designing data visualizations

[...]... with more or new data While they may show great volumes of data, information visualizations are often less aesthetically rich than infographics As you will have inferred from the title of this book, it is this latter category of data visualizations with which we are primarily concerned here However, the principles we present are relevant to the design of both infographics and data visualizations Exploration... visualizations based on the relationships between the three necessary players: the designer, the reader, and the data This section refers to explanatory (or hybrid) visualizations exclusively, because it discusses designing visualizations of data with known parameters and stories If you don’t yet know the message you intend to convey, then you’re still in an exploration phase, and probably aren’t designing. .. 1: Classifications of Visualizations Data Visualization By contrast, we suggest that the terms data visualization and information visualization (casually, data viz and info viz) are useful for referring to any visual representation of data that is: • algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods); • easy to regenerate with different data (the same... easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics); • often aesthetically barren (data is not decorated); and • relatively data- rich (large volumes of data are welcome and viable, in contrast to infographics) Data visualizations are initially designed by a human, but are then drawn algorithmically with graphing,... your data and visual encodings Here is a handy glossary for quick reference Chart: Something that shows qualitative information (e.g., flow charts) Data dimensions: One single channel of data A stock graph may comprise four properties: date, price, company, and market cap Each is a unique dimension of the data, which can be encoded separately, with a different visual property Data visualization: Visualizations. .. of data dimensions can be described as the level of complexity of the visualization As visualizations become more complex, they are more challenging to design well, and can be more difficult to learn from For that reason, visualizations with no more than three or four dimensions of data are the most common— though visualizations with six, seven, or more dimensions can be found Adding more volume or data. .. once you understand the filters he may be using, you can discern how best to present that information to him Data The third source of influence in designing a visualization is your data The best visualizations will reveal what is interesting about the specific data set you’re working with Different data may require different approaches, encodings, or techniques to reveal its interesting aspects While default... referring to any visual representation of data that is: • manually drawn (and therefore a custom treatment of the information); • specific to the data at hand (and therefore nontrivial to recreate with different data) ; • aesthetically rich (strong visual content meant to draw the eye and hold interest); and Infographics versus Data Visualization | 5 • relatively data- poor (because each piece of information... or a point of view from the designer to the reader Explanatory visualizations typically have a specific “story” or information that they are intended to transmit Exploratory visualization: Data visualizations that are used by the designer for self-informative purposes to discover patterns, trends, or sub-problems in a dataset Exploratory visualizations typically don’t have an already-known story Graph:... going and for cheering us on xii | Preface PART I What Will You Design? Since you are interested in learning more about designing data visualizations (by virtue of the fact that you’re reading this book), then chances are good that you have been the reader of other people’s data visualizations You may already understand—intuitively or consciously—some of the visual techniques that work well, and some . class="bi x0 y0 w0 h1" alt="" Designing Data Visualizations Noah Iliinsky and Julie Steele Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo Designing Data Visualizations by Noah Iliinsky. learning more about designing data visualizations (by virtue of the fact that you’re reading this book), then chances are good that you have been the reader of other people’s data visualizations. . to transmit. Exploratory visualization: Data visualizations that are used by the designer for self-informative purposes to discover patterns, trends, or sub-problems in a data- set. Exploratory visualizations typically