Our first animated air quality maps
In a recent post we revealed our first map of local air quality, created solely from our network of low-cost air quality sensors - the ODINs (Outdoor Dust Information Nodes).
This map is based on the average levels of airborne particles over nearly a whole month (August 2016). This data was collected by 16 ODINs spread across our study town of Rangiora.
But we are actually recording data every minute. And the pattern of pollution can change rapidly. But really, you don't want to just take my word for it - the data is crying out to be animated.
Luckily for us, NIWA has some pretty nifty visualisation software that we routinely use to create brilliant weather and climate graphics like the one below (I highly recommend 'liking' NIWAWeather on facebook).
But we are actually recording data every minute. And the pattern of pollution can change rapidly. But really, you don't want to just take my word for it - the data is crying out to be animated.
Luckily for us, NIWA has some pretty nifty visualisation software that we routinely use to create brilliant weather and climate graphics like the one below (I highly recommend 'liking' NIWAWeather on facebook).
So, what happens when we plug the ODIN data into the same computer system?
Below is our first attempt. It depicts two days of air quality data from 2016. Note the time in the top left corner.
Animation is great, shows some interesting trends. I was wondering if you could add wind direction into the animation?
ReplyDeleteWe are working on it. We're trying different ways of displaying that wind information so that it is informative and not only visually appealing.
DeleteWe don't have wind information for the whole area (as a "wind field") but we do have a few wind stations that were monitoring at the time on the outskirts of the town.
Hey guys, great animation! This is an awesome visual tool depicting the rapid change in air quality in Rangiora - and that really deep purple colour is quite disturbing!!
ReplyDeleteThanks, we're still working through the data and trying different ways of visualising the spatial distribution and how it changes throughout the day. Stay tuned!
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