ISBN-13: 9781420075731 / Angielski / Twarda / 2010 / 182 str.
ISBN-13: 9781420075731 / Angielski / Twarda / 2010 / 182 str.
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."
Recognized as important tools in the geovisualization of both data exploration and communication, micromaps link statistical graphical elements in progressively highlighted views of the same small map. This book compiles information on these graphical methods into one resource. The authors demonstrate how micromaps can be used for a wide variety of tasks that include data quality assessment, communication, pattern discovery, hypothesis generation, and model criticism. The book includes pointers for using Java shareware such as CCmaps and to R script files which can be used for micromap production.