rud.is

The Fund For Peace (FFP) and Foreign Policy jointly released the 2012 version of the “failed states index” (FSI). From the FFP site, the FSI:

…focuses on the indicators of risk and is based on thousands of articles and reports that are processed by our CAST Software from electronically available sources.

I read it every year (mostly due to being an ardent reader of Foreign Policy magazine) and find the rankings, methodology & insights quite intriguing. With my recent work on slopegraphs, I thought this would be a good data set to play with to determine what – if any – features were necessary to support rank order (and to provide some impetus to finally refactor the code to support multi-column slopegraphs…more on that later).

However, I was not looking forward to transcribing the data from the Flash visualization on the Foreign Policy web site. There are HTML grids on the FFP site but I really just wanted the overall rankings (i.e. no sub-indices) and noticed this interesting scrollable mini-grid on one of the FFP FSI pages:

Thankfully[?] it’s an IFRAME and I was able to pull 2010, 2011 & 2012 data in a very usable format by manipulating this URL: http://www.fundforpeace.org/global/tables/fsiindex2010_sml.htm.

After some quick transformations, I had two CSV files for a 2010-2012 comparison and a 2011-2012 comparison.

(Before continuing, I feel the need to point out that the data, methodology, etc is 100% Copyright © 2012 The Fund for Peace as they overtly point out many times on their site.)

When I threw the data into the slopegraph tool, it was immediately obvious that I was missing something important: the ability to specify sort order for the data. For most slopegraphs, the code works well since our brains expect the larger values on the top. For a rank-order slopegraph, that sort order (for the most part) should be ascending vs descending to best represent changes in rank position. It does feel odd that being “#1” in the FSI actually means you’re really a loser, but I didn’t make the rules for their index.

So, PySlopegraph now handles two column rank order slopegraphs and, as you’ll see in part two, also handles multi-column slopegraphs (but that bit needs some work). The code will be up on github in a couple days as I’ve also got some half-finished support for Processing.js and Paper.js that I want to finish before another push. If anyone needs it sooner, just @ or DM me.

Now, For The Data

The “Top 25” (that sounds way too positive for what it really means) slopegraph is the easiest to read (as it’s the smallest). It is also where Foreign Policy & FFP focus some dataviz effort as well (though they do have visualizations for all the data). Here’s the slopegraph showing the rank order chance from 2010 to 2012:

The full slopegraphs are tall slopegraphs (I’ve been prototyping some ways to make tall ones more useful, but that’s nowhere near ready for public consumption). You may just want to grab the two PDFs and look there vs in this post:

• 2010-2012
• 2011-2012

While it requires scrolling, the changes in rank are immediately noticeable as is the fact that the the FFP folk allow for ties that leave “holes” in the table. I think you really get a feel for which countries are stable, improving and declining very quickly with the slopegraph version, but I’d like to hear your thoughts if you have an opine you’d like to share.

Stay tuned for part two!

There were enough eye-catching glitches in the experimental javascript support and the ugly large-number display in the spam example post that I felt compelled to make a couple formatting tweaks in the code. I also didn’t have time to do “real” work on the codebase this weekend.

So, along with spacing adjustments, there’s now an “add_commas” non-mandatory option that will toss commas in large numbers so they’re easy to read. Here’s an example of the new output (both the Raphaël display and commas):

As usual, it’s up on github

One of the last items for the 1.0 release is support for multiple columns of data. That will require some additional refactoring, so I’ve been procrastinating by exploring the recent “fudging” discovery. Despite claims to the contrary on other sites, there are more folks playing with slopegraphs than you might imagine. The inspiration for today’s installment comes from Jon Custer (@stuffisthings). He has a two part “Telling Stories with Data” series that does some exploration of export data with slopegraphs. In his “Slopegraph Strikes Back” post, Jon does a spiffy job discussing data visualization fundamentals and walks the reader through his re-design of a chart on commodities ranking, including a commentary on an aspect of slopegraphs that I’ve been noticing as I’ve been doing my exploring: the ‘scale’ problem (which I began to point out in the aforementioned “fudging” post).

The data set Jon is working with allows for a great exploration as to what works best when trying to convey a message with slopegraphs. I took the values from one of the tables he extracted:

and made a “raw” slopegraph from them (focusing on the “top 10”). The graphic won’t even come close to fitting in this post but you can grab the PDF of it and see how scale is the primary enemy of slopegraphs. It does show how gold and precious metal ores have skyrocketed from 1998 to 2007, but it’s hardly an engaging and easy to read visualization (unless you really like using your scroll wheel).

Jon grok’d this point, too, and decided to focus on the power law ranking and use the slopegraph to present the rate of change of each commodity:

While he didn’t “pull a Tufte” and just include values without caveat (see left & right 90° side labels), I still believe that there needs to be either increased annotation or the inclusion of base tabular data. Using my PySlopegraph code (forgot to mention the name change), I worked up a version of Jon’s visualization that I believe provides a clean, honest view of the data (click for larger view):

Because the chart is still based on the percentages that are fairly precise:

I finally added an option to the PySlopegraph configuration file for rounding (NOTE: rounding != true binning). If you add the “round_precision” option with a value that supports Python’s round function’s little-known second parameter (arbitrary positional rounding), you can have the values round to decimal or tens/hundreds/etc places which will help with scaling issues, but will also group items (in ways that you may not have originally intended). For this chart, if we use a value of “1” (first decimal rounding precision…use negative values for rounding on the whole integer side of the decimal) it’s still unreadable due to the scale it imposes by that precision, so I ended up using the nearest whole integer rounding option (value of “0”) and also included the table of actual values, along with annotating the “rate of change” nature of the slopes.

This (again) defeats the “no wasted ink (pixels?)” component of Tufte’s original creation, but I believe it’s necessary for some types of slopegraphs to ensure the chart can stand on it’s own. I’m definitely becoming more convinced that many slopegraphs are more suited for an interactive visualization where you can encode more information in rollovers/popups/etc plus allow for switching of view from percentage, power-law ranking or raw numeric comparison.

For those interested in playing with this particular data set, it’ll be included in the next github code push, which will also include the rounding feature.