Bivariate Statistical Legends: Mapping Examples on Florida Covariance Data using Scatterplots from Open Source Visual Analytic Tools

Georgianna Strode, Benjamin Thornton, Victor Mesev, Nathan Johnson

Abstract


Maps representing two variables sometimes use a single combined, or bivariate legend to improve clarity when comparing relationships, and to avoid less convenient side-by-side legends. However, conventional bivariate legends typically omit the underlying bivariate distributions. Using simple scatterplots statistical indicators of covariance can be plotted as points directly on to the choropleth bivariate legend. This allows map readers to not only compare aggregate magnitudes between the two variables but also visualize disaggregate distributions that may represent statistical normality in the data as well as skewness, and linearity. In addition, the covariance distributions can direct class interval selection, or at least inform the reader of which classes represent data abundance or data sparsity. Bivariate statistical legends are tested using examples drawn from Florida population census data at the fine scale of block groups. Practicality is demonstrated by open source software using Data Driven Documents (D3) visual analytic software (http://d3js.org).


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