Chapter 5 Statistical Maps

In this Chapter, I continue the exploration of mapping options with a focus on statistical maps, in particular maps that are designed to highlight extreme values or outliers. Some of these classifications were originally introduced in GeoDa and are gradually being adopted by other exploratory software (e.g., the Python PySAL library). Their layout illustrates the emphasis on statistical exploration rather than cartographic design. Specifically, this includes the Percentile Map, Box Map (with two options for the hinge), and the Standard Deviation Map.

Other topics covered in this Chapter include maps for categorical variables in the form of a Unique Values Map. Construction of this type of map does not involve a classification algorithm, since it uses the integer values of a categorical variable itself as the map categories. The Co-location Map is an extension of this principle to multiple categorical variables.

The Chapter closes with a brief discussion of the Cartogram and map animation (Map Movie), which move beyond the traditional choropleth framework to visualize a spatial distribution.

I continue to use the Ceará Zika sample data set to illustrate the various features.