  Binning is applied to categorize data values or to see distributions of data.
Existing binning algorithms often rely on statistical properties of data.
However, there are semantic considerations for selecting appropriate binning
schemes. Surveys, for instance, gather respondent data for demographic-related
questions such as age, salary, number of employees, etc., that are bucketed
into defined semantic categories. In this paper, we leverage common semantic
categories from survey data and Tableau Public visualizations to identify a set
of semantic binning categories. We employ these semantic binning categories in
OSCAR: a method for automatically selecting bins based on the inferred semantic
type of the field. We conducted a crowdsourced study with 120 participants to
better understand user preferences for bins generated by OSCAR vs. binning
provided in Tableau. We find that maps and histograms using binned values
generated by OSCAR are preferred by users as compared to binning schemes based
purely on the statistical properties of the data.
