Visualizing Data Patterns with Micromaps
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...
Search in google:
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 websiteOffering 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.
An Introduction to MicromapsIntroduction Row-labeled plots Linked micromaps Conditioned micromaps Comparative micromaps Summary and preview of book chaptersResearch Influencing Micromap DesignInfluence of statistical graphics research on micromap designs Contributions from other research areas Human perceptual and cognitive strengths and limitations impacting data visualization SummaryData Visualization Design PrinciplesIntroduction Enabling accurate comparisons Strive for simple appearance Engage the analyst SummaryLinked MicromapsIntroduction Page layout Data encodings Micromap highlighting Multivariate data Multivariate sorting Pushing the envelope Software SummaryConditioned MicromapsIntroduction One-way conditioned layouts Two-way layouts Higher order layouts Describing and comparing groups of regions Weighted descriptions and comparisons Alternative views CCmaps software options SummaryComparative MicromapsIntroduction Representing change Types of comparisons Two-way comparisons Rates of change Alternative views Summary and future directionsPutting It All TogetherSummary Exploration of Louisiana population changes after the 2005 hurricanes Concluding remarksAppendix 1: Data sources and notesAppendix 2: Symmetric perceptual groupingsReferencesIndex
