At first glance, I thought I was looking at an overhead view of the North (or South) Pole, which I think actually could be an interesting way to drop a variation of this data on a map. If each of the lines represented a year, you could see how much the ice cap was receding over the years. Although, I don't know how many feet/miles that would represent and if the difference would be noticeable on an actual map, but if so - much more interesting/valuable.

Sticking to the data actually used/represented by the spider chart, any of your re-renderings are vast improvements!

Of course a regular line plot is much, much better. I would choose to plot only April and September, the months with largest and smallest ice volume. The fluctuation within a year is not as relevant, and it seems quite consistent from year to year.

One criticism: why is there no units on your vertical axis? That's graphing 101! ;)

It seems also you mixed up the months when grouping them by season. A minor detail, but could cause confusion if someone reposts your graphs.

Cris: For season, I just did months 1-3 = Spring, 4-6 = Summer, etc. Can you be more specific? What is being mixed up?
Speaking of which, the raw data came with days numbered from 1 to 365. Does anyone know how leap years are handled?

Would you recommend using a seasonally adjusted seriess or no? They're not terribly complicated to create; do they add too much of a sophisticated statistical feel that turns off more casual readers (in the same way that some people object to seasonally adjusted employment numbers)?

I've seen the multi series style used many a time, and it always seems cluttered to me. And while I'm a fan of boxplots, they don't reveal the underlying pattern within the year - all the lowest points in the second boxplot chart correspond to summer and all the highest points to winter, but in that single display, that gets lost.

Adam: In a way, the charts above perform a similar function to seasonal adjustment. Applying a formula makes it more precise. I totally support seasonal adjustment (I have a whole chapter on employment statistics in Numbersense!) The objection to seasonal adjustment stems from not understanding it.

As for boxplot versus line chart, Andrew Gelman if he sees this will say have both and make it clickable between the two.

In this dataset, it's clear to me the signal is in the year-to-year changes, and the seaonal month to month pattern is just noise. I like to keep readers focused on the key message. If you insist, I'd create a second chart that has the average month to month seasonality to convey that information (average the years so as to focus the second chart on seasonality).

Posting this comment from Robert Simmon who had trouble with the commenting system:

Andy:
Mapping Arctic Sea Ice over time does't work to show the trend because the shape of the ice cap changes, as well as the size: http://earthobservatory.nasa.gov/Features/WorldOfChange/sea_ice.php

In addition to a seasonal cycle plot the Earth Observatory (the NASA site I design for) has been comparing the most recent few years' data with the long-term mean plus standard deviations:
http://earthobservatory.nasa.gov/IOTD/view.php?id=82094

This gives both a sense of the normal seasonal changes, and how unusual the last few years were.

Kaiser:
Typically leap years run from 1 to 366 in a day of year system. So March 1 is usually 060, but it's 061 on leap years. However, the sea ice extent data from NSIDC—which should be the official data—uses YYYY MM DD i.e.:

Year, Month, Day, Extent, Missing, Source Data
YYYY, MM, DD, 10^6 sq km, 10^6 sq km
1978, 10, 26, 10.19591, 0.00000
1978, 10, 28, 10.34363, 0.00000
1978, 10, 30, 10.46621, 0.00000
1978, 11, 01, 10.65538, 0.00000,

Posting this comment for Xan Gregg who also had trouble with the commenting system:

Very nice. I think radar charts are worse than most realize. For instance, a dip in a cartesian chart can turn into a straight line in a radar chart.

As I commented on Alberto's post that you linked to, the paper Graphical Tests for Power Comparison of Competing Designs (http://users.soe.ucsc.edu/~pang/visweek/2012/infovis/papers/hofmann.pdf) by Heike Hofmann et al. may be of interest. One of their test cases shows that circular charts underperform even when the data is naturally circular (wind direction).

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