This is sort of an early Friday post that follows up from my post on David Petraeus yesterday. Today’s comes from Hilary Sargent, once of the Boston Globe. It diagrams the network that ultimately resulted in the conviction I mentioned yesterday.
For President-elect Donald Trump’s campaign to run so heavily against Secretary Clinton for mishandling classified information, his potential choice for Secretary of State did worse. He was actually convicted of mishandling classified information.
Today’s post is the graduate work of Michael Barry and Brian Card of Worcester Polytechnic Institute. The two looked at the available public data of the Massachusetts Bay Transportation Authority (MBTA)—the T to those that know—to better understand the Boston area subway system. Here the subway system refers to the heavy rail lines, i.e. the Blue, Orange, and Red lines.
In short, the piece has a lot to look at that is worth looking at. This particular screenshot is an analysis of the stations across all times on average weekdays and weekends. You can see how in this particular selection, the size of the station markers pulse depending upon the time of day and the number of turnstile entries. Meanwhile the charts to the right show you the density through time of said entries and then compares the average number of turnstiles entries per day. Text beneath the system map to the left provides a short analysis of the data, highlighting work vs. home stations.
Credit for the piece goes to Michael Barry and Brian Card.
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).
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.
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.
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.
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.
In the interests of transparency and full disclosure, for my employer I design interactive web-based applications that display significant amounts of data on various countries and industries—along with other design things. So I am always curious to see how others handle similar types and quantities of data. This interactive application—I hesitate to call something like this an interactive infographic because of its scale and scope—comes from the Global Economic Dynamics project.
I commend the designers for opting not to use a map despite the nature of a dataset that focuses on countries. Especially in this application, where the full pattern of trade or migration would only be visible through multiple clicks to load maps of export/import markets of a particular country.
The user can add multiple countries, switch to a different dataset, change the year of the displayed data, currency, metrics, &c. There is quite a bit going on in this application and the controls are carefully placed in the margins of the piece.
And while I could probably write a lot more about this piece, I will end up the ability to share any insights made while using this application. Because what is the value of a kernel of knowledge if you cannot share it? Consequently, this piece offers a multitude of options. The usual social media options are present. You can also download a .png for use in a presentation, e.g. PowerPoint, or you can download the data. But fascinatingly, the application allows you to embed the piece into your own site.
Unfortunately, I cannot find any specific designers attached to the project. So credit goes to the Global Economic Dynamics project.
Of the acting and directing world over time. This interactive piece from the New York Times charts the networks between actors and directors. The networks on the right while examples and stories are located to the left. When you scroll to an example, the network to the right is highlighted in yellow. If you click a link, you are taken to the IMDb page for that particular film. A really nice piece.
Credit for the piece goes to Mike Bostock, Jennifer Daniel, Alicia DeSantis, and Nicolas Rapold.
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.
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.
Here’s an older, March graphic from the New York Times that looks at Alaska Airlines. This exemplifies what maps do well; it maps relevant data onto a map. Perhaps that reads silly, but too often people map data just because most things are tied to a geography; things that happen in the world happen somewhere, ergo everything could be mapped.
In this graphic, however, mapping the tight and Alaska-focused network with tendrils sneaking off-map to distant cities. The map supports the article that tells how after decades of focusing on Alaska, the airline has begun to expand to Midwestern cities in the US, cities in Mexico, and Hawaii.
I am not terribly keen on the stacked bar chart. It highlights the steady Alaska market over the decades at the cost of showing dynamism in those Midwestern, Mexican, and Hawaiian markets.
Credit for the piece goes to the New York Times Graphics Department.
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?
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.
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.
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.