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 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.

在認知心理學、統計圖形學、計算機科學和地圖製作等領域的研究基礎上，經過15多年的發展，微地圖設計已成為主流統計視覺化的一部分。《Visualizing Data Patterns with Micromaps》這本全彩色書籍匯集了這一領域兩位領先專家的研究成果，介紹了微地圖的設計變體和應用，將統計信息與一組有組織的小地圖相關聯。這本書幫助讀者同時探索他們數據中的統計和地理模式。

在介紹了三種主要類型的微地圖之後，作者們總結了支持空間數據模式探索和交流的可視化工具設計背後的研究。然後他們解釋了如何將這些研究成果應用於微地圖設計，並詳細介紹了相關的連接、條件和比較微地圖設計的具體內容。為了比較和對比它們的目的、限制和優勢，最後一章將這三種技術應用於路易斯安那州在卡特里娜颶風和里塔颶風之前和之後的人口統計數據。

附加網站：書籍的網站http://mason.gmu.edu/dcarr/Micromaps/提供了許多附加功能，包括邊界文件和真實數據集，涉及物種生物多樣性和酗酒等主題。一個完整的數據示例文件夾提供了癌症統計數據、風險因素和人口統計數據。該網站還包括CCmaps，這是一個用Java編寫的條件微地圖的動態實現，以及一個通用微地圖程序的鏈接。它還包含用於連接和比較微地圖的R函數和腳本，可以重新創建書中所有相應的示例。

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.

Daniel B. Carr是維吉尼亞州費爾法克斯的喬治梅森大學統計學教授。在太平洋西北國家實驗室開發靜態和動態圖形的早期經驗基礎上，Carr博士繼續以新數據作為創建新圖形的動機。他曾向數百名計算數據科學研究生教授統計圖形學。三十多年來，他一直為探索性可視化開發圖形和計算方法，並與多個聯邦機構的研究人員合作，包括與國家癌症研究所的Pickle博士合作。Carr博士是美國統計學會的會士。

Linda Williams Pickle是馬里蘭州蓋瑟斯堡的StatNet Consulting, LLC的首席統計師兼主任，也是賓夕法尼亞州立大學地理學和公共衛生服務的兼職教授。她致力於癌症研究社區超過30年，曾在國家癌症研究所（NCI）、喬治敦大學文森特·T·隆巴第綜合癌症中心和國家健康統計中心（NCHS）工作。Pickle博士廣泛發表有關疾病空間模式的文章，包括屢獲殊榮的《美國死亡地圖集》，該地圖集是第一個在背景中使用統計建模並基於建模數據生成年齡特定死亡率的作品。她是美國統計學會的會士。