  Point sets in 2D with multiple classes are a common type of data. A canonical
visualization design for them are scatterplots, which do not scale to large
collections of points. For these larger data sets, binned aggregation (or
binning) is often used to summarize the data, with many possible design
alternatives for creating effective visual representations of these summaries.
There are a wide range of designs to show summaries of 2D multi-class point
data, each capable of supporting different analysis tasks. In this paper, we
explore the space of visual designs for such data, and provide design
guidelines for different analysis scenarios. To support these guidelines, we
compile a set of abstract tasks and ground them in concrete examples using
multiple sample datasets. We then assess designs, and survey a range of design
decisions, considering their appropriateness to the tasks. In addition, we
provide a web-based implementation to experiment with design choices,
supporting the validation of designs based on task needs.
