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Milwaukee User Group Leaders:John Heisler, Continuus Technologies •Jared Grant, Manpower•Colleen Hayes, Rexnord Industries•Matt Michaelson, Continuus Technologies
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The MAUG Leadership team is pleased to announce the
First Annual Milwaukee Alteryx Users DATA CHALLENGE!
Participants should post their solutions to this thread, please include a workflow (with all associate assets necessary to run it) and a short presentation (PowerPoint Deck, short video, images, etc).
Submissions are due by November 20th. After that we will post a method for users to vote on their preferred solution. Winners will be announced at the Q4 MAUG meeting / Holiday party (tentatively scheduled for December 5th).
A zipped archive with the dataset and a PowerPoint are attached. A full explanation of the Data Challenge, the problem, rules, etc, can be found in the attached PowerPoint.
NOTE: Much of this data is fictitious or randomized in some way, please do not use for any purpose other than the data challenge!! A data explanation can be found in the attached PowerPoint.
A jolly anonymous benefactor wants to send holiday parcels to Milwaukee homes purchased since 2002.
You are going to make some of the deliveries using a magic flying vehicle.
The catch is that the vehicle can only fly 200 miles total as the crow flies.
You will need to prioritize your delivery in what ever fashion you choose, there are no wrong answers and the User Group will vote on which solution is the winner.
1) There are some odd values in the data set, address data issues and resolve them.
2) Determine a method of prioritizing your deliveries (there are no wrong answers) this will affect/inform your sequence.
3) You may choose any location in the file as your starting point. Justify the starting point.
4) You must build a sequence poly-line (spatial>poly-build) with a distance of less than 200 miles as the crow flies (spatial>spatial info).
5) Post your workflow and a short presentation or explanation to this thread.
We would like to have at least some submissions do a quick presentation at the Q4 meeting!
A resource you may wish to consider is a post titled Santa’s First Iterative Macro on the community.
No distance component in the example, but it would be great to see enhancements!! Submissions need not be this complex or utilize macros all levels of complexity are appreciated!
1) You must be a member of the Milwaukee Alteryx User Group (MAUG) to win.
2) Winners will be chosen based on votes from the MAUG community (method of vote will be announced once all submissions are received).
3) Winners will be announced at the 2019 Q4 MAUG Meeting / Holiday Party.
4) You may augment the dataset in any way you like.
5) Alteryx swag, other awards, and bragging rights for a year go to the winners!!
I've recently spent some time exploring macros and finally felt prepared to take on this challenge question.
My approach has roughly 3 steps:
1) Removing unnecessary variables and observations from the dataset.
2) Using a slightly modified version of the Santa's iterative macro to repeatedly find the nearest undelivered residence, assign stop numbers and calculate distances between points.
3) Tidy up results: Tabulate distance traveled and number of packages delivered, display a route map.
Thanks MAUG for the educational challenge and holiday whimsy!
2) SANTA'S ITERATIVE DELIVERY MACRO
Mine is essentially the same as the "Santa's first iterative macro", with two minor changes.
First, It's built to expect my subset of columns from the MAUG dataset as it's input, rather than the used in the Santa's iterative macro demo.
Second, this technique CAN be used to calculate distances. Unlike in the original macro, I'm outputting the DistanceMiles field from the FindNearest tool and carry it through the selects and joins.
Briefly, the iterative macro takes the top record in the dataset and finds its nearest neighbor in the dataset. The top record is then output, along with the distance between it and its neighbor in the DistanceMiles field and the Stop Number (iteration number). The nearest neighboring record is pushed to the top of the dataset and the process begins again. From the interface designer, the macro can be set to run as many times as desired.
3) LAST STEPS AND LOOK AT RESULTS
Map of Santa's first 50 stops:
In 200 miles, my Santa makes 3980 deliveries and hands out a total of 12278 parcels. His full route for the night is below.