Ranked data is commonly used in research across many fields of study
including medicine, biology, psychology, and economics. One common statistic
used for analyzing ranked data is Kendall's {\tau} coefficient, a
non-parametric measure of rank correlation which describes the strength of the
association between two monotonic continuous or ordinal variables. While the
mathematics involved in calculating Kendall's {\tau} is well-established, there
are relatively few graphing methods available to visualize the results. Here,
we describe a visualization method for Kendall's {\tau} which uses a series of
rigid Euclidean transformations along a Cartesian plane to map rank pairs into
discrete quadrants. The resulting graph provides a visualization of rank
correlation which helps display the proportion of concordant and discordant
pairs. Moreover, this method highlights other key features of the data which
are not represented by Kendall's {\tau} alone but may nevertheless be
meaningful, such as the relationship between discrete pairs of observations. We
demonstrate the effectiveness of our approach through several examples and
compare our results to other visualization methods