  The advent of low cost, accessible, and high performance augmented reality
(AR) has shed light on a situated form of analytics where in-situ
visualizations embedded in the real world can facilitate sensemaking based on
the user's physical location. In this work, we identify prior literature in
this emerging field with a focus on situated analytics. After collecting 47
relevant situated analytics systems, we classify them using a taxonomy of three
dimensions: situating triggers, view situatedness, and data depiction. We then
identify four archetypical patterns in our classification using an ensemble
cluster analysis. We also assess the level which these systems support the
sensemaking process. Finally, we discuss insights and design guidelines that we
learned from our analysis.
