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Visual insights : a practical guide to making sense of data
\"In 2010, Börner published Atlas of Science: Visualizing what We Know with us, and the work found an audience across a wide range of readers. Although Katy is busy working on the second Atlas volume, she has taken her info viz talents to the street via an Indiana University MOOC. This course applies advanced data mining and visualization techniques to communicate temporal, geospatial, topical, and network data of IVMOOC13 teaching and learning, provides instructions on how to collaborate with external clients and presents the best 2013 project results, closes with an outlook on MOOC trends and opportunities. This book is for this course. The work is the core of her information visualization course and is intended to serve as a stand-alone resource and how-to guide for those seeking to learn the tricks of information visualization. Part \"how-to\" book and part primer in data and information across the disciplines, Börner's work provides the perfect text for beginner mastery of the topic\"-- Provided by publisher.
Book
Visual Insights
In the age of Big Data, the tools of information visualization offer us a macroscope to help us make sense of the avalanche of data available on every subject. This book offers a gentle introduction to the design of insightful information visualizations. It is the only book on the subject that teaches nonprogrammers how to use open code and open data to design insightful visualizations. Readers will learn to apply advanced data mining and visualization techniques to make sense of temporal, geospatial, topical, and network data. The book, developed for use in an information visualization MOOC, covers data analysis algorithms that enable extraction of patterns and trends in data, with chapters devoted to \"when\" (temporal data), \"where\" (geospatial data), \"what\" (topical data), and \"with whom\" (networks and trees); and to systems that drive research and development. Examples of projects undertaken for clients include an interactive visualization of the success of game player activity inWorld of Warcraft; a visualization of 311 number adoption that shows the diffusion of non-emergency calls in the United States; a return on investment study for two decades of HIV/AIDS research funding by NIAID; and a map showing the impact of the HiveNYC Learning Network.Visual Insightswill be an essential resource on basic information visualization techniques for scholars in many fields, students, designers, or anyone who works with data.
eBook
WHEN
Chapters 2–6 introduce different types of analysis and resulting visualizations that answer a specific type of question. This chapter aims to answer “WHEN” questions using temporal data, analyses, and visualizations. The main goal is to understand the temporal distribution of datasets; to identify growth rates, latency to peak times, or decay rates; to see patterns in time-series data, such as trends, seasonality, or bursts.
Each theory part of the subsequent five chapters starts with a discussion of exemplary visualizations followed by an overview and definition of key terminology, and introduction of general workflows. This chapter also introduces burst detection,
Book Chapter
Discussion and Outlook
This chapter reviews lessons learned in the IVMOOC as delivered in Spring 2013. We briefly review feedback provided by students, present results of an in-depth analysis of IVMOOC data, and conclude with a discussion of planned activities related to the further development of MOOC content and delivery.
The IVMOOC at Indiana University attracted 1,901 students from more than 90 countries. However, few of the IVMOOC 2013 students completed the course—a feature it shares with most other MOOCs. As a graduate-level course, the midterm and final were particularly demanding, and many students could not complete the client work due to
Book Chapter
Visualization Framework and Workflow Design
Welcome to the Information Age, where each one of us receives more information via tweets, emails, news, and other data streams each day than can humanly be processed in 24 hours; and anyone with an Internet connection has access to a majority of humankind’s knowledge. Our offices are filling up and our email inboxes are overflowing (see Figure 1.1, left). We urgently need more effective ways to make sense of this massive amount of data—to navigate and manage information, to identify collaborators and friends, or to notice patterns and trends (see Figure 1.1, right).
This book teaches you how
Book Chapter
WHAT
In this chapter, we will discuss topical (also called textural, linguistic, or semantic) data analysis and visualization to answer “WHAT” questions. The termtopic analysisis used in a variety of ways, but for the purposes of this book it means extracting a set of unique words or word profiles and their frequencies to determine the topic coverage of a body of text. That is, we will be using texts (e.g., from article abstracts or grant titles) to identify major topics, their interrelations, and their evolution over time at different levels of analysis—micro to macro.
Just like the previous
Book Chapter
Dynamic Visualizations and Deployment
Some visualizations are too large or too complex to comprehend easily. In these cases, a dynamic deployment that supports interactive search, filtering, clustering, zoom and pan, or details on demand is beneficial. Tools like Tableau,¹ Gephi,² but also GUESS,³ and Cytoscape⁴ available as Sci2 Tool plugins, support interactive visualizations for data exploration and communication. In this chapter, we discuss the design of dynamic visualizations and the deployment of interactive visualizations via desktop programs, interactive online visualizations, and large touchscreens. Online visualization services such as Microscoft’s Zoom. it⁵ or Gigapan⁶ that support sharing of very high-resolution images such as largescale visualizations
Book Chapter