation and Collaboration: Engaging New Audiences for Information Visualization - Information Visualization: Human-Centered Issues and Perspectives - page 93

developments signify an increased desire for collaboration around complex data,
yet, information visualization tools are still primarily designed according to a
single user model. To meet the demands of an increasingly diverse audience, the
design of information visualization technologies will have to incorporate features
for sharing and collaboration.
In this paper, we discuss creation and collaboration tools for interactive visu-
alization. Our goal is to begin to characterize the increasingly diverse audience
for visualization technology and map out the design space for new creative and
collaborative tools to support these users. In section 2 we classify the expand-
ing user base for visualization technologies by looking at their skills, goals and
the data they are trying to analyze. We then take a look at existing informa-
tion visualization tools and classify them along these dimensions. In sections 3
and 4 we examine the new collaborative trends. Section 3 discusses co-located
collaboration, while section 4 explores the area of distributed, asynchronous col-
laboration on the Web. Finally, we conclude by considering the ways the research
community should respond to these developments.
2 End-User Creation of Visualizations
The term “end-user visualization” encompasses a broad range of visualization
users and use-cases. For example, a marketing executive might create an overview
of the sales in different product segments to show to his manager, a scientist may
create a coordinated visualization application to study a biomedical dataset,
or a Facebook user may present her social network in a visualization on the
site. All these use cases involve different types of users employing information
visualization to tackle different types of problems. If we wish to provide end-
users with the ability to construct and deploy custom information visualizations
of their own data, we need an understanding of these users, their goals, and their
data. In the following sections, we will broadly classify each of these dimensions.
Note that we do not intend to construct a formal taxonomy of users. Instead,
our goal is to broaden the discussion on who our users are and how visualization
can help them.
2.1 Data
Scientific, geographic, economic, demographic, and other domains of human
knowledge produce vast amounts of wildly different forms of information, varied
in terms of both individual interest and broad social importance. Visualization
seeks to provide perceptually and cognitively effective tools to display and inter-
act with these different kinds of data. Data is commonly categorized by inherent
complexity (e. g., data homogeneity, number of dimensions) or size. In this sec-
tion, however, we consider data from the perspective of users by categorizing
three different kinds of data in terms of potential audience.