Hi Giuseppe,
Thanks for confirming what I already suspected, and that "geography" is
not supported.
Maybe, as a first step to implementing this, just assuming the globe is
a perfect sphere instead of spheroid, would help in easing implementing
something like this for "geography", and provide significant performance
benefits as well. Since 'ST_ClusterDBSCAN' is already a non
deterministic process, the error of assuming the globe is a sphere is
not particularily relevant I think, but would allow you to run the
clustering process against a global data set.
Marco
Op 21-12-2020 om 12:15 schreef Giuseppe Broccolo:
Hi Marco,
Il giorno dom 20 dic 2020 alle ore 10:03 Marco Boeringa
<ma...@boeringa.demon.nl <mailto:ma...@boeringa.demon.nl>> ha scritto:
Hi,
Reading through the PostGIS documentation, I noticed the
"ST_ClusterDBSCAN" function takes a distance as one of the inputs.
Now
the docs suggest the current algorithm only takes in "geometry" type
data. Is that true? Based on the distance input variable, it would
seem
logical to have a "geography" variant as well, even if that is a
considerably slower variant considering the more complex distance
calculation.
Yes, ST_ClusterDBSCAN takes just the geometry type in input. This is
because most of the algorithm
is implemented using utilities already existing in GEOS for planar
geometries.
Adding the support for the geography type would mean to re-implement
the algorithm specifically for
spherical objects, as you mentioned.
Regards,
Giuseppe.
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