Challenge #43: Inspire Europe '16 Grand Prix (L2)

[Kevin_VANCAPPEL]

Hello,

  here is my proposal.

  Regards,

  Kévin V.

[DanTh]

Spoiler

Other than a small glitch where I kept all of the data (rather than only keeping accidents in known postcodes, which I would argue was vague), this was the first weekly challenge I've done where even visually the workflows look identical! Completed with 34 seconds to spare.

5/5

[ARussell34]

This one wasn't too bad after I realized I needed to incorporate the Leeds Postcode data to move forward. I was able to find that:

1.) LS8 was the postal code with the most casualties

2.) For 2015, there were 978 accidents after 9/1 which was 36.71% of the accidents

3.a) For 2014, there were 910 accidents after 9/1 which was 35.93 % of the accidents  

3.b) 2015 differed by .78 % compared to 2014

On to the final!

[jjrbondoc]

Spoiler

[Erin]

Spoiler

Forgot to time myself but here it is all the same! 

[pfaigmani]

My solution to challenge # 43

[EduardoRombolli]

My solution

[pdave87]

PFA my solution. It was a good data prep + spatial exercise. Learnings continue to develop because of these exercises. 

Thanks to all SMEs.

Regards,

Pratik

[alexnajm]

Little unclear about of the 2nd and 3rd question required the full dataset or just those matching the 1st question, but here we go! Done in under 10 as well!

Spoiler

[LiuZhang]

Spoiler