Ann's Blog | Avoiding diagonal text in your charts, and other shameful mistakes I made before I knew better. I’ve been building portfolios of my work for a while. Over the past year or so I’ve been building an online portfolio of speaking and writing engagements. Before that, I collected paper copies of reports I worked on: Before The great sobering thing about portfolios is that you can bask in the glory of your wonderful work bow your head in shame. Most of these charts from my prior work were ineffective. One of these charts was so horrific that I just had to share it with you: What’s so bad? Each of these mistakes–from the before I knew better period of my career, never to be revisited again–kept viewers from understanding the information: Generic and centered title (Should have a “so what?”
Introducing the Data Visualization Checklist Now I know better. Guess how my old chart scored on the Data Visualization Checklist? Preview the checklist below. Remakes The first phase in learning about data visualization is usually critiquing charts. So I’ll lead by example. Why the black and white? P.S. A Handsome Atlas: Wildly Awesome Data Visualizations from the Nineteenth Century. The Data Visualisation Catalogue. Design Principles. Description People using your application may have all different types of backgrounds and understandings of data and visualizations. It is important to keep this in mind when designing. If you will have a mix of novice and advanced users, it is important to think about how that will affect the application’s workflow and the data presentation. You don’t want to overwhelm and confuse the novice user, but you want to include appropriate features for the advanced user.
Choosing the right chart for the information you have and using the simplest appropriate visualizations, especially if you will have novice users, are two important things to consider. One thing to do is to use common language. You can also make a more complicated visualization usable for a novice by clarifying with annotations, tooltips or other using other ways to provide instruction. Another option would be the use of drill down. Customization is also an option, though not used often. Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization.
Data Visualization. Data Visualization for the Public Sector. CourseWiki - CS448B Data Visualization. The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.
In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems. There are no prerequisites for the class and the class is open to graduate students as well as advanced undergraduates. Schedule Tu Oct 26: Color. Parsons Journal for Information Mapping. The Parsons Journal for Information Mapping (PJIM) is an academic journal and online forum to promote research, writing, and digital execution of theories in the field of information mapping and its related disciplines. Our mission is to identify and disseminate knowledge about the fields of information mapping, information design, data visualization, information taxonomies/structures, data analytics, informatics, information systems, and user interface design.
PJIM focuses on both the theoretical and practical aspects of information visualization. With each issue, the Journal aims to present novel ideas and approaches that advance the field of Knowledge Visualization through visual, engineering, and cognitive methods. We have an rolling, open-call for submissions for original essays, academic manuscripts, interactive and non-interactive projects, and project documentation that address representation, processing, and communication of information.
Le « Camembert » : Par-delà la polémique, quand peut-on utiliser. Super Crunchers. Super Crunchers: Why Thinking-by-Numbers Is the New Way to be Smart is a book written by Ian Ayres, a law professor at Yale Law School about how number analysis, such as multiple regression analysis affects all areas of life, often in unexpected ways. Awards[edit] The Economist - Books of the Year 2007 [1] Notes[edit] See also[edit] Freakonomics External links[edit]