Visualizing Data Patterns With Micromaps

"The book is extremely well written. I was totally absorbed by chapters one through four. It is obvious that the authors are in total command of the topic and bring years of experience in the field to the project. The authors understand what areas need special attention and explanation. The language flows nicely and the text is illustrative and entertaining. The limitations of micromaps are known and discussed. The reader gains a good understanding of what they are for and what they can and cannot accomplish.---Oliver Schabenberger, SAS Institute Inc., Cary, North Carolina, USA

This is a terrific book and it introduces some clever tools."---David Berrigan, NIH/NCI

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. 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.

Showing how to bring statistics to life, this full-color book helps readers simultaneously explore the statistical geographic patterns in their data. The supplementary website provides many resources, including boundary files, real-world data sets, Java-based CCmaps for conditioned micromaps, and R functions and scripts for linked and comparative micromaps.

Daniel B. Carr is Professor of Statistics at George Mason University in Fairfax, Virginia. Building on his early experience in developing static and dynamic graphics at Pacific Northwest National Laboratory, Dr. Carr continues to use new data as the motivation to create new graphics. He has taught statistical graphics to hundreds of graduate students in the computational data sciences. For more than 30 years, he has developed graphical and computational methods for exploratory visualization and has collaborated with researchers at several federal agencies, including working with Dr. Pickle at the National Cancer Institute (NCI). Dr. Carr is a Fellow of the American Statistical Association.

Linda Williams Pickle is principal and chief statistician at StatNet Consulting, LLC in Gaithersburg, Maryland, and Adjunct Professor of Geography and Public Health Services at Pennsylvania State University. She has devoted more than 30 years to the cancer research community, working for the National Cancer Institute (NCI), the Vincent T. Lombardi Comprehensive Cancer Center at Georgetown University, and the National Center for Health Statistics (NCHS). Dr. Pickle has published extensively about the spatial patterns of disease, including the award-winning Atlas of United States Mortality, which was the first of its kind to use statistical modeling in the background and to produce age-specific mortality rates based on modeled data. She is a Fellow of the American Statistical Association.