How to use SAS software to capture Pokémon!

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With the Pokémon Go craze sweeping the world, techies and programmers are looking to apply their skills to gain an advantage over the average user. In this blog post, I show how to use some of SAS' geospatial analytics capabilities to capture a Pikachu.

Let's say you know of a building that has an active Pokéstop with verified Pikachu sightings. First, you'll want to obtain (or create) a floor plan, and save it in an image file (png, jpg, gif, etc).

pikachu_graph_blog_floorplan

Next, you'll want to come up with a convenient coordinate system, and create a grid of unit cells by looping through the grid values in a SAS data step loop, and output 4 coordinates and an id variable for each cell. You can then use Proc GMap to draw the grid, and annotate the image of the floor plan behind the grid (here's my code).

pikachu_graph_blog_grid

Now you'll need to start collecting geospatial data that you can plot as colored areas on the grid. Here, I have determined the x/y grid locations of lures attached to this Pokéstop, and plotted them as dark brown areas on the grid. Can you detect any clustering or trends here? (Note that my friend Kenny, who was a professional/paid gamer, helped me with the finer details of this analysis.)

pikachu_graph_blog_lure

Next, I collected data about verified, and suspected, Pikachu sightings. I plotted the verified sightings in red, and the suspected sightings as yellow and orange. These sightings definitely seem to be most dense in a certain area of the lobby (red), and then become less dense as you go out from that location (yellow, then orange). The results resemble a contour or gradient heat map.

pikachu_graph_blog_sightings

Here's an example of one of the verified Pikachu sighting, from my friend Jennifer N.

pikachu_sighting_jennifer_n

And when you combine the two graphs above, look what you've got - you've captured (an image of) a Pikachu! I bet you didn't see that coming!  (Note that I used the template from yusufisik.com for the Pikachu design.)

pikachu_graph_blog_capture


OK - I apologize profusely for tricking you like that ... but, I thought it would be a great idea to have a little fun, while learning what you can do with analytics.

If you have geospatial data (Pokémon or otherwise), you really can use these SAS graphing techniques to plot and analyze it. You probably won't get a picture of a cute Pikachu when you're done, but you can gain lots of insight about your data.

For example, here's a mock-up of insurance claims after a storm in Wake County:

grid_map

And here's a similar technique applied to basketball data:

nba_shot_analysis

What other kinds of data might you analyze using techniques like this (overlaying grids on floor plans and maps)? Perhaps you have some similar techniques and tips you'd like to share?

 

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About Author

Robert Allison

The Graph Guy!

Robert has worked at SAS for over 25 years, and is perhaps the foremost expert in creating custom graphs using SAS/GRAPH. His educational background is in Computer Science, and he holds a BS, MS, and PhD from NC State University. He is the author of several conference papers, has won a few graphic competitions, and has written a book (SAS/GRAPH: Beyond the Basics).

4 Comments

  1. Pingback: Most efficient way to find rare Pokémon - SAS Learning Post

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  3. I work in the Biopharmaceutical industry. I am often asked to extract data from published graphs from which the raw data is proprietary. I love this article! Instead of Pokeman, I used the competitor's image, and overlayed a grid. Although it's still a tedious activity, I'm now able to more accurately estimate the raw data from the competitor, which gives us a better idea of how our competition is faring. Thanks!

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