However, this sort of data is prone to observation bias – that is, as more people are in Australia, more tornadoes are seen. And in talking to the Bureau of Meteorology, they confirmed the effect of having more observers as time progresses, including key initiatives such as the Severe Storm unit and the Storm Spotter network, have skewed the data, so it’s impossible to determine if tornadoes are more or less frequent.
It is still possible to display the tornadoes by location, with a measurement of intensity (the Fujita scale), over time, which is what I ended up doing.
I used my bushfire map as a base for this, and added a toggle to switch between intensity and damage. Also, instead of animating by using d3 to filter the dataset and draw the points at each time point, as I did with the bushfire map. I instead drew all the points and then animated the visibility with jQuery. Animating with jQuery with an up front load time makes it faster, which is great. This wasn’t possible with the bushfire map as the dataset was so large it was only feasible to load sections at a time.
- Vitamin C
- Just testing that these posts are stacking correctly. Read more – ‘Vitamin C’.
- Iframer testerino
- This is a little thing we call a test Read more – ‘Iframer testerino’.
- social boyyy
- another little tester gfdgdzf Read more – ‘social boyyy’.