Data, Matter, Design presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of outputs. Technological changes, through the use of computational tools and processes, have radically altered and influenced our relationship to cities and the methods by which we design architecture, urban, and landscape systems. This book presents a wide range of curated projects and contributed texts by leading architects, urbanists, and designers that transform data as an abstraction, into spatial, experiential, and performative configurations within urban ecologies, emerging materials, robotic agents, adaptive fields, and virtual constructs. Richly illustrated with over 200 images, Data, Matter, Design is an essential read for students, academics, and professionals to evaluate and discuss how data in design methodologies and theoretical discourses have evolved in the last two decades and why processes of data collection, measurement, quantification, simulation, algorithmic control, and their integration into methods of reading and producing spatial conditions, are becoming vital in academic and industry practices.

Information matters to us. Whether recorded, recoded, or unregistered, information co-shapes our present and our becoming. This book advances new views on information and surveillance practices. Starting with a methodology for studying the liveliness of information, Kaufmann provides four empirical examples of making information matter: association, conversion, secrecy, and speculation. In so doing, she presents an original and comprehensive argument about the materiality of information and invites us to investigate, and to reflect about what matters. This is a go-to text for scholars and professionals working in the fields of surveillance, data studies, and the digitization of specific societal sectors.

What is Big Data, and why should you care? Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can. Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book. Starting with the basics – what IS data? And what makes it big? – Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of Things. Finally, she asks the big questions about where it's taking us; is it too big for its boots, or does it think too small? Are you a data point or a human being? Will this book be full of rhetorical questions? No. It also contains puns, asides, unlikely stories and engaging people, inspiring feats and thought-provoking dilemmas. Leaving you armed and ready to decide what you think about one of the decade's big ideas: big data.

You have a mound of data front of you and a suite of computation tools at your disposal. Which parts of the data actually matter? Where is the insight hiding? If you’re a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way. When incorporated into the process early and often, iterative visualization can help you refine the questions you ask of your data. Authors Danyel Fisher and Miriah Meyer provide detailed case studies that demonstrate how this process can evolve in the real world. You’ll learn: The data counseling process for moving from general to more precise questions about your data, and arriving at a working visualization The role that visual representations play in data discovery Common visualization types by the tasks they fulfill and the data they use Visualization techniques that use multiple views and interaction to support analysis of large, complex data sets

The authors summarize and synthesize research on the selection and presentation of data pertinent to public health and provide practical suggestions, based on this research summary and synthesis, on how scientists and other public health practitioners can better communicate data to the public, policy makers and the press.

For many outside of the scientific community, big data and the forms it takes, such as statistical lists, spreadsheets and graphs, often seem abstract and unintelligible. This book investigates how digital fabrication and traditional making approaches are being used to present data in newly engaging and interesting ways. The first part of the book introduces the basic premise of the data object and the concept of making digital data into a physical form. Contributors cover topics such as biometrics, new technology, the economics of data and open and community uses of data. The second part presents a selection of exemplar forms and contexts for the application of data-objects, such as smart surfaces, smart cities, augmented reality techniques and next generation technical interfaces that blend physical and digital elements. Making Data delivers the importance and likely future prevalence of physical representations of data. It explores the creative methods, processes, theories and cultural histories of making physical representations of information and proposes that the making of data into physical objects is the next important development in the data visualisation phenomenon.