Ealing Visualizations
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The Visualization of Spatial Social Structure
How do you draw a map of 100,000 places, of more than a million flows of people, of changes over time and space, of different kinds of spaces, surfaces and volumes, from human travel time to landscapes of hopes, fears, migration, manufacturing and mortality? How do you turn the millions of numbers concerning some of the most important moments of our lives into images that allow us to appreciate the aggregate while still remembering the detail? The visualization of spatial social structure means, literally, making visible the geographical patterns to the way our lives have come to be socially organised, seeing the geography in society. To a statistical readership visualization implies using data. More widely defined it implies freeing our imaginations. The Visualization of Spatial Social Structure introduces the reader to new ways of thinking about how to look at social statistics, particularly those about people in places. The author presents a unique combination of statistical focus and understanding of social structures and innovations in visualization, describing the rationale for, and development of, a new way of visualizing information in geographical research. These methods are illustrated through extensive full colour graphics; revealing mistakes, techniques and discoveries which present a picture of a changing political and social geography. More complex aspects on the surface of social landscapes are revealed with sculptured symbols allowing us to see the relationships between the wood and the trees of social structure. Today's software can be so flexible that these techniques can now be emulated without coding. This book centres on a particular place and time; 1980s Britain, and a particular set of records; routine social statistics. A great deal of information about the 80s' social geography of Britain is contained within databases such as the population censuses, surveys and administrative data. Following the release of the 2011 census, now is a good time to look back at the past to introduce many new visualization techniques that could be used by future researchers.
Information Visualization
Offering an interactive, visual opportunity to learn how to clarify and interpret data, this text features real world examples and applications.
Visualization for Social Data Science
"This is an important book on an important topic. I particularly like the examples showing different visualizations of the same data and the parallel presentation of graphics and code. And I absolutely love the chapter on visual storytelling. I can't wait to use this book in my classes."- Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University, New York "A book that gives learners the inspiration, knowledge and worked examples to create cutting edge visualisations of their own."- James Chesire, Professor of Geographic Information and Cartography, University College London Visualization for Social Data Science provides end-to-end skills in visual data analysis. The book demonstrates how data graphics and modern statistics can be used in tandem to process, explore, model and communicate data-driven social science. It is packed with detailed data analysis examples, pushing you to do visual data analysis. As well as introducing, and demonstrating with code, a wide range of data visualizations for exploring patterns in data, Visualization for Social Data Science shows how models can be integrated with graphics to emphasise important structure and de-emphasise spurious structure and the role of data graphics in scientific communication -- in building trust and integrity. Many of the book’s influences are from data journalism, as well as information visualization and cartography. Each chapter introduces statistical and graphical ideas for analysis, underpinned by real social science datasets. Those ideas are then implemented via principled, step-by-step, workflows in the programming environment R. Key features include: • Extensive real-world data sets and data analysis scenarios in Geography, Public Health, Transportation, Political Science; • Code examples fully-integrated into main text, with code that builds in complexity and sophistication; • Quarto template files for each chapter to support literate programming practices; • Functional programming examples, using tidyverse, for generating empirical statistics (bootstrap resamples, permutation tests) and working programmatically over model outputs; • Unusual but important programming tricks for generating sophisticated data graphics such as network visualizations, dot-density maps, OD maps, glyphmaps, icon arrays, hypothetical outcome plots and graphical line-ups plots. Every data graphic in the book is implemented via ggplot2. • Chapters on uncertainty visualization and data storytelling that are uniquely accompanied with detailed, worked examples.