Last week Philadelphia became the first large US city to introduce a soda tax. (Berkeley introduced one a few years ago, but is 1/10 the size of Philly.) The Guardian has a really nice write-up on how the tax was sold not on health benefits, but of civic benefits to the education system. But the article made me wonder if somebody had published a map looking at obesity in Philadelphia. Turns out Philadelphia Magazine published an article with just such a map from another source, RTI International. (You can find the full interactive map here.)
The map has three views, one of which allows you to see areas of statistically significant clustering. North and West Philly had some bright red clusters, whereas the western suburbs, in particular along the Main Line have some very cold blues.
Well the Democratic DC primaries were last Tuesday and Hillary Clinton won. So now we start looking ahead towards the July conventions and then the November elections. Consequently, if a day is an eternity in politics we have many lifespans to witness before November. But that does not mean we cannot start playing around with electoral college scenarios.
The Wall Street Journal has a nice scenario prediction page that leads with the 2012 results map, in both traditional map and cartogram form. You can play god and flip the various states to either red or blue. But from the interaction side the designers did something really interesting. Flipping a state requires you to click and hold the state. But the speed with which it then flips is not equal for all states. Instead, the length of hold time depends upon the state’s likelihood to be a flippable state, based on the state’s partisan voter index. For example, if you try and flip Kansas, you will have to wait awhile to see the state turn blue. But try and flip North Carolina and the flip is near instantaneous.
While the geographic component remains on the right, the left-hand column features either text, or as in this other screenshot, smaller charts that illustrate the points more specifically.
Taken all together, the piece does a really nice job of presenting users with a tool to make predictions of their own. The different sections with concepts and analysis guide the user to see what scenarios fall within the realm of reason. But, what takes the cake is that flipping interaction. Using a delay to represent the likelihood of a flip is brilliant.
Credit for the piece goes to Aaron Zitner, Randy Yeip, Julia Wolfe, Chris Canipe, Jessia Ma, and Renée Rigdon.
Funny story, a virus hit my workplace this week. And it basically cost us four days of work because nobody could actually access their work files. That made me remember this recent piece from xkcd, which is so very apropos at the end of this week.
Monday I examined a chart from the BBC that in my mind needlessly added confusing visual components to what could have been a straight table. So here we take a look at some other options that could have been used to tell the same story. The first is the straight forward table approach. Here I emphasised the important number, that of those killed. I opted to de-emphasise the years and the injured in the table. Also, since the bulk of my audience is from the United States, I used the two-letter states codes.
But let us presume we want a graphic because everyone wants everything to be visual and graphic. Here are two different options. The first takes the table/graphic from the BBC and converts it into a straight stacked bar chart, again with emphasis on the dead. I consolidated the list into a single column so one need not split their reading across both the horizontal and vertical.
And then if you examine the dates, one can find an interesting component of the data. Of the top-eight shootings, all but two occurred within the last ten years. So the second version takes the graphic component of the stacked bars from the first and places them on a timeline.
For those that wonder about the additional effort needed to create three different options from one data set, I limited myself to an hour’s worth of time. A little bit of thought after examining the data set can save a lot of time when trying to design the data display.
Yesterday I opined about how simple tables can convey meaningful information without the aid of unnecessary chart elements. And while we will get back to that post, I did want to take a moment to share an older piece from the New York Times I recalled and that has been updated since Orlando.
The piece uses a table to compare the gun homicide rates for various countries and compares it to other causes of death. Being killed by a gun in the Netherlands is as likely as dying by accidental gas poisoning in the United States. It puts the absurdly high gun homicide rates in the United States in a new light.
Credit for the piece goes to Kevin Quealy and Margot Sanger-Katz.
I will be trying to do a longer piece on the data visualisations surrounding the shootings in Orlando later this week. But for starters, a simple point through this piece from the BBC—not that they are the only culprits of this. Not all data-driven stories need visualisations. Sometimes a nicely typeset table will do the job better and faster.
An actual table with typographic emphasis on the tables would have been better and clearer than this. Or with a little more time and effort—not that those always exist in a journalism organisation—something more appropriate to the type of data could have been designed.
Credit for the piece goes the BBC graphics department.
Well, the election battleground has been set: Trump vs. Clinton. (Yes, I know the District of Columbia has yet to vote.) For those unhappy with the choices presented, the question of “what about a third party candidate?” arises. (Yes, I know there is both a Libertarian Party and Green Party already.) Months ago, FiveThirtyEight looked at where Michael Bloomberg fell as a third party candidate, a run he briefly considered. Turns out he too moved out of the middle and into one of the four corners of the board.
The night after the California primary—or as an East Coaster should say, the night of the New Jersey primary—we take a look at how US presidents often experience a counterbalancing political force in state, gubernatorial, House, and Senate races. The content comes from the Washington Post and it makes use of nicely annotated graphics, including the screenshot below.
What I enjoy about the piece, however, is how it responds to a narrower browser, like one might see on a mobile phone. The screenshot to the right shows how the data visualisation changes. You can see how some of the annotations disappear, like the note about Nixon’s support growing.
The same adaptation to the display occurs for the other graphics throughout the piece, with axes and orientations changing to take advantage of the more vertical orientation.
I also think it is worth pointing out that the more illustrative ornamentation of the piece, i.e. the presidential illustrations, drop off completely. I could have lived without them as they do not contribute directly to the data story. I also think the white lines on the charts above could be removed to make the narrower margins more visible on the charts.
Today marks the end of primary season for the US presidential election. By all accounts, at night’s end Hillary Clinton will be the Democratic nominee, but Bernie Sanders, while unlikely to win, could make California interesting tonight. And then there is Donald Trump. He is the Republican Party’s presumptive nominee and man, can that guy tweet.
Thursday he retweeted a set of small multiple charts arguing that President Obama’s legacy is an absolute disaster.
Friday the Washington Post went through all nine points and fact-checked the charts, this being the refutation of the Food Stamps chart.
Well today has arrived and it is finally Friday. So if you are a Pennsylvanian like me, according to research by Estately (hat tip to a good friend and regular reader), the question I am likely asking is “When is X-Files?”. What did your state enquire of the Google?
I mean I liked the new series. Even if just for the rush of nostalgia.