The thing is that 20,000 points is more points than anyone can
reasonably perceive on the screen to get any useful information. You
are on the right track to consider clustering.
My application (tracking 70 boats across the Pacific) gives me a raw
data set of about 15,000 points each with lat, lon, time, and boat id.
That's a lot of data to process and to upload.
What I am doing instead is pre-thinning the data using the
Douglas-Peuker algorithm on the server side, which nicely reduces the
data to any level of detail you care to specify. I got about a 90%
reduction in point count with no real loss of information on tracks.
If your data is not lines, but discrete points, then I am less versed.
Yeah, clustering sounds good. You may also want to use the BBOX
strategy to only fetch the points that are relevant to your map.
I can post the douglas-peuker code somewhere if that would be of value.
Michael
On 9/6/2011 6:15 AM, Tito, Joseph wrote:
I'm trying to expand one of my current application to handle larger
amounts of data. Right now the application comfortably handles
2000-3000 points at a time, but I'd like to expand its capabilities to
handle at LEAST 20,000 points. More would be nice, but the minimum
requirement is 20,000. Displaying these 20,000 points as X number of
'clusters' is totally acceptable, as long as it gives a good visual
representation of the full data set.
One thing I need to take into consideration is that I have multiple
data types represented as their own layers, and each user has the
ability to turn on/off layers based on their personal preferences.
My question to everyone is, how have others solved this problem? I
have a couple server side ideas to "pre-cluster" data in mind, but I
want to see how others have tackled this problem before I attack it.
If anyone has links to other relevant posts or websites, please share!
Thank you
Joe Tito
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