Manuel Lima's smash hit Visual Complexity is now available in paperback. This groundbreaking 2011 book—the first to combine a thorough history of information visualization with a detailed look at today's most innovative applications—clearly illustrates why making meaningful connections inside complex data networks has emerged as one of the biggest challenges in twenty-first-century design. From diagramming networks of friends on Facebook to depicting interactions among proteins in a human cell, Visual Complexity presents one hundred of the most interesting examples of informationvisualization by the field's leading practitioners.

Our ability to generate information now far exceeds our capacity to understand it. Finding patterns and making meaningful connections inside complex data networks has emerged as one of the biggest challenges of the twenty-first century. In recent years, designers, researchers, and scientists have begun employing an innovative mix of colors, symbols, graphics, algorithms, and interactivity to clarify, and often beautify, the clutter. From representing networks of friends on Facebook to depicting interactions among proteins in a human cell, Visual Complexity presents one hundred of the most interesting examples of information-visualization by the field's leading practitioners.

This radical first course on complex analysis brings a beautiful and powerful subject to life by consistently using geometry (not calculation) as the means of explanation. Aimed at undergraduate students in mathematics, physics, and engineering, the book's intuitive explanations, lack of advanced prerequisites, and consciously user-friendly prose style will help students to master the subject more readily than was previously possible. The key to this is the book's use of new geometric arguments in place of the standard calculational ones. These geometric arguments are communicated with the aid of hundreds of diagrams of a standard seldom encountered in mathematical works. A new approach to a classical topic, this work will be of interest to students in mathematics, physics, and engineering, as well as to professionals in these fields.

In this book, three main notions will be used in the editors search of improvements in various areas of computer graphics: Artificial Intelligence, Viewpoint Complexity and Human Intelligence. Several Artificial Intelligence techniques are used in presented intelligent scene modelers, mainly declarative ones. Among them, the mostly used techniques are Expert systems, Constraint Satisfaction Problem resolution and Machine-learning. The notion of viewpoint complexity, that is complexity of a scene seen from a given viewpoint, will be used in improvement proposals for a lot of computer graphics problems like scene understanding, virtual world exploration, image-based modeling and rendering, ray tracing and radiosity. Very often, viewpoint complexity is used in conjunction with Artificial Intelligence techniques like Heuristic search and Problem resolution. The notions of artificial Intelligence and Viewpoint Complexity may help to automatically resolve a big number of computer graphics problems. However, there are special situations where is required to find a particular solution for each situation. In such a case, human intelligence has to replace, or to be combined with, artificial intelligence. Such cases, and proposed solutions are also presented in this book.

How can you turn dry statistics into attractive and informative graphs? How can you present complex data sets in an easily understandable way? How can you create narrative diagrams from unstructured data? This handbook of information design answers these questions. Nicole Lachenmeier and Darjan Hil condense their extensive professional experience into an illustrated guide that offers a modular design system comprised of 80 elements. Their systematic design methodology makes it possible for anyone to visualize complex data attractively and using different perspectives. At the intersection of design, journalism, communication and data science, Visualizing Complexity opens up new ways of working with abstract data and invites readers to try their hands at information design.

This book provides a systematic introduction to functions of one complex variable. Its novel feature is the consistent use of special color representations – so-called phase portraits – which visualize functions as images on their domains. Reading Visual Complex Functions requires no prerequisites except some basic knowledge of real calculus and plane geometry. The text is self-contained and covers all the main topics usually treated in a first course on complex analysis. With separate chapters on various construction principles, conformal mappings and Riemann surfaces it goes somewhat beyond a standard programme and leads the reader to more advanced themes. In a second storyline, running parallel to the course outlined above, one learns how properties of complex functions are reflected in and can be read off from phase portraits. The book contains more than 200 of these pictorial representations which endow individual faces to analytic functions. Phase portraits enhance the intuitive understanding of concepts in complex analysis and are expected to be useful tools for anybody working with special functions – even experienced researchers may be inspired by the pictures to new and challenging questions. Visual Complex Functions may also serve as a companion to other texts or as a reference work for advanced readers who wish to know more about phase portraits.