dataviz
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Depuis la fin des années 90, les expériences concluantes de Data-journalism représentent une nouvelle jeunesse pour les métiers de la presse, longtemps dominée par les règles de la presse d'opinion. En voici les principes fondateurs. Que défend OWNI. À l’opposé des intentions de la presse d’opinion, celle qui dicte une manière de penser le monde, une nouvelle presse émerge, désireuse de transmettre toutes les données susceptibles de lire le monde différemment, de nourrir toutes les pensées critiques, sans tenter d’en imposer une. Pour cette presse-là, le journalisme de données (ou Data Journalism à l’anglo-saxonne) s’apparente à une nouvelle profession de foi.
Voici un très joli motion tout en Noir et blanc qui montre la quantité assez astronomique de pub réalisées par les plus grandes marques qu'on se prend dans la tronche toute la journée… Une bonne réflexion sur l'impact et l'efficacité de tous ces visuels au quotidien qui s'approchent par moment d'une certaine propagande… C'est le Studio "Smack" qui est à l'origine de cette création minimaliste, découvrez la vidéo dans la suite du billet ! <p style="text-align:right;color:#A8A8A8"></p>
Try out the newest version of IBM Many Eyes! New site design and layout Find visualization by category and industry New visualization expertise and thought leadership section Expertise on the Expert Eyes blog Learn best practices to create beautiful, effective visualizations New, innovative visualizations from the visualizations experts of IBM Research New visualization options
Protovis is an open-source Javascript visualization library by the Stanford Visualization Group and has become one of the preferred tools in our arsenal. If you want to get started with the popular toolkit too, Jerome Cukier has a comprehensive tutorial about how to work with data in Protovis. The tutorial is split in five parts covering using ( 1 , 2 ), sorting ( 3 ) and reshaping ( 4 ) arrays as well as how to structure data to work with complex structures like treemaps or force-directed layouts ( 5 ). For the past year or so I have been dabbling with protovis. I don’t have a heavy CS background but protovis is supposedly easy to pick up for people like me, who are vaguely aware that computers can make calculations but who need to check the manual for the most mundane programming instructions.
We have many dashboards available to us today in many contexts: finance, forecasts, consumption, demographics, etc.
Enjoy these sample visualizations built with Protovis. For any example, use your browser to view the source or the backing dataset. Protovis is no longer under active development. The final release of Protovis was v3.3.1 (4.7 MB) .
Catherine from visualizingeconomics.com has written an interesting article about the roles in data visualization. The posts that your reading right now are my thoughts about the diagram she created and how I would alter the stages in the process. I recommend reading her article first, as my answer is based on her thoughts. But here’s the diagram that illustrates well the basic idea:
. Quand doit-on l’utiliser ? Sauf à vouloir exploiter le sentiment de maitrise du sujet plus qu’à transmettre des données précises, le camembert est quasiment toujours à proscrire.
Review April 20, 2011 06:00 AM ET Computerworld - You may not think you've got much in common with an investigative journalist or an academic medical researcher. But if you're trying to extract useful information from an ever-increasing inflow of data, you'll likely find visualization useful -- whether it's to show patterns or trends with graphics instead of mountains of text, or to try to explain complex issues to a nontechnical audience.
Google Fusion Tables (ci-dessus, une carte des Etats-Unis montrant le pourcentage de foyers ayant un accès Internet en 2007, par états, d'après le bureau américain du recensement) Vous avez des données à explorer ? Voici quelques outils qui pourront vous être utiles pour les transformer en informations et en graphiques attrayants. Pour faire parler des données, rien ne vaut une panoplie d'outils de visualisation graphique. Il en existe de nombreux, notamment destinés aux professionnels versés dans l'analyse statistique. Mais leur prix, généralement élevé, ne convient pas aux utilisateurs moins spécialisés qui n'ont besoin qu'occasionnellement d'afficher des données sous une forme graphique.
One of the most frequent questions I get is, " What software do you use to visualize data?" A lot of people are excited to play with their data, but don't know how to go about doing it or even start. Here are the tools I use or have used and resources that I own or found helpful for data visualization – starting with organizing the data, to graphs and charts, and lastly, animation and interaction. Organizing the Data by sleepy sparrow
AutoMap is a text mining tool developed by CASOS at Carnegie Mellon. Input: one or more unstructured texts. Output: DyNetML files and CS files. AutoMap is designed to work seamlessly with ORA. AutoMap enables the extraction of information from texts using Network Text Analysis methods. AutoMap supports the extraction of several types of data from unstructured documents.
Read more on Author Profile here . Who’s Who? The Scopus Author Identifier helps solve one of the biggest problems associated with author searching: How do you distinguish between articles belonging authors with similar names? How can you be confident that you captured all results for an author when their name is recorded in different ways? And, can you be sure that names with unusual characters such as accents have been included?
CiteSpace is a freely available Java application for visualizing and analyzing trends and patterns in scientific literature. It is designed as a tool for progressive knowledge domain visualization ( Chen, 2004 ). It focuses on finding critical points in the development of a field or a domain, especially intellectual turning points and pivotal points. Detailed case studies are given in ( Chen, 2006 ) and other publications . CiteSpace provides various functions to facilitate the understanding and interpretation of network patterns and historical patterns, including identifying the fast-growth topical areas, finding citation hotspots in the land of publications, decomposing a network into clusters, automatic labeling clusters with terms from citing articles, geospatial patterns of collaboration, and unique areas of international collaboration.