Clustering

22 posts
Digital revolution in China: two visual takes

The following map accompanied an article in the Economist about China's drive to create a "digital silkroad," roughly defined as making a Silicon Valley.  The two variables plotted are the wealth of each province (measured by GDP per capita) and the level of Internet penetration. The designer made the following choices: GDP per capita is presented with less precision than Internet penetration. The former is grouped into five large categories...

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Discoloring the chart to re-discover its plot

Today's chart comes from Pew Research Center, and the big question is why the colors? The data show the age distributions of people who believe different religions. It's a stacked bar chart, in which the ages have been grouped into the young (under 15), the old (60 plus) and everyone else. Five religions are afforded their own bars while "folk" religions are grouped as one, and so have "other" religions....

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Governor of Maine wants a raise

In a Trifecta checkup, this map scores low on the Q corner: what is its purpose? What have readers learned about the salaries of state governors after looking at the map? (Link to original) The most obvious "insights" include: There are more Republican governors than Democratic governors Most Democratic governors are from the coastal states There is exactly one Independent governor Small states on the Eastern seaboard is messing up...

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A gem among the snowpack of Olympics data journalism

It's not often I come across a piece of data journalism that pleases me so much. Here it is, the "Happy 700" article by Washington Post is amazing.   When data journalism and dataviz are done right, the designers have made good decisions. Here are some of the key elements that make this article work: (1) Unique The topic is timely but timeliness heightens both the demand and supply of...

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Excellent visualization of gun violence in American cities

I like the Guardian's feature (undated) on gun violence in American cities a lot. The following graphic illustrates the situation in Baltimore. The designer starts by placing where the gun homicides occured in 2015. Then, it leads readers through an exploration of the key factors that might be associated with the spatial distribution of those homicides. The blue color measures poverty levels. There is a moderate correlation between high numbers...

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The visual should be easier to read than your data

A reader sent this tip in some time ago and I lost track of who he/she is. This graphic looks deceptively complex. What's complex is not the underlying analysis. The design is complex and so the decoding is complex. The question of the graphic is a central concern of anyone who's retired: how long will one's savings last? There are two related metrics to describe the durability of the stash,...

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Getting into the heads of the chart designer

When I look at this chart (from Business Insider), I try to understand the decisions made by its designer - which things are important to her/him, and which things are less important. The chart shows average salaries in the top 2 percent of income earners. The data are split by gender and by state. First, I notice that the designer chooses to use the map form. This decision suggests that...

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Making people jump over hoops

Take a look at the following chart, and guess what message the designer wants to convey: This chart accompanied an article in the Wall Street Journal about Wells Fargo losing brokers due to the fake account scandal, and using bonuses to lure them back. Like you, my first response to the chart was that little has changed from 2015 to 2017. It is a bit mysterious the intention of the...

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Making the world a richer place #onelesspie #PiDay

Xan Gregg and I have been at it for a number of years. To celebrate Pi Day today, I am ridding the world of one pie chart. Here is a pie chart that is found on Wikipedia: Here is the revised chart: It's been designed to highlight certain points of interest. I find the data quite educational. These are some other insights that are not clear from the revised chart:...

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Charting all the Pokemon

Pokemon is everywhere these days. I think it’s just something the world really needs right now. I know very little about the universe, but I do like it when people analyze fictional worlds and characters. Joshua Kunst grabbed a data dump about all the Pokemon (seriously, I don’t even know if I’m referring to them/it/thing correctly) and clustered them algorithmically. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to be specific....

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