A bivariate map displays two variables on a single map by combining two different sets of graphic symbols or colors. Bivariate mapping is an important technique in cartography. It is a variation of simple choropleth map that portrays two separate phenomena simultaneously. The main objective is to accurately and graphically illustrate the relationship between two spatially distributed variables. It has potential to reveal relationships between variables more effectively than a side-by-side comparison of the corresponding univariate maps.
Bivariate mapping is a comparatively recent graphical method. A bivariate choropleth map uses color to solve a problem of representation in four dimensions; two spatial dimensions — longitude and latitude — and two statistical variables. Take the example of mapping population density and average daily maximum temperature simultaneously. Population could be given a colour scale of black to green, and temperature from blue to red. Then an area with low population and low temperature would be dark blue, high population and low temperature would be cyan, high population and high temperature would be yellow, while low population and high temperature would be dark red. The eye can quickly see potential relationships between these variables.
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.
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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