I have a dataset of just over 700,000 incidents that happened in square-ish Texas county that's about 30 miles on each side.
Here's the parameters reported by v.kernel as it's executing: *STDDEV: 1000.000000* *RES: 111.419043 ROWS: 458 COLS: 447* * * *Writing output raster map using smooth parameter=1000.000000.* * * *Normalising factor=6482635.018778.* I am running this on a Windows 7 x64 machine with 8 GB RAM and an Intel Core i7 Q720 1.6 GHz with 4 physical cores. I notice that it's not multithreaded, only using 1 core. It takes about 16 hours to complete. Is this correct? I'd like to use this on a dataset with closer to 5 million records, and I'm really concerned how long it may take. I tried the v.kernel again with about 10X more cells on each dimension, just to see what would happen. It ran for several hours, and based on the progress meter, I guess it would take less than a day to complete. Unfortunately, it crashed mid-way. See http://trac.osgeo.org/grass/ticket/1800. I posted my question about the 16+ hours at http://gis.stackexchange.com/questions/41058/how-do-i-compute-v-kernel-maps-in-less-than-16-hours/. Bill Huber, who si apparently knowledgeable about kernel density calculations in general, posted a response, and he felt like a kernel density map shouldn't take much time at all. But digging more deeply, turns out he had come up with a kernel density calculation method over a decade ago using Fourier transforms. See http://www.directionsmag.com/features/convolution/129753 and the next two articles linked to it (they are short articles). Apparently this transforms it from an O(n^2) problem to an O(n ln n) complexity problem. I inspected v.kernel's main.c ( http://trac.osgeo.org/grass/browser/grass/trunk/vector/v.kernel/main.c), and looks like v.kernel uses an output-centric method (using Bill's wording) of calculating the output, which seems like O(n^2) complexity. So I guess what I'm getting at is it appears to me that the algorithm behind GRASS GIS's v.kernel is straightforward but is a greedy algorithm ( http://en.wikipedia.org/wiki/Greedy_algorithm), which is fine, but it make take a while to execute. Is this true? Is there not spatial indexing I could add to the dataset? I've done various Google searches on that and can't come up with anything clear. Aren
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