Guidelines For Pursuing and Revealing Data Abstractions

Alex Bigelow, Katy Williams, Katherine Isaacs

View presentation: 2020-10-29T14:15:00Z GMT-0600 Change your timezone on the schedule page
2020-10-29T14:15:00Z
Exemplar figure
A summary of study events over time, their relationship with memos, memo relationships with codes, and code relationships with themes. The timeline at the top shows the timing of events, with curved lines indicating when individual memos were created. The four rows below the timeline indicate the nature of the context in which memos were written, including Meetups, when data workers discussed their applied datasets, when the authors engaged in theoretical discussions, and when the authors engaged in open coding. Rows C1-C24 show which memos directly informed the development of codes. Columns T1–T4 show which codes directly inform which themes.
Keywords

Data abstraction, Grounded theory, Survey design, Data wrangling

Abstract

Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.