0

I am working on an exercise which consists on retrieving data from foursquare in order to determine where to add a Gym based on less competition. I have already called the API and cleaned my data, and was even able to map all the gyms. I am stuck, however in figuring out a way to use the data (coordinates) from my set, with the restriction that it needs to be limited to a specific area (I figured I can do top-left, right bottom coordinates for this). Any ideas?

picture of my current map

A solution using heat maps instead of an exact location would work as well.

Fabianz
  • 1
  • 1
  • Please, refine your question with a clear goal. Furthermore, please provide full code and data. Thanks. – sentence May 26 '20 at 17:30

1 Answers1

0

Euclidean distance xor Manhattan distance could be triangulation based pattern recognition derived of knneighbor alg.

  • I would suggest looking into linking and chaining locations and applying a knn based algorithm compatible to the api call for optimal dist, and further develop derived metas from the bases.
  • some immediate thoughts came to me as I had to complete a very similar project in 10 days.

:initial_thoughts ~

.__o(N1?)=rnt?

[.get, .describe, .info, .shape, .round, .map, warm_start(condition)=T/F, geopandas, pyautogui, pyautoui, .describe, .dist, .metrics, .values, *(also, is this linear or nonlinear based? Some discretionary values can be calculated from bootstrapping ceiling floor differentials :: partial differentials for aprx dist.

Take a look into different density, even theta per area*dens, Kp) / sensitivity * time(req) ,..., and foot.traffic functions that are available. py 3.8 was best for this based on mapping properties.

that_stanford_kid
  • 1
  • 1