As the title says, baseball is almost back. Red Sox spring training games begin as the Red Sox take on Northeastern today. The off-season is perhaps the hardest part of the year for a fan, because unless you take super interest in trades, there is no baseball. But what about on Twitter? Well, today’s piece is an article from Fangraphs that looks at team-by-team off-season Twitter use.
Twitter engagement
Personally, I am not really a fan of the graphic. As a static image, it does not allow me to easily compare the different retweets or favourites. But, in the aggregate, you can see that the Seattle Mariners are perhaps the most active Twitter account.
Today’s post comes via a co-worker. LinkedIn’s R&D lab published a tool to map your LinkedIn connections. You login to your account and then receive a social network of map. Mine, seen below, clearly shows three different and generally not inter-connected networks. The orange represents my current employer; the blue is my university network; the green and pink are high school and my employer while in university (they were in the same town).
My LinkedIn network map
To be fair, I’m not a frequent user of LinkedIn. So for those of who you use it more regularly to make connections, contacts, and acquaintances will find yourselves with more complex networks.
The thing with the NSA spy scandal is not that it collects data on Americans. But it collects data on the Americans that the Americans that the Americans know. Three degrees of separation can actually be quite a few people whose privacy is violated in the name of security. The Guardian has an excellent piece that shows you as in you yourself—if you grant access to your Facebook profile—how many people could be investigated because you know them.
My personal three hops
Well, I hate to tell you, Norway. But apparently, with me you are far from safe. Or at least a Norway-sized chunk of the American population. More seriously, this is a great piece that personalises an abstract sort of concept. Not just through the use of your own personal data, but by using (potentially) familiar items to contextualise scale. How many people is 190? Almost two Concordes worth. How many is 4,779,123 people? More than the population of Norway. You know, a country. Well done, Guardian.
Credit for the piece goes to the Guardian’s US Interactive Team.
I am not terribly familiar with local politics outside of my local areas. So the background and details of this piece escape me. However, this interactive graphic and story from the Los Angeles Times does a really great job of leading the reader through the story.
First, the piece starts with a general overview or flowchart of the network of connections. Mouseovers do a fine job of highlighting and filtering for the appropriate piece. For example, a person shows the entities to which he is connected whereas the entities show the people to which it is connected.
The flow diagram of connections
Secondly, the piece then goes in detail about the different connections. The example screenshot below shows how each story is highlighted by a red dot as the user scrolls down the page. When that story is highlighted, the network diagram to the left changes, either replacing the contacts or highlighting the contacts specifically noted in the story.
Example of the connections in this story
As I said at the outset, this is a very nice piece that step-by-step shows and explains how all the connections work while filtering out the momentarily irrelevant data. Very well done.
This network diagram from the New York Times looks at a community of doctors with respect to a prescription oncology drug. Colour is used to denote types of doctors while size denotes the volume of prescriptions for any oncology drug. Admittedly, I am not keen of the bubble effect placed on the circles. Those effects and the heavy black outlines for the circles distract a bit, but not excessively so.
Network diagram of doctors
What really makes this graphic, as is making so many of the Times’ graphics, is the annotation and explanation of the presented data. The user can readily see how some doctors are connected, but understanding the shapes and patterns of those connections is not as clear. But then the Times furthers that by explaining how the marketers of this oncology drug would use this data.
Credit for this piece goes to the New York Times graphics team.
If you look at senators who voted with other senators at least 50% of the time and at least 75% of the time, and compare those numbers to numbers over a decade ago, you can see there is a lot less bipartisanship in 2013.
Compare 2013…
75 percent shared votes in 201350 percent shared votes in 2013
to 2001.
75 percent shared votes in 200150 percent shared votes in 2001
Earlier this week, Wolfram Alpha released some findings from its analytics project on Facebook. While the results offer quite a bit to digest, the use of some data visualisation makes it a little bit easier. And a lot more interesting.
The results offer quite a bit of detail on interests, relationship statuses, geographic locations, and ages. Below is just one of the small multiple sets, this one looks at the number of friends of different ages for people of different ages. Basically, how many young or old people are friends of young people? Friends of old people?
Friends of Friends for the Ages
But I was most interested in the analysis of social networks. The mosaic below is indicative of the sheer size of the survey, but also begins to hint at the variance in the social structures of the data donors.
Just Some of the Networks
While these views are all neat, where it begins to get really interesting is Wolfram Alpha’s work on classifying the different types of social networks. By aggregating and averaging out clusters, simple forms begin to emerge. And after those forms emerged, they were quantified and the results are a simple bar chart showing the distribution of the different types of networks.
Simplified Cluster Distribution
Overall, some very interesting work. But one might naturally wonder how their own networks are structured. Or just be curious to look at the data visualisation of their own Facebook profile. Or maybe only some of us would. Fortunately, you still can link your account to a Wolfram Alpha account (you have to pay for advanced features, however) and get a report. Below is the result of my network, for those who know me semi-well I have labelled the different clusters to show just how the clustering works.
China is a big country, both geographically and demographically. It can also be rather opaque and difficult for an outsider to understand. So this recent work from Reuters is amazing because it makes China a bit more transparent while illustrating just how the political system structures power and personnel appointments.
The table of contents/introduction
Truthfully, there is more content there than ought to be consolidated into a single blog post here. Briefly, the project was some 18 months in development and hits upon three key areas: Social Power, Institutional Power, and Career Comparisons. Two other sections, China 101 and Featured Stories, offer additional material to help the user understand China’s past and what is going on in the present.
Social Power with Xi Jinping's network shown
Social Power examines, primarily through the use of network diagrams, the social dynamics of the upper echelons of the Chinese leadership. Previous generations of Chinese political leaders saw power confined into the hands of a few, e.g. Mao Tse-tung, but in recent years the Chinese Communist Power has decentralised that power into several individuals. Many of those individuals have friendships, marriages, and business relationships that have advanced them and kept them in power. The interactivity allows the user to dive deep into these relationships. And should things becoming confusing, here and throughout the app, there are links to biographies, definitions, and guides to explain what is before the user.
Institutional Power
Institutional Power roughly compares to a look at the American system of checks and balances. The responsibility of governing China falls to three “branches”: the Communist Party, the Chinese government, and the People’s Liberation Army (under which the navy and air force fall, e.g. the People’s Liberation Army Navy). This section of the app lists who belongs to each post or group and how that post or group falls into the broader structure of the Party, Government, or PLA.
Career Comparison with the guide open
The Career Comparison shows the different—but not really—tracks taken by the leaders of China. The user can compare individuals both present and past, along with potential future players, to see their route to power. China’s political system, because of its arguably undemocratic nature, is different from that of the United States. The path to power is longer and more established in China, as this section clearly shows.
As aforementioned the app was designed over 18 months and was optimised for the iPad 2+ and modern browsers (especially Chrome and Safari). All in all, a stellar piece of work. Design and development credits go to Fathom Interactive Design. The credits listed in the About section are as follows:
EDITOR+PROJECT LEADER: Irene Jay Liu
PRODUCTION HEADS: Yolanda Ma, Malik Yusuf
LEAD WRITER: Chris Ip
COPY EDITOR: John Newland
DESIGN+DEV: Fathom Information Design
