formation. The basic ideas underlying knowledge visualization and information vi- alization are outlined. In a short preview of the contributions of this volume, the idea behind each approach and its contribution to the goals of the book are outlined. 2 The Basic Concepts of the Book Three basic concepts are the focus of this book: "data", "information", and "kno- edge". There have been numerous attempts to define the terms "data", "information", and "knowledge", among them, the OTEC Homepage "Data, Information, Kno- edge, and Wisdom" (Bellinger, Castro, & Mills, see http://www.syste- thinking.org/dikw/dikw.htm): Data are raw. They are symbols or isolated and non-interpreted facts. Data rep- sent a fact or statement of event without any relation to other data. Data simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself.

This book constitutes the refereed proceedings of the First International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2004, held in Palma de Mallorca, Spain, in September 2004. The 28 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers address all current issues in cooperative design, visualization, and engineering, ranging from theoretical and methodological topics to various systems and frameworks to applications in a variety of fields.

Sophisticated interactive maps are increasingly used to explore information - guiding us through data landscapes to provide information and prompt insight and understanding. Geovisualization is an emerging domain that draws upon disciplines such as computer science, human-computer interactiondesign, cognitive sciences, graphical statistics, data visualization, information visualization, geographic information science and cartography to discuss, develop and evaluate interactive cartography.This review and exploration of the current and future status of geovisualization has been produced by key researchers and practitioners from around the world in various cognate fields ofstudy. The thirty-six chapters present summaries of work undertaken, case studies focused on new methods and their application, system descriptions, tests of their implementation, plans for collaboration and reflections on experiences of using and developing geovisualization techniques.In total, over 50 pages of color are provided in the book along with more than 250 color images on an enclosed CD-ROM.

Researchers and data analysts are increasingly relying on graphical tools to assist them in modeling their data, generating their hypotheses, and gaining deeper insights on their experimentally acquired data. Recent advances in technology have made available more improved and novel modeling and analysis media that facilitate intuitive, task-driven exploratory analysis and manipulation of the displayed graphical representations. In order to utilize these emerging technologies researchers must be able to transform experimentally acquired data vectors into a visual form or secondary representation that has a simple structure and, is easily transferable into the media. As well, it is essential that it can be modified or manipulated within the display environment. This thesis presents a data-driven modeling technique that utilizes the basic learning strategy of an unsupervised clustering algorithm, called the self-organizing feature map, to adaptively learn topological associations inherent in the data and preserve them within the topology imposed by its predefined spherical lattice, thereby transforming the data into a 3D tessellated form. The tessellated graphical forms originate from a sphere thereby simplifying the process of computing its transformation parameters on re-orientation within an interactive, task-driven, graphical display medium. A variety of data sets including six sets of scattered 3D coordinate data, chaotic attractor data, the more commonly used Fisher s Iris flower data, medical numeric data, geographic and environmental data are used to illustrate the data-driven modeling and visualization mechanism. The modeling algorithm is first applied to scattered 3D coordinate data to understand the influence of the spherical topology on data organization. Two cases are examined, one in which the integrity of the spherical lattice is maintained during learning and, the second, in which the inter-node connections in the spherical lattice are adaptively changed during learning. In the analysis, scattered coordinate data of freeform objects with topology equivalent to a sphere and those whose topology is not equivalent to a sphere are used. Experiments demonstrate that it is possible to get reasonably good results with the degree of resemblance, determined by an average of the total normalized error measure, ranging from 6.2x10-5 1.1x10-3. The experimental analysis using scattered coordinate data facilitates an understanding of the algorithm and provides evidence of the topology-preserving capability of the spherical self-organizing feature map. The algorithm is later implemented using abstract, seemingly random, numeric data. Unlike in the case of 3D coordinate data, wherein the SOFM lattice is in the same coordinate frame (domain) as the input vectors, the numeric data is abstract. The criterion for deforming the spherical lattice is determined using mathematical and statistical functions as measures-of information that are tailored to reflect some aspect of meaningful, tangible, inter-vector relationships or associations embedded in the spatial data that reveal some physical aspect of the data. These measures are largely application-dependent and need to be defined by the data analyst or an expert. Interpretation of the resulting 3D tessellated graphical representation or form (glyph) is more complex and task dependent as compared to that of scattered coordinate data. Very simple measures are used in this analysis in order to facilitate discussion of the underlying mechanism to transform abstract numeric data into 3D graphical forms or glyphs. Several data sets are used in the analysis to illustrate how novel characteristics hidden in the data, and not easily apparent in the string of numbers, can be reflected via 3D graphical forms. The proposed data-driven modeling approach provides a viable mechanism to generate 3D tessellated representations of data that can be easily transferred to a graphical modeling and ana

This comprehensive and well-established cartography textbook covers the theory and the practical applications of map design and the appropriate use of map elements. It explains the basic methods for visualizing and analyzing spatial data and introduces the latest cutting-edge data visualization techniques. The fourth edition responds to the extensive developments in cartography and GIS in the last decade, including the continued evolution of the Internet and Web 2.0; the need to analyze and visualize large data sets (commonly referred to as Big Data); the changes in computer hardware (e.g., the evolution of hardware for virtual environments and augmented reality); and novel applications of technology. Key Features of the Fourth Edition: Includes more than 400 color illustrations and it is available in both print and eBook formats. A new chapter on Geovisual Analytics and individual chapters have now been dedicated to Map Elements, Typography, Proportional Symbol Mapping, Dot Mapping, Cartograms, and Flow Mapping. Extensive revisions have been made to the chapters on Principles of Color, Dasymetric Mapping, Visualizing Terrain, Map Animation, Visualizing Uncertainty, and Virtual Environments/Augmented Reality. All chapters include Learning Objectives and Study Questions. Provides more than 250 web links to online content, over 730 references to scholarly materials, and additional 540 references available for Further Reading. There is ample material for either a one or two-semester course in thematic cartography and geovisualization. This textbook provides undergraduate and graduate students in geoscience, geography, and environmental sciences with the most valuable up-to-date learning resource available in the cartographic field. It is a great resource for professionals and experts using GIS and Cartography and for organizations and policy makers involved in mapping projects.

In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.