Were you the kid who sat there analyzing the amusement park map before entering the park, planning out how you could visit the most rides in the least amount of time? If so, then this blog's for you, my data analyst kindred spirit!
And to get you in the mood, here's one of those amusement park maps. This is a map of Carowinds, the park I went to often when I was a kid:
Some might call it overkill, but if you are serious about your Pokémoning then you will want to use every tool at your disposal - including the most powerful analytics software in the world! In this blog post, I show you how to use SAS software to optimize your search for rare Pokémon.
Let's say you've been playing for a while, and only have 15 of the more rare Pokémon left to catch. While you could try hatching them from eggs, you've decided to try a more analytical approach. You have perused all the online forums and compiled a list of possible sightings of these rare Pokémon, and then determined the closest location of each one. You can now plot these on a map using SAS' GMap Procedure:
Of course you could start driving across the state, in a random fashion, zig-zagging from one location to the next ... but just like your Carowinds visit, that's not the kind of person you are. You want to find the maximum number of Pokémon in the minimum amount of time and distance. And for that, you can use SAS' OptNet Procedure to calculate the "optimal tour" to visit all the locations in the most efficient order (using the TSP statement to invoke an algorithm that solves the classic traveling salesman problem).
Now that I've demonstrated this technique using some fun data, what non-Pokémon analyses might you be able to use it for? (when you come up with some ideas, here's my code in case you want to re-use it) Don't forget to check out my other Pokémon-related blog posts on: types of Pokémon, contour/heat maps, and the Nintendo stock price!
And for the big question - what's the most rare Pokémon you've caught so far.