For those who may not be aware, part of Africa’s largest country is holding a referendum on whether it should remain a part of Sudan or secede and become an independent state, Southern Sudan—though one wonders if they would not come up with a different name. About 2005 a peace agreement all-but-ended a decades-long civil war in Sudan and as that violence subsided the attention in Sudan shifted to Darfur. The terms for the peace deal included a referendum on independence for Southern Sudan, long since joined with the northern half of Sudan as part of a British colony in Africa to control the Nile River.
The two halves of the country were and are very much different and the BBC has used maps to highlight these differences. The problem is that there are oil fields along the proposed border and a disputed region, called Abeyi, has significant deposits of its own. And so the question of who controls the oil and thus the money and the power remains. That, however, is a story for another post. This is just to highlight the maps.
Overall, not bad. I find using the different hues for the different subject matters a smart idea. The shift forces the audience to focus on the change in meaning, which is very important if the shape of the coloured area is not otherwise changing. That said, the infant mortality choropleth uses too great a shift in hues between data bins when compared to the water, education, and food security maps. The oil field map, while not necessarily a data-heavy map in the same way as the choropleths, is a nice bookend to what started the series, i.e. the satellite view. These highlight the sheer environmental and, in a fashion, economic differences between the two regions of Sudan.
I find the ethnic groups map, however, the most difficult to fit into the series. Certainly from a subject matter it is the most important. However, the colours chosen to represent the various ethnic groups seem disparate. The southern Dinka, for example, are represented as an olive-green whereas the northern Beja are more of a bluish-green. I think a better solution would have been to keep the three main groups, i.e. the Arabs, Northern Sudan, and Southern Sudan, and then assign each a different hue, for the sake of an example say the three primary colours. Then within those groups, different tints for the various but related ethnic groups. This would highlight the various ethnic groups existing across Sudan, but show the geographic split between the two groups.
Overall, a good effort from the BBC to highlight the stark differences between Northern and Southern Sudan.
This piece comes from my coworker, Ben, who found the graphic in Scientific American. Broadly speaking the piece is looking at the obese and the overweight in the United States, comparing the numbers of both children and adults in 1980 to 2008. These numbers are supplemented by the risk of death posed to both men and women from a few different causes. (I know at least diabetes is linked to weight, but as to whether the others are linked I am unaware.)
I have a few quibbles with the piece; for in general I think educating the public about the health risks of obesity a worthy endeavour. From a more scientific-ish point of view, as I recall, BMI (body mass index) is not a particularly useful tool in determining obesity because it fails to differentiate people who are heavy with fat from those who are heavy with muscles. A strong and regular weight-lifter is not necessarily overweight, but simply has a lot of muscles. Does that make the weight-lifter less healthy than those with lots of body fat? Methinks not.
From the data side, I am curious to know why only the two years? It may very well be that they are the only two years for which relevant data exists. But I doubt that. 1980 compared to 2008 is interesting, but perhaps already well-known. What would perhaps be more interesting is whether over the past few years, the increasing attention paid to weight and other health issues has begun to affect the growth of the obesity problem—poor pun very much intended.
The accompanying text makes a point about the number of adult Americans being obese. Certainly the dots as a percentage of the population achieve that goal of showing percents—though I hasten to add that their arrangement around the body in the centre does very little to aid in comparing the adults of 1980 to 2000 let alone the children. And as to the children, the article points out that they are growing fastest. At this, however, I can only take the authors at their word for the graphic does nothing to visualise this statement. Perhaps they outgrew the adults—but then the adults were themselves at one point children, but that is another matter—but their growth could now be slowing as a recent turn of events. But since we only have two years, we cannot know for certain.
The risk of death by [type of death] is interesting. But running bar charts as more of a radial chart could become a bit confusing. Is there any reason the bars grow in width as they extend further out? Or was that part of an all-too-obvious play on the problem. After all, the growth in area could be significant; a simple line of constant stroke to a point along the radial distance markers would have sufficed. And then I would be particularly curious to know whether any of these types of death are related to obesity. Neither the article nor the graphic provide any clues besides whatever knowledge the viewer brings to the table. (Okay, I think I am done with the puns.) And if one happens across the article with almost no knowledge of what diseases or medical conditions are caused by obesity, how does the graphic tie into the cost of healthcare costs brought upon the country by obesity.
Overall, I think the graphic is well-intentioned. The public is becoming more accustomed to seeing data visualised. However, we need to make certain that we are communicating clearly by making datapoints easier to compare. (Looking at things across half of a circle is a bit tricky.) And then we need to make certain that the data we are visualising supports our statements. (Are children really the fastest growing? Over what span of time?) And then take the time to explain to the audience those things that may not be common knowledge. Does that mean dumb a piece down to the lowest common denominator of someone who has absolutely no knowledge? No. Design needs to elevate and educate its audience. Perhaps some of the finer details remain unexplained because of sheer complexity, but when amidst a host of details well-understood, that confounding bit may push an unsure viewer to do some additional research and educate him- or herself about the subject matter. And that, surely, is not a bad thing.
These are photographs from a small series published by CNET that focuses on a power grid control room. As one can imagine, managing the flow of electrical energy across somewhere the size of New England could be a bit…complicated. And so one can see from some kind of network map (perhaps?) on the main display. At the very least I can make no sense of it.
On the other hand, I only wonder what would happen if Homer were sitting behind a bank of those monitors?
The BBC has an article about the massiveness of Facebook—at least in the United States. They have taken the data and spent time to do a little bit of visualisation. It is worth a look; the design is not perfect but acceptable in a broad sense.
The Washington Post has released an in-depth article, or series of articles, about the intelligence community of the United States and its growth since 11 September 2001. There are several visualisations of data and relationships between government agencies and companies along with a video introduction and, well, a traditional written article or two.
Overall, the piece is quite interesting to look through—although I have not yet had the time to do just that. Some of the visualisations appear a bit thin. But, that may be just because I have not yet had time to play with them enough to draw out any particular insights.
What is nice, however, is again having visualisations supporting editorial content in such a fashion.
We all know about the BP oil spill in the Gulf of Mexico and so there is no need to rehash what has already been said. However, I do want to point out the continuing and evolving coverage from the New York Times. At the outset they located the spill on a map and began to add interactivity to the map in order to show change over time.
When I returned to the NYT for the latest—after admittedly more than a few days away—I discovered that an interactive supplemental to news articles had transformed into an interactive article in a sense. The story is broken into different chapters or components and each of these chapters uses graphics or photographs or videos to explain just what is going, what happened, and what the effects may be.
The site is worth checking out, though it shall take more than a few minutes to read and look through. But it evidences how the smart use of charts, graphics, and photos can be combined with well written prose to tell a great—or in this case perhaps tragic is more the word—story.
Today the New York Times published an in-depth examination of NYPD stops of individuals ‘based on a reasonable suspicion of a crime’. The item includes a lengthy article; a printed, full-page information graphic; and an online, interactive piece from which the printed piece appears to be derived. The print piece is credited to Ford Fessenden and Janet Roberts, the online piece to the same along with Matthew Bloch.
Each version of the information graphic centres upon a street map of the five boroughs. Data for the number of stops is graphed at the appropriate addresses, thus making a geographically-correct map appropriate for the type of data. What is interesting is that a decision was made to represent the number of stops by means of the area of circles presumably centred upon the address or the street—each police stop is encoded into the circle by an incremental edit to the circle’s radius. This is despite the fact that area is less than an ideal means of discerning comparisons between discrete datapoints. I am left to wonder if other means of representing the data could have been perhaps more effective. Perhaps if individual streets were coloured according to a carefully crafted distribution one could see a better examination of individual streets. For while absolute fidelity would be lost in grouping datapoints into bins, individual streets and intersections would become far more visible and, perhaps, accessible. Perhaps there are even other ways of representing the data that are not so readily apparent to me.
And while on the topic of street-specific data, an interesting point about these pieces is that the online piece displays the circles atop a desaturated Google map of the region whereas the print piece is atop a stripped-down outline of the five boroughs. Some of this may well be due to the difference between the screen and print resolutions. However, I find that the Google map is distracting for displaying too much in a nearly garish fashion. To the designers’ credit, they reduced much of those distracting elements by eliminating colour from the equation. However, and perhaps this is an issue of personal aesthetics, the map is still competing too much with the circles. Despite the reduction in quality on the newsprint, I prefer the print version of the map.
That all, of course, assumes that one is looking at the full picture of the city. The online version allows one to zoom into particular neighbourhoods and intersections. To some degree this alleviates the clutter of Google’s maps but for the loss of realising the larger message. From my perspective, the printed piece provides a more interesting view of the whole story, for the large map is clearer through the reduction of extraneous map data but the interesting neighbourhood stories are highlighted on the large map with the most interesting given a detailed review. And it is in this review that the specific features of street names, buildings, &c. are made available to the audience. Indeed, the detailed look at Brownsville, Brooklyn is not available in the same level of clear, concise depth as it is in the online version.
Another advantage of the print format is the ability to present the map in a larger context and integrate stories and supplemental charts in the white space carved out by the natural geography of the boroughs. Combined, these elements occupy all the space above the fold whereas in the browser windows I used at both work and at home, only the map and one story for the selected neighbourhood is immediately visible. Thus the print’s integration, albeit made at the expense of the online’s interactive map, makes for a more inviting initial experience.
The remainder of the print and online versions are largely the same with the exception of the detail about Brownsville—which, as aforenoted, is available as a subset of of the online map and is provided outright in the print version. The space of the print version allows for the charting elements to be laid out amongst two columns whereas the online version is a single, vertical column down the webpage. Between the two versions, the largest difference is colour. I would suspect this is due to the differences in fidelity between printing the charts and viewing the charts online. I think both colours work in their respective medium.
Of the remaining graphics, the most interesting is that which displays the breakdown of stops by age in comparison to the city’s population as broken down by age. The first interesting point is the omission of a vertical scale; I can only assume that the scales are identical in both positive and negative directions. I did, however, readily understand the chart. Some may not ‘get it’ as quickly as one is asked to add the city population as it heads in a typically ‘negative’ direction. However, that the entire piece is designed to invite one to explore the statistics in detail, I think creating charts that may require some to think just a few seconds more are perfectly acceptable.
When the information graphic is combined with the whole of the article, the New York Times has again pulled off an impressive feat of editorial design that combines adeptness at the use of the English language with video and photography—from the associated multimedia from the article—along with the here-critiqued information design. Such level of depth provides a well-rounded examination of the issue or subject at hand and better informs the audience by way of both anecdote and fact while photography brings the audience visually into the story.
This post’s image comes from my coworker Darrough, though I know not the original author of the piece. The graphic is a periodic table of swear words and so for those with sensitive ears—or perhaps eyes—I shall advise you to skip forthwith this post. Now, in general, there is little remarkable about the graphic. Many different subject matters have borrowed the motif to organise themselves.
There are a few things lacking that would make the graphic a touch more interesting; one would be some sort of rationale for as to why the author placed certain swear words into different groups. In the table for the chemical elements, the elements are arranged by their electron shells and number of protons, groups and periods. For example, the alkali metals are the first group and are among the most reactive chemical elements. Is there a link between the reactivity of lithium to that of saying cunt? Is there a link between the non-reactive elements in the noble gases, e.g. argon, and those swear words originating with the word tit? One might ordinarily assume that the first group are the most reaction-provoking swear words whereas the last group is the least reactive. However, I know people equally offended by both words.
Another interesting consideration is the colour of the piece. Broadly speaking, the colours resemble those typically seen in colouration of groups of similar elements. For example, the first few periods of Groups 14–16 share either a pink or violet-red depending upon where they fall along a diagonal axis. In the chemical element table, a three-way division of elements appears with the divisions delineating the non-metals from the metalloids from the metals. Is there a similar reasoning for the division in this chart? If so, the reason does not readily appear to me.
Another interesting note is that the ‘pissed up’ group replaces the lanthanides and actinides—which contain uranium and plutonium. However, the ordering by atomic number is incorrect and I would be curious in knowing if there is any particular reason for that decision.
One final consideration is that because I know not the origin of the piece, I cannot know the cultural background by the selection. For example, as an Anglophile American, I know well the use of bloody, twat, arse, and bollock among other words. However, most Americans would have other choice words to use in their place. Is this piece an attempt to classify perhaps British/English/Scottish swearing or is it an attempt to try and fit many English-language swear words into a single table? If the latter, I would be curious to see if there are any words of, say, Canadian, Australian, South African, or New Zealander origin that have been excluded.
All told, however, this piece is just downright entertaining and in all likelihood the author intended it to be as such. (Though I would be most curious to see an etymologically correct attempt at defining English swear words.) Aesthetically, the piece fits into the style of most old-fashioned textbook diagrams that I have seen in old textbooks.
So, all-in-all, I can sum this piece up in two words. Fucking brilliant.
The election has come and gone yet very little is resolved; the UK now has a hung parliament. Labour, the Tories, and the Lib Dems are now left to negotiate on the details of forming a coalition government, wherein two parties formally agree to cooperate in governing the country, or a minority government, wherein the Tories try to govern with the most seats but less than a majority. Or does Labour try to work with the Lib Dems and achieve something of a minority coalition government. The one certain thing about the election is that we now have loads of electoral data that wants to be visualised.
A few things at the top, as an American, despite my following of British politics, I am, well, an American. I am more familiar with the American system and so some of what may follow may be inaccurate. If at all, please do speak up. I should very much like to understand an electoral system that may now change entirely.
I wanted to point out a couple of sites real quick and some advantages and disadvantages thereof. Most of these were likely around before the election, however, I have been a tad busy with work and some other things to provide any commentary until now.
Auntie. The Beeb. The BBC. They have done a pretty good job at playing with four variables and the results. Are pie charts great? No. Not at all. However, they naturally limit us to 100% whereas bar charts displaying share are not necessarily as limiting—understanding that, yes, such things can be coded into the system.
Another interesting thing about the BBC’s electoral map is their cartographic decision to represent each constituency as a hexagon instead of overlaying the constituencies over a political map. This actually makes quite a lot of sense, however, if one considers that British constituencies are supposed to be rather equal in terms of population—not geographic area. And so while a traditional map will portray vast swathes of Tory blue and Lib Deb yellow, Labour counters in holding numerous visually insignificant constituencies in the inner cities of the UK.
Does the BBC need to represent each Commons seat as a square and arrange them to cross the majority line? Most likely not. However, it does keep with the idea of displaying each constituency as the boxes are placed next to the hexagons.
All in all, I think the BBC’s piece is quite effective. I do miss seeing the actual geography of the UK. But I understand how it is less useful in displaying the outcome of one’s playing with the electoral swing. Useful, but not necessarily needed, is the provision of several historical elections as comparisons to one’s playing.
The Guardian is next, in no particular order. Their swingometer is a bit less interesting than that of the BBC’s. Certainly in some senses it makes more sense, any bi-directional swing, while easier to grasp, ignore the complexities in having the Liberal Democrats as a viable third party and thus third axis. The circular swingometer attempts to rectify that. However, what the BBC does with their pie chart version is delve into the politics of the regional and fourth party candidates. For example, the Greens won a single constituency in southern England. In a hung parliament a single vote may be the difference between passing and defeating a bill. The BBC accounts for this while the Guardian does not.
What is particularly interesting about their calculator, however, is the ability to track individual seats and watch as one’s changes affect that particular constituency. As I play with the calculator, I can watch as Brighton Pavilion, where the Green party candidate won, changes from Labour to Conservative. However, nowhere in my exercises, have I managed to switch the seat to a fourth party candidate. The BBC solves this by not allowing one to select particular constituencies; one can only guess which seats they are looking at.
Also interesting about the Guardian’s version is their provision of different data displays. The default is a proportional representation, with each seat equating to a single square. However, they also allow one to view the results on a natural geographic level and strictly in terms of number of results and how close said results are to the magic number of 326. Additionally, the map allows you to filter for only that region of particular interest to you. If I only wish to look at, for example, the West Midlands, I can look at just the West Midlands without being distracted by additional regions. (The West Midlands provides another interesting example of being unable to factor in the role of fourth party players as Wyre Forest switched to the Conservatives, a result I cannot here duplicate.)
Overall, I really like how the Guardian provides different ways of viewing the data and the ability to track those changes to a particular constituency—even across the changes in data views. However, the Guardian is lacking at least in the ability to address the role of independents and regional parties. Perhaps this is do to a level of difficulty in predicting results at that level of granularity; something that is wholly understandable. However, that the BBC does just that is unfortunate for the rest of the Guardian’s piece because the rest of it is so nice. Even aesthetically, I find the Guardian’s to be appealing.
Next is the Sun. This, admittedly, is not so much a calculator but more a results map. And as such, it is effective in its simplicity. There is no messing about with swing or such—again probably because it is simply filling in constituencies by result. However, where the Sun’s piece fails is that to see any result, one needs to click a specific region. When selecting the UK, one can only see the outlines of the various regions of the UK. There appears to be no way of seeing UK-wide results.
Additionally, the data is presented strictly on a natural geography. This has the deficits as outlined above. And while the Guardian does present the results in such a fashion, it is not the only fashion in which data can be presented. Further, to see any results for a particular constituency, one must click all the way through the map before seeing data. None of this helps one access the actual data. And while one could say that the results are less important than showing the victor, one still needs to click into a specific region to see a victor thereby requiring a click whereas the other pieces provide results at an instant view.
Aesthetically, while both the BBC and Guardian favour a lighter, more open space the Sun’s piece feels trapped in a claustrophobic space surrounded by dark advertisements and flush against menus and heavy-handed navigation. All in all, I must confess that the Sun’s piece strikes me as an underwhelming piece that is less than wholly successful. It could have been made at least wholly successful if I needed not navigate into a particular region to mouseover a constituency.
The last piece I am going to look at is that from the Times. While there appears to be no way of playing with possible outcomes, the Times provides interesting ways of slicing the data in a more narrative structure. In terms of the map, the display suffers from being viewable only as the natural geography of the United Kingdom without being able to even toggle to a proportional view.
The additional data is displayed nicely in a side panel. I have to say that from an aesthetic standpoint, the Times’ mini site for the election results is my favourite. The black banner and main navigation sits well against the light colours used for the remainder of the piece. The serifed typeface for the numbers fits well with the newspaper feel and the black and serif combined works well to recall No. 10, Downing Street. A very nice touch and design decision.
As noted, the display fails in that only shows the data in a natural geographic sense. Now, the site overall provides links to news coverage of the event; these are accessible through a dropdown menu in the black banner. But, when clicked, these stories alter the map and highlight the particular constituencies in question. This approach provides a nice touch on straight data visualisation in linking the data to the editorial content of the newspaper. Which seats were taken or lost by independents? On a broad and filled-in map of the United Kingdom, I may not be able to know. But by clicking on that story, the map filters appropriately and I can click each constituency and get the story.
And so while the data visualisation is not necessarily on par with that of the BBC and the Guardian, the tie-in with the editorial emphasis—in my mind—makes up for the lack of detail in data visualisation. Data is wonderful, however, the narrative is what helps us make sense of what is otherwise just numbers and figures.
That editorial link and the subtle design decision to link the minisite to the sort of 10 Downing Street aesthetic makes the Times version my favourite and the best designed experience. Besides the lack of detail in the data visualisation aspect, the only other drawback is perhaps the load time for each change in display.
Sunday night, the US House of Representatives passed a bill that you may not have heard about. The bill goes towards addressing universal healthcare coverage for US citizens. As I said, you may not have heard of it…
The bill was passed largely along partisan lines with about 30 conservative Democrats joining the conservative Republicans in voting against the legislation. This morning, the GOP unveiled a new website called Fire Nancy Pelosi that seeks to capitalise on the anger against—and perhaps even hatred for—providing healthcare to all Americans by collecting donations to capture 40 House seats in the forthcoming mid-term election. The website uses a map of the United States to show “who wants to fire Nancy Pelosi most”. According to the map, states are ranked by donation totals.
Without attempting to talk about the politics, the problem with the map is that it is attempting to equate the state’s supposed anger against Speaker Pelosi and healthcare for Americans with the sum of donations per state. As of the time when I captured my screen, the interesting visual is that many traditional red states are blue and purple, e.g. West Virginia and North Dakota, whereas many traditional blue states are purple and red, e.g.California and Illinois. The problem however, is that one state may be able to provide more donations than another.
The most obvious difference is in terms of population. Without access to the data I cannot state facts about exact donation totals. However, the map does break down the states into deciles and so I have quickly pulled from Wikipedia some rankings on population (from 2009) and income per capita (from 2000). What is quite clear is that the states donating the least are among the states with the smallest population. Six of the Bottom 10 donating states are from the ten smallest states. Conversely, eight of the GOP’s Top 10 Donating States are among the ten largest states in the country by population.
If you compare income per capita, I find the message a bit more confusing, but still quite interesting. I do not claim to be a statistician and an analytic review of the numbers is a bit outside my area of expertise. However, for the Top 10, not a single state is ranked 40 or below in terms of income. And only one state is ranked in the 30s. One Top 10 Donating State is also found in the top ten by income per capita and a total of five of the Top 10 Donating States are in the top twenty by income per capita. Among the Bottom 10 Donating States we also find five states from the top twenty states by income per capita. However, we also find four of the last twenty states by income.
What strikes me is that the Top 10 Donating States have a larger population base from which to draw donations and, loosely, earn more per capita and thus, perhaps presumptuously, have more disposable income for contributions. The Bottom 10 Donating States have among the smallest populations and while some are seemingly quite wealthy, a significant number are among the least well-off in terms of income per capita.
And none of this critique discusses how the Top 10 Donating States use a bright and vivid red to draw attention whereas the Bottom 10 use a fairly dark and almost dull blue to push forward the bright red.