.
This chapter introduces the concept ofsituated analyticsthat
employs data representations organized in relation to germane objects,
places, and persons for the purpose of understanding, sensemaking, and
decision-making. The components of situated analytics are characterized
in greater detail, including the users, tasks, data, representations, interac-
tions, and analytical processes involved. Several case studies of projects
and products are presented that exemplify situated analytics in action.
Based on these case studies, a set of derived design considerations for
building situated analytics applications are presented. Finally, there is a
an outline of a research agenda of challenges and research questions to
explore in the future.
Level 1: Include/Exclude
Papers must discuss situated information visualization* (by Willet et al.) in the application domain of CH. *A situated data representation is a data representation whose physical presentation is located close to the data’s physical referent(s). *A situated visualization is a situated data representation for which the presentation is purely visual – and is typically displayed on a screen.
Representation must include abstract data (e.g., metadata).
Papers focused solely on digital reconstruction without information visualization aspects are excluded.
Posters and workshop papers are excluded to focus on mature research contributions.