After over fifteen years of research and trial and error, micromap designs have evolved to the point where they are slowly finding their way into mainstream statistical visualizations. Now seems to be a good time to pull all of the work together into a book in order to introduce micromaps to a wide range of people interested in visualizing their data. Our intent is not only to describe these graphics but also to present the research of others in cognitive psychology, statistical graphics, computer science, and cartography who laid the foundation for our methods. By understanding why we favor particular design elements in micromaps, you should be betterable to tailor your micromap designs to be more effective. If we have missed opportunities or made compromises not to your liking, you can use the guidance here, do it your way, and do better. Thus, we have written this book for anyone who wishes to explore the statistical and geographic patterns in their data simultaneously and for designers of visualization tools that will support visual exploration and communication of patterns in maps.

After illustrating the three main types of micromaps, the authors summarize the research behind the design of visualization tools that support exploration and communication of spatial data patterns. They then explain how these research findings can be applied to micromap designs in general and detail the specifics involved with linked, conditioned, and comparative micromap designs. To compare and contrast their purposes, limitations, and strengths, the final chapter applies all three of these techniques to the same demographic data for Louisiana before and after Hurricanes Katrina and Rita.

Supplementary website

Offering numerous ancillary features, the book’s website at http://mason.gmu.edu/~dcarr/Micromaps/ provides many boundary files and real data sets that address topics, such species biodiversity and alcoholism. One complete folder of data examples presents cancer statistics, risk factors, and demographic data. The site includes CCmaps, the dynamic implementation of conditioned micromaps written in Java, as well as a link to a generalized micromaps program. It also contains R functions and scripts for linked and comparative micromaps, enabling re-creation of all the corresponding examples in the book.