# Playing more with Data

I recently watched a documentary on Tsunamis and they showed an animation of a large meteor slowly tumbling while approaching earth (to explain that in the past Tsunamis could be created by meteor strikes). In an idle speculation moment I wondered at what speed meteors and other small bodies actually tumble around in space (I had also just watched the trailer for ‘Gravity’ which makes use of this idea to dramatic effect).

So I looked around and found the JPL small body database and its API for retrieving data . I donwloaded data for bodies that have a measured rotational period and did some analysis. My naive expectation was that there would be a rather uniform distribution of rotational periods, after all there would be very few limiting interactions.

Well the first insight was that rotational period is a Poisson distribution with a peak at 10 hours. So far so good. It indicates that there is some physical origin determining that rotation speed. I am cool with that, given that many such bodies originate from the asteroid belt and therefore some commonality is not surprising.

Then I decided to do a scatter diagram between diameter and rotational period and got a result that is the opposite of what I would expect. Basically the smaller the diameter the wider the distribution and the higher the median rotational period. In short, larger bodies rotate faster!?!

I would have expected larger bodies to have longer rotational periods, just as real big bodies (i.e. planets (let;s ignore moons)) do.

Do I make a fundamental error here?

**Update 17 September**

Well I did some more analysis and found out a few things. First of all I calculated the median wrong – using a proper package like NumPy highlighted that very quickly.

As you can see the median does not change as drastically towards the smaller bodies,

Second I grouped the records by sizes and then plotted the distribution,

So this shows that the differences are becoming much less of an issue. And smaller bodies do now appear to rotate slightly faster.

The upshot: be careful with visualisation! The scattergram gave an impression that upon more detailed analysis does not stand up.

So the big contradiction appears to have gone away but it is still interesting that there is a distribution. Where does it come from?