[postgis-users] 'clustering' of points
amitkulz at yahoo.com
Mon Mar 13 07:04:06 PST 2006
You might want to look at Crimestat. Ned Levine has created a nice
little program which is *not* crime specific. It may suffice for your
needs, if you dump the stuff into X, Y points. I had emailed Ned a year
back about integrating some scripting functionality and he emailed me
that some people had got it working using VB/VBScript. Unfortunately,
Crimestat is Windows only.
My only comment: I sometimes despair at the hacks we have to create in
order to fulfill our time/space constraints. I wish there was a better
way to attack speed/memory problems involved with int/float/double
arithmetic. Hmm, a good topic for some research..
This is somewhat related to a thread about "density mapping" that took
place on the mapserver list.
My last response
I accomplished a similar task, and how I would add it into Mapserver.
One thing to note, however, is that this is for visualization, not
statistics. If you're looking for "real" kernel density, a tool like R
or GRASS might be better.
Josh Livni wrote:
> I am creating a map where it would be useful to cluster points, such
> that if many points were 'close' together, the map instead displays a
> 'cluster' point for that area.
> Right now I have a python script that queries my postgis database for
> points with a bbox, and then I crudely break up the bbox into small
> grids, count the points within it, and if there are lots, I may
> replace some of those points with 'cluster' point.
> But, this is very crude. I am wondering if there's a clever way to
> make some kind of SQL query, such that if there are a 'lot' of points
> near a point, it will look at all 'nearby' points, and then return
> also 'center point' (perhaps a new point that's the average of nearby
> points) along with a list of points included in that 'center points'
> And assuming there's not a pure SQL query, I am guessing this is a
> problem that people have looked at before, but I don't know in what
> context or jargon. So, I hope the above makes sense, and someone has
> an algorithm or better jargon words they can point me to.
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