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Data classification and graphic representation of the classified data are two important processes involved in constructing a bivariate map. The number of classes should be possible to deal with by the reader. A rectangular legend box is divided into smaller boxes where each box represents a unique relationship of the variables.
[[File:Dot map black hispanic.png|thumb|left|A bivariate dot density map showing the distribution of the African American and Latino populations in the contiguous United States in 2010.]]
In general, bivariate maps are one of the alternatives to the simple univariate choropleth maps, although they are sometimes extremely difficult to understand the distribution of a single variable. Because conventional bivariate maps use two arbitrarily assigned color schemes and generate random color combinations for overlapping sections and users have to refer to the arbitrary legend all the time. Therefore, a very prominent and clear legend is needed so that both the distribution of single variable and the relationship between the two variables could be shown on the bivariate map.
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