visual data
Follow
Find tag "graphs"
87.5K views | +300 today
visual data
learning, conceptualizing + communicating data with infographics, visualizations, etc...
Curated by Lauren Moss
Your new post is loading...
Your new post is loading...
Scooped by Lauren Moss
Scoop.it!

The 11 Most Fascinating Charts From Mary Meeker's Epic Slideshow of Internet Trends

The 11 Most Fascinating Charts From Mary Meeker's Epic Slideshow of Internet Trends | visual data | Scoop.it
Facebook is the only major social network in decline. Saudis share more online than anyone. You check your phone 150 times a day. And more.


Every year, Mary Meeker and the team from KPCB unleash upon the world the mother of all slideshows, which aims to sum up The State of the Internet. This year's behemoth was born this morning, weighing in at 117 pages. Here are the 12 most interesting pages.

Check out the full report here.

more...
No comment yet.
Rescooped by Lauren Moss from Polymath Online
Scoop.it!

Graphing the history of philosophy

Graphing the history of philosophy | visual data | Scoop.it

Each philosopher is a node in the network and the lines between them (or edges in the terminology of graph theory) represents lines of influence. The node and text are sized according to the number of connections. The algorithm that visualises the graph also tends to put the better connected nodes in the centre of the diagram so we the most influential philosophers, in large text, clustered in the centre. It all seems about right with the major figures in the western philosophical tradition taking the centre stage. (I need to also add the direction of influence with a arrow head – something I’ve not got round to yet.)

A shortcoming however is that this evaluation only takes into account direct lines of influence. Indirect influence via another person in the network does not enter into it. This probably explains why Descartes is smaller than you’d think.

It gets more interesting when we use Gephi to identify communities (or modules) within the network. Roughly speaking it identifies groups of nodes which are more connected with each other than with nodes in other groups. Philosophy has many traditions and schools so a good test would be whether the algorithm picks them out...


Via Martin Daumiller
more...
No comment yet.
Scooped by Lauren Moss
Scoop.it!

A Visual Thesaurus of the English Language

A Visual Thesaurus of the English Language | visual data | Scoop.it

One of the very first examples of visualization that succeeds in merging beauty with function is Visual Thesaurus, a subscription-based online thesaurus and dictionary that shows the relationships between words through a beautiful interactive map.

more...
No comment yet.
Scooped by Lauren Moss
Scoop.it!

Top 3 Common Mistakes of Infographic Designers

Top 3 Common Mistakes of Infographic Designers | visual data | Scoop.it

An infographic is a very useful tool to present data to people in an easier manner using visuals. It combines facts via words and accompanied logically by graphic designs into one image.

In this article, we will take a deeper look into what infographic really is and what it should be, and of course the top 3 common mistakes that designers commit when they create those...

more...
No comment yet.
Scooped by Lauren Moss
Scoop.it!

d3.js ~ Examples of Visualization Types

d3.js ~ Examples of Visualization Types | visual data | Scoop.it

D3 is not traditional visualization framework. Instead of a system with all the features one may ever need, D3 solves the crux of the problem: efficient manipulation of data-based documents. This gives flexibility, exposing the full capabilities of underlying technologies such as CSS3, HTML5 & SVG.

With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. And, for those common needs, D3’s functional style allows code reuse through a diverse collection of optional modules.

more...
No comment yet.
Scooped by Lauren Moss
Scoop.it!

Infovis, infographics, and data visualization: Statistical Modeling, Causal Inference, and Social Science

Infovis, infographics, and data visualization: Statistical Modeling, Causal Inference, and Social Science | visual data | Scoop.it

My first goal is to get statisticians and social science researchers to think more about their goals in displaying numerical information. It would be great if infovis could inspire and empower researchers to better visualize their data, models, and inferences.

My second goal is for graphics designers and creators of information visualization tools and infographics to become aware of a statistical perspective in which a graph can not only be evocative of data but can also convey quantitative comparisons. Appreciating new tools is fine, but I think infovis could also benefit from focused criticism and improvement, which might start with refections on the goals of any graph.

My third, modest, goal is for statisticians and graphics designers alike to consider the virtues of multiple displays: maybe an infographic to grab the reader’s attention, followed up by a more conventional dotplot or lineplot to display as much of the data as possible, and maybe then an unusual and innovative plot that might be hard to read but might inspire some out-of-the-box thinking.

One way to get the best of both worlds is to recognize the limitations of our separate approaches. On the web, there’s plenty of space for multiple visualizations of the same data...

more...
No comment yet.