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Do you have the skills to make it to the top? Subscribe to our weekly challenges. Try your best to solve the problem, share your solution, and see how others tackled the same problem. We share our answer too.
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Do you have the skills to make it to the top? Subscribe to our weekly challenges. Try your best to solve the problem, share your solution, and see how others tackled the same problem. We share our answer too.
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Challenge #100: Find Ned!

Director, Customer Enablement
Director, Customer Enablement

@lminors - link is back online! Sorry for the hiccup!

Alteryx
Alteryx

Got within 0.3m in 3.3 seconds.

 

My solution:

 

Spoiler
Tried several different approaches, including buffering the network to refine, but decided that iterating through reducing grid-sizes for the grid furthest from the road network (using find nearest) was the optimal solution.

Another little trick I used to optimise was to use the records from file load within the find nearest tool, which shaved several seconds off!

Capture9.PNG
Fireball

 

 

This one took me a while!

 

Spoiler
Result:
challenge_100_coords.PNG
challenge_100_result.PNG

 

Spoiler
As others did, I used the Find Nearest tool with increasingly smaller grid squares. For each iteration, I kept the 10 closest grid squares to limit the data size.
I used 10-km, then 0.5-km, then 0.05-km (50 m) grids.

challenge_100_workflow.png

 

 

Alteryx Certified Partner
 
Asteroid

Hi! Here my solution :)

 

Spoiler
success.PNG
I tried it first the way of 'brute force' solution...but after processing +50GB and not even 1% of total, i changed to an approximation

First, I looked for 10x10km areas where there were no roads...then, modelling those areas with points 100m distance from one to another...and again looking for the maximum-minimum distance...
Pulsar
Pulsar
Spoiler
All I can say about my process to get this answer is yikes...

2.PNG

Capture.PNG
Highlighted
Alteryx Certified Partner
Spoiler
100. Spatial.PNG
Haven't used spatial tools much , and so I looked for some ideas. In the end, I took @David-Carnes' idea of using a couple of grids and the find nearest tool to get nearer and nearer to Ned's location. Excellent challenge though!

100. Spatial 2.PNG
Alteryx Certified Partner
Alteryx Certified Partner
Spoiler
20-06-_2019_12-54-10.png

I tried different approaches, but ended up with some iterations. 12.2 seconds seems to be okay ...

 

20-06-_2019_12-55-15.png

 

Comet

Didn't come up with an efficient method and the method I wanted to take takes too long to solve, so ended up with this which takes over 2 minutes to solve.

 

Spoiler
So the way I originally approached this was to take the road network and add a buffer around each point, to maximise the chance that it would create a complete network. Then summarised this to get a polygon for the entire road network. Then doing a spatial process with the State polygon and splitting to end up with the holes/regions which I could then use a spatial process to get the centroid of and calculate the distance to nodes on the road network and sorting to find the furthest point but this took too long to solve (over 15 mins and still without an answer). 


So instead took a similar approach to others finding smaller grids with no roads in until you get to a solution (worked with 100m grids).

challenge 100a.PNGChallenge 100.PNG

 

Asteroid
Spoiler
100.PNGNED.PNG