On 18/09/13 00:10, Markus Neteler wrote:
Hi,
I came across this question:
http://gis.stackexchange.com/questions/71734/how-to-calculate-mean-coordinates-from-big-point-datasets
and wondered if this approach would be the fasted:
# http://grass.osgeo.org/sampledata/north_carolina/points.las
v.in.lidar input=points.las output=lidarpoints -o
...
Number of points: 1287775
...
Now I would use
v.univar -d lidarpoints type=point
This calculates geometry distances, not the mean coordinate:
$ v.univar -d myhosp type=point
number of primitives: 160
number of non zero distances: 12561
number of zero distances: 0
minimum: 9.16773
maximum: 760776
range: 760767
sum: 2.69047e+09
mean: 214193
mean of absolute values: 214193
population standard deviation: 128505
population variance: 1.65136e+10
population coefficient of variation: 0.599953
sample standard deviation: 128511
sample variance: 1.6515e+10
kurtosis: 0.277564
skewness: 0.801646
Is it the best way?
How about v.to.db -p op=coor and then calculating the mean of the
coordinates with an ad-hoc script. But that's probably not any faster
than Hamish' v.out.ascii approach in v.points.cog.
Would probably be a nice little module to have in C: calculate centroid
of polygon, center point of line and centroid (possibly weighted) for
points.
Moritz
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