I have location posts from Instagram Data and want to cluster them by density - using DBSCAN. I played around quite a lot with different minPts and eps but never receive clusters (always one big cluster) - even if this is wordwide data. Does anyone have a suggestion on how to choose the parameters in order to receive a suitable result?

Enclosed the file I am playing with my file

  • 1
    what range of values have you tired? – Ian Turton Jan 27 '19 at 16:53
  • Dear Ian, 0.1 to 1000 – Roman Egger Jan 27 '19 at 17:36
  • 3
    the issue is likely due to your point coordinates being Lon/Lats (i.e. a geographic CRS), where values between 0.1 - 1000 loosely corresponds to 10km - 100000km (along the equator)... – geozelot Jan 27 '19 at 18:01
  • So what can I do? Tried now to convert it to a shp-file (that´s what I googled but doesn´t work either) – Roman Egger Jan 28 '19 at 10:14

Based on @ThingumaBob's suggestion, it seems the issue is that you're working in a geographic CRS, where the units are degrees. Since each degree has an inconsistent size depending on where you are on the globe, this results in clusters of wildly different sizes. You need to reproject your data into a projected CRS. Given that your data is worldwide and you're trying to use a tool based on distance, I suggest a world equidistant projection. Options include:

  • World_Azimuthal_Equidistant
  • World_Equidistant_Conic
  • World_Equidistant_Cylindrical
  • World_Two_Point_Equidistant

See Wikipedia's List of map projections for a discussion of the different projection types.

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