Abstract: The purpose of the method presented in this interactive instructional workshop is to facilitate the early design stages of information visualizations by gaining a thorough understanding of the variables and data to be visualized. Data attributes can limit the types of graphical formats appropriate for visualization but can also suggest exciting design opportunities. Optimal visualization design may also be influenced by practical considerations related to visualization automation and deployment. Therefore, it is worthwhile to invest in a thorough exploration of data attributes in order to streamline the design process. In this workshop, we will present a simple, systematic method for discovering the data attributes that influence visualization design. Through a series of interactive exercises, participants will work through the method from start to finish, culminating in preliminary design sketches.
The method for identifying data attributes consists of answering a series of questions related to the precise meaning of a variable, the values that are possible and likely for the variable, and the interpretation of those values. Special attention is given to the theoretical underpinnings of latent variables, possible/desirable data transformations, treatment of extreme and non-missing zero values, and the value judgments, cutpoints, and normed scores that are associated with some variables. The presentation is illustrated throughout with case studies drawn from our visualization work, and informed by key lessons learned. Participants will get hands-on, small group practice for each step of the method and will then have the opportunity to share their findings with the full group. Participants also will be provided with a list of practical information visualization resources.
Learning Objective 1: By the end of this workshop, participants will be able to explain the rationale for using a systematic method for identifying data attributes prior to visualization design.
Learning Objective 2: By the end of this workshop, participants will be able to extract data attributes from variable documentation.
Learning Objective 3: By the end of this workshop, participants will be able to select an appropriate graphical format based on summary data attributes.
Learning Objective 4: By the end of this workshop, participants will be able to sketch information visualization designs using simulated data.
Adriana Arcia (Presenter)
Samantha Stonbraker, Columbia University