After more than 15 years of development drawing on research in
cognitive psychology, statistical graphics, computer science, and
cartography, micromap designs are becoming part of mainstream
statistical visualizations. Bringing together the research of two
leaders in this field, Visualizing Data Patterns with Micromaps
presents the many design variations and applications of micromaps,
which link statistical information to an organized set of small
maps. This full-color book helps readers simultaneously explore the
statistical and geographic patterns in their data.
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.
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