December 2017
Beginner to intermediate
168 pages
3h 53m
English
Content preview from Making Data Visual
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Chapter 6. Multiple and Coordinated Views
The previous chapter provided a gallery of single-chart visualizations. This chapter brings those views together into interactive, connected visuals called multiple linked views (MLVs). An MLV leverages multiple visualizations by linking the information shown in each view to the others through user interactions.
MLVs are vital to understanding large and complex data. They allow many different attributes to be viewed at once by splitting them up across a set of views and partitioning the data items to find interesting trends. They can be designed to help guide a user toward the most interesting data items, to show multiple perspectives on data, or to allow the user to dive more deeply into a dataset. In an MLV system, a dataset is shown in multiple simple visualizations, with the data items shown in the different charts corresponding to each other. The charts in each visualization can be used to highlight, control, or filter the data items shown in the others.
There are a number of well-defined MLV design patterns, each of which supports a different set of analysis tasks. This chapter covers five of the best-known patterns: small multiples, scatterplot matrices (SPLOMs), overview+detail, multiform views and dashboards, and overlays. Small multiples and SPLOMs are series of small visualizations that use the same view but show different parts of the data. Overview+detail pairs two views, one as an overview of the complete dataset and the ...
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