Why d3 Will Change How We Publish Maps. Publishing maps to the web. Simple right? Just reproject all your data to web mercator and be done with it. Have we all learned to live with the crazy distortion? Do we need mercator to help us get our galleons from the Spanish Main back to the king? Of course not, but we’ve all learned the world is not flat since then. Five years ago I complained about polar projections because of a project I was working on. I even offered up a SVG example from Esri’s ArcWeb project (that’s how desperate I was!). Well I’ve never really abandoned this quest. D3.js is a JavaScript library for manipulating documents based on data. Bingo, D3 takes the browser out of the equation so you can just focus on the visualization. The examples are great and this sick D3 satellite view from @vtcraghead.
Flare | Data Visualization for the Web. Create: Information Wheel | thinkDataVis. The top 20 data visualisation tools. Perceptual Edge’s 2012 Dashboard Design Competition. A few weeks ago I mentioned in this blog that I would soon announce the 2012 Perceptual Edge Dashboard Design Competition.
Today, the competition officially begins. This will be the most challenging event of this type to date resulting in the most esteemed award for dashboard design (in my not-so-humble opinion) since I judged a similar competition for the B-Eye-Network back in 2006. This competition will serve several purposes: A showcase for the current state of expert dashboard design.An opportunity for me to use the submissions to teach best practices by critiquing several of them on this website and in the second edition of the book Information Dashboard Design, which I am currently writing.An opportunity to provide sample dashboard designs that could actually be used to improve the quality of education in schools, for this competition involves the design of a dashboard that could be used by teachers to monitor the performance of their students. Here are the basic facts: Take care, Unfolding - Interactive Map Library for Processing and Java.
Unfolding Library for Interactive Maps. Expressing UX Concepts Visually. By Barnabas Nagy Published: May 7, 2012 “Words are not always sufficient to describe things accurately.” It is all too easy to create UX deliverables that are not visually pleasing. But UX expertise encompasses Web design, graphic design, and branding, so why should we be satisfied with mediocre design in our deliverables?
When we present our personas, sitemaps, user flows, wireframes, and other design deliverables to our clients and stakeholders, it is our duty and responsibility to create well-designed deliverables. Words and Objects Are Not Enough People visualize words differently. People also visualize objects differently. Consequently, it is not right to expect our clients to understand a concept that we’ve outlined purely with boxes and arrows or words and objects.
Why Visuals Are Important “Visual communication lets people perceive concepts and ideas most easily.” Our perception of the world is primarily visual. By Bradford G. A More Visual Approach to Communicating UX Concepts. Online and Open-Source Resources for Data Sourcing, Visualization, and Analysis - Professor Bear Braumoeller. Five of the Guardian Datablog's best visualisations | Online Journalism Features. Guardian Datastore editor Simon Rogers: Rogers said: 'We're not just data analysers, we've become data providers' Data is the "new normal" in journalism, according to Simon Rogers, the editor of the Guardian’s Datablog and Datastore. The aim of the Guardian when it comes to data is to guide the public through the mass of information available and present it in an attractive and easy-to-understand format.
Speaking at the PPA (Professional Publishers Association) conference this week Rogers said: "What's happening is we're not just data analysers, we've become data providers. "What we’re doing is essentially journalism, but it’s different journalism. The root of it is all about the stories. " In his presentation to delegates, Rogers outlined 10 ways data is changing journalism today, using examples from the Guardian’s own Datablog to illustrate new techniques and tools used to visualise data for a variety of news stories.
Here are five of the best examples he shared from the Guardian Datablog: Try the VIDI Wizard: Build and Embed Visualizations | Data Visualization Demo. Why has Data Visualization Become so Popular? Not all that long ago, when someone mentioned data visualization images, graphics printed in the USA Today, New York Times or the Economist would instantly come to mind – not any more. Over the last two to three years there has been an explosion in the popularity of data visualization. There’s no question that data visualization is hot right now, but why? Why have we become so fascinated with data visualization? Why is it desirable and why is it useful? Today, it seems that whenever anyone mentions a new infographic or data visualization, hoards of people instantly line up to check it out. If any of you are like me, then I’m sure that there’s a little part of you that wonders why? If you’ve ever pondered this, then today we’re going to set you straight. According to Lliinsky, the reason why we are so enamored with data visualization is because it’s been written into our DNA.
The point of above example is to illustrate why data visualization is so important. Related. How to Pick a Chart for Your Dashboard. As Dashboard Spy readers know, dashboard chart selection is fraught with peril and the subject of many books and blogs. I’ve written at length about the relative merits of different chart types and stress how the decision of which chart to use should not be made frivolously nor at random. To help you (or perhaps to confound you further), I present the “Chart Chooser” or aka “Chart Selections Thought Starter” from www.extremepresentation.com. Take a look at this screengrab of the graphic and I’ll give you a higher resolution pdf link below the chart. For a larger pdf, use this link: Chart Suggestions – A Thought Starter Here’s an excerpt from the author: Choosing a good chart: Here’s something we came up with to help you consider which chart to use. By the way, the chart chooser is step 7 in the 10-step Extreme Presentation method for designing presentations that drive action.
Infographic vs. Data Visualization (Who Cares?) The more visually inclined we become in our quest to consume information, the blurrier the line between data vis and infographic gets. To the great dismay of art directors, designers, etc. these terms are even being used interchangeably. For clarification, I went digging around the Web and found a pretty great summation of the differences from Nick Iliinsky: “Infographics are the ones that are usually illustrated by a graphic designer; they’re probably done in Illustrator, there’s some data in them, but they’re not necessarily data-rich.
They tend to be manually authored, manually constructed — obviously on a computer — but somebody sat down and said, “we’re going to put the big windmill here for ‘more windpower’ and more sunshine for ‘more solar power’ and a smaller oil barrel here” or whatever. That’s an infographic. A data visualization tends to be generated automatically or algorithmically. In other words, the real difference between these guys is in the process. Communication Rules. R, Octave, and Python: A Follow-Up.
In my recent article posted on May 16, I compared functionalities for R, Octave and Python at a very high level. The article received many insightful comments. I wanted to share what the commenters had to say—this follow-up is to clarify or expand upon some of the points raised. I will focus on two hot discussion points here: Whether Python should be listed as a powerful analytical tool alongside with R, and whether R functions well with big data. Is Python a legitimate data analysis tool? Quite a few readers questioned Python’s position as an analytical tool.
“It is a programming language, … roughly akin to Perl, Java, Ruby, and other scripting and rapid application development languages. …” I realized that I need to differentiate “Python by itself” and Python with packages, thanks to comments from Dingles, HuguesT and tb()ne: My passion with Python started with its natural language processing capability when paired with the Natural Language Toolkit (NLTK).
Start the analysis: Miso: An open source toolkit for data visualisation. R training: Visualization, Big Data, Data Mining, and Marketing Analytics. Revolution Analytics is hosting several live and online courses over the next couple of months that will be of interest to R users looking to hone their skills: Visualization in R with ggplot2. Garrett Grolemund and Winston Chang instruct how to use the ggplot2 package to make, format, label and adjust graphs using R.
(August 28, Redwood City, CA.)Big Data Analytics with RevoScaleR. Jared Lander provides training on how to take advantage of the capabilities of RevoScaleR for high performance analytics on big datasets. (September 6-7, online.)Intro to R for Data Mining. Joseph Rickert teaches a class with a combination of lecture and labs to instruct students on how to effectively use R for Data Mining. (September 13 & 20, online.)Analytics for Marketing. Click the links above for full course descriptions and pricing and registration information. To leave a comment for the author, please follow the link and comment on his blog: Revolutions. Battle of the Charts: Why Cartesian Wins Against Radial. Undeniably, radial charts based on a polar coordinate system are more visually appealing than regular line, area and bar charts, or even tree maps, all of which are based on a cartesian coordinate system. Unfortunately, they are rarely better at communicating information. Radial charts are things like radar charts, Coxcomb charts, radial bar charts, or radial tree maps.
These charts are all very visually interesting. The have relationships that our brain can easily detect, but that are hard for us to interpret. This duality is why they are more beautiful, but at the same time worse, for showing data. On the other hand, cartesian charts make the data much easier to digest. So, why are the cartesian versions better? Each chart has its own issues that can be slightly different from the other charts. Radar Charts Radar charts are basically a line or area chart plotted on polar coordinates. One place where these charts are generally seen as acceptable is in small multiples. Coxcomb Charts. Visualizing Massive Amounts of Big Data. At the end of the day, the whole point of a business intelligence (BI) application is to make it easier to discern patterns and trends that would otherwise not be obvious.
As such, the competition between BI applications is ultimately going to come down to which one best fulfills that mission, especially in an era where Big Data is making massive amounts of information readily available. Pentaho upped its game in that regard with release today of version 4.5 of its namesake open source BI application, which enhances the application's core visualization engine with new geo-mapping, heat grids, scatter/bubble chart visualizations, interactive visual analysis capabilities such as lasso filtering, zoom and attribute highlighting on all chart types, and a variety of reporting enhancements.
The good news is that rather than extrapolating insights based on a thin slice of data, NoSQL databases make it easier and more affordable to base decisions on large sets of complete data. Interactive Dynamics for Visual Analysis. Graphics Jeffrey Heer, Stanford University Ben Shneiderman, University of Maryland, College Park The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors.
In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data. Visualization provides a powerful means of making sense of data. The goal of this article is to assist designers, researchers, professional analysts, procurement officers, educators, and students in evaluating and creating visual analysis tools. Some visualization system designers have explored alternative approaches.
The Miso Project. The Audacity of the Visually Inclined. Michael Dillon Scott has a very bold face. His eyes are deeply set, his brows are thick and rumpled, and the pronounced divot between his mouth and nose (I looked it up, it’s called a philtrum) gives off the impression that he is constantly on the verge of smiling. As the author of over 100 books, including a recently completed series called The Secrets of the Immortal Nicholas Flamel, Scott attributes his prolific output to the use of visual processes (hence the details — it’s impossible to mention him without getting visual about it). The Visual Cue For starters, he takes pictures. Lots of pictures. Even of spaces that are already ingrained in his mind, like San Francisco, where much of the Flamel series takes place. “There’s a scene where one of the characters sits on a seat at the Hard Rock Cafe, just to the left of the door.
He went on to describe those memories in careful detail: the smell of the air, the sounds of the restaurant, the feel of the chair. Mind Mapping Related. Twitter analysis of air pollution in Beijing. One of the air pollution detection machine in Beijing (at the American Embassy) is connected to Twitter and tweet about the air quality in real time. By default the machine in Beijing output the 24hr summary PM2.5 air pollution information. What is PM2.5 is define here Next will be to compare the pollution level between different cities such as LA and Beijing. But it turns out the air quality data for California are not so easy to get programmatically. Here is the code I used to produce this analysis:Read more » To leave a comment for the author, please follow the link and comment on his blog: R Chronicle. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...
Creating Stunning Visualizations With Impress.js. Do you want to create beautiful slideshow presentations for the web using just HTML and CSS? That won’t be a problem with Impress.js, a powerful CSS3 transformation framework that lets you convert your HTML content into a slideshow with amazing effects. Impress.js is a jQuery plugin created on github by bartez which uses CSS3 functionality to create presentations. All the modern browsers will support the stunning visualizations created by Impress.js. Demo | Download Project File Downloading and Setup Impress.js Download Impress.js by visiting github. Once the download is completed extract the zip folder and you will find sample files used for the Impress.js demo. Basic HTML document with impress.js included Initializing Impress.js In order to use Impress.js you need to use syntax defined by the library. Now we are ready to explore the powerful features of Impress.js.
Lets Start Creating Slides Explaining Slide Creation I have created two DIV elements with ID start and slide2. Whats Next ? Emergent Futures Mapping with Futurescaper. Visualizing 2012 census estimates using CartoDB and Leaflet | Carl V. Lewis. Learning data visualization. Travis Kochel’s FF Chartwell. Newly launched – The Miso Project. Why does Data Visualization Matter? How data visualization turns scientists into storytellers. Wordle - Beautiful Word Clouds.
How to Make an Interactive Network Visualization. Fancy HTML5 Slides with knitr and pandoc. More on Horizon Charts. Hadley Wickham’s ggplot2 basics. C2: Clojure/ClojureScript data visualization - Clojure. Four Easy Visualization Mistakes to Avoid. List of Hand-Picked and Recommended Data Visualization Tools. Bio7 1.6 for Windows and Linux released! Gephi Toolkit Tutorial. Gaphi. Mobile User Interface (UI) Kits for Designers. Notch launches to creatively visualize data captured by consumer fitness devices. Quadrigram: New visual programming environment launches. It’s Time to Practice Decision Visualization. Data Visualization Techniques for Those Who Can’t Draw.
Visualisation Devices from Filip on the Behance Network. Geospatial Visualization | Where matters | Tech Trends 2012| Deloitte Consulting LLP. FF Chartwell: Make cool graphs by simply typing. Data Visualization: Clarity or Aesthetics? 38 visualization api. Useful scripts to plot charts in web pages. What we can learn about charts from The WSJ Guide to Information Graphics | Carl V. Lewis. World’s tallest tower – Burj Dubai. The Charts That Should Accompany All Discussions of Media Bias - James Fallows. The Design Choices You Make for Information: How to Create Great Data Visualizations. Storytelling: The Next Step for Visualization. Papers/2013/Kosara_Computer_2013.pdf. Seven dirty secrets of data visualisation. 28 Rich Data Visualization Tools. Making infographics using R and Inkscape.