Spring’s Got Speed


I must admit when Iron Viz was announced for this qualifier I had mixed feelings. On a positive side I know nature and animals, as a photographer and bird watcher I’ve spent a long time gathering data on animals. I even run a website dedicated to sightings in my local area (sadly it’s been neglected for years but still gets used occasionally). On the negative side though I knew I would be faced with competition from a bunch of amazing looking visualisations, using striking imagery and engaging stories.

Did I want to compete against that? With precious time for vizzing lately (especially at end of quarter – you can tell the Tableau Public don’t work in the sales team!) I only wanted to participate in Iron Viz if I could be competitive, and for those who don’t know me I like to be competitive….

So, as you perhaps guessed, my competitive edge won and I pulled some late hours and risked the wrath of my family to get something that did the competition justice.

A Note On Visualisations and creating a Brand

I’ve noted above I expected a lot of pictures and text from people in this qualifier, after all Jonni Walker has created his own brand around animal visualisations, stock photography and black backgrounds. However I have my style of visualisation,  I’m not Jonni Walker, what he does is amazing but there’s only place for so many “Jonni Walker” vizzes. I couldn’t replicate what he does if I tried.

In the past I’ve deliberately combined analytics and design, treading the fine line between best practice and metaphor, staying away from photograph and external embellishments and preferring my visualisations to speak for themselves through their colours and data. The subject this time was tricky though…was it possible to produce an animal visualisation without pictures?

Finding a Subject

I could turn section this into a blog post on it’s own! . I trawled the internet for data and subjects over days and days. Some of the potential subjects :

  • Homing Pigeons (did you know their sense of smell affects their direction)
  • Poo (size of animal to size of poo) – this was my boys’ favourite
  • Eggs (had I found this data I’d have been gazumped: http://vis.sciencemag.org/eggs/)
  • Zebra Migration
  • Sightings Data of Butterflies

Literally I couldn’t find any data to do these enough justice, I was also verging on writing scientific papers at points. I was running out of ideas when I found this website: http://www.naturescalendar.org.uk/ – and I dropped them an email to see if I could use their data. Phenology (studying natures cycles) has always interested me and getting my hands on the data would be fantastic. There was even a tantalising  mention of “measuring the speed of spring” on their news site with some numbers attached but no mention of the methodology….

Now, I’m impatient and so….using a few “dark art” techniques I didn’t wait for a reply and scraped a lot of the data out of their flash map using a combination of tools including Alteryx.

Thankfully a few days later they came back and said I’d be able to use it (after a short discussion) and so all ended well.

Measuring the Speed of Spring

Now I had the data working out how to measure my own “speed of spring” was difficult. Several options presented themselves but all had their drawbacks…the data is crowd-sourced from the public, mainly by people who can be trusted but amateur outliers could affect the result (do you want to say Spring has hit Scotland based on one result). Also the pure number of recorders in the South East, say, could affect any analysis, as could the lack of them in say, Scotland. Given we’re expecting to see Spring move from South to North then that could seriously sway results.

In the end I played between two methods:

  1. A moving average of the centroid of any sightings – and tracking it’s rate of movement
  2. A more complex method involving drawing rings round each sighting and then tracking the overall spread of clusters of sightings across the UK.

In the end I opted for the latter method as the former was really too likely weighted by the numbers of sightings in the south.

Very briefly I’ll outline my methodology built in Alteryx.

  1. Split the country into 10 mile grids and assign sightings to these based on location
  2. Taking each grid position calculate the contribution to the surrounding grids within 50 miles based on a formula: 1-(Distance/50). Where Distance is the distance of the grid from the source grid.
  3. Calculate the overall “heat” (i.e. local and surrounding “adjusted” sightings) in each grid cell
  4. Group together cells based on tiling them into groups dependent on “heat”
  5. Draw polygons based on each set of tiles
  6. Keep the polygon closest to the “average” grouping i.e. ignoring outliers beyond the standard deviation

I then did the above algorithm for each week (assigning all sightings so far this year to the week) and for each event and species.

These polygons are what you see on the first screen in the visualisation and show the spread of the sightings. I picked out the more interesting spreads for the visualisation from the many species and events in the data.

Small Multi Map

The above process was all coded in Alteryx.

Alteryx

If you look closely there’s a blue dot which calls this batch process:

Alteryx Batch

which in turn calls the HeatMap macro. Phew, thank god for Alteryx!

Now to calculate the speed, well rate of change of area if you want to be pedantic! Simple Tableau lookups helped me here as I could export the area from Alteryx and then compare this weeks area to the last. The “overall speed” was then an average of all the weeks (taking artistic licence here but given the overall likely accuracy of the result this approximation was okay in my book).

Iterate, Feedback, Repeat

I won’t go into detail on all the ideas I had with this data for visualisation but the screenshots will show some of what I produced through countless evenings and nights.

 

“Good vizzes don’t happen by themselves they’re crowdsourced”

I couldn’t have produced this visualisation without the help of many. Special mentions go to:

Rob Radburn for endless Direct Messages, a train journey and lots of ideas and feedback.

Dave Kirk for his feedback in person with Elena Hristzova at the Alteryx event midweek and also for the endless DMs.

Lorna Eden put me on the right path when I was feeling lost on Friday night with a great idea about navigating from the front to the back page (I was going to layer each in sections).

Also everyone else mentioned in the credits in the viz for their messages on Twitter DM and via our internal company chat (I’m so lucky to have a great team of Tableau experts to call on as needed).

Difficulties

Getting things to disappear is a nightmare! Any Actions and Containers need to be in astrological alignment….

Concentrating on one story is hard – it took supreme will and effort to concentrate on just one aspect of this data

Size – I need so much space to tell the story, this viz kept expanding to fit it’s different elements. I hope the size fits most screens.

The Result

Click below to see the result:

Final.png

 

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