[R-sig-Geo] Center of mass of a SpatialPointsDataFrame object - Which projection?

[PCB]

Yes, in this case I have one set of observations per year, so Time is
discreet.

I did look at the Spatial Task View, but could not understand all that I
read...
I am using the gstat package, looks like it has all the features I want, but
I still need to find out how to calculate the distances if I am using a
lon-lat CRS. Anyway, I will try a go at using UTM, and will get back to the
Spatial Task View. I am looking at an area between -5 and -25 Degrees, with
a longitudinal extent of about 15 degrees longitude
Since we are at this, is gstat the most appropriate package for doing this?
or should I be trying one of the others? I am trying to work also with the
"raster" package, for doing the cropping, but your mail suggests I could do
it using gstat. I will find out how to do it there.
Thanks a lot

.
Barry Rowlingson wrote
> 
> On Thu, Jun 7, 2012 at 2:10 PM, PCB <pedrocontebarros@> wrote:
> 
>> I have a dataset that represents measurements of fish density at a number
>> of
>> sampling locations at sea, in several different occasions (the sampling
>> locations are not always the same), stored as a a list of
>> SpatialPointsDataFrame objects.
> 
>  So your data is:
> 
>  Lat, Long, Time, Mass
> 
>> I want to calculate the center of mass of each distribution, to describe
>> whether there is a trend in the overall distribution of the fish.
> 
>  What do you mean by 'each distribution'? Your second sentence makes
> me think that Time is discreet, or can be grouped into discreet
> 'Occasions'.
> 
>> I have three main questions:
>> a) Should I use the original points for calculating the centre of mass,
>> or
>> should I interpolate them first to a grid? I assume doing it on the
>> original
>> points will make the result dependent on the location of these points,
>> that
>> is really not linked to the underlying fish distribution
> 
>  Your data are samples from an underlying distribution of mass, so you
> want to do something like Kriging to get an interpolated surface over
> a grid.
> 
>> b) Should I reproject the data before calculating the center of mass
>> (they
>> are in lon-lat). If so, which projection should I use?
> 
>  If the Kriging package you use (and there's about 4 in R - gstat,
> geoR, et al - read the Spatial Task View) can work out distances from
> lat-long on the sphere (or ellipsoid) then you don't need to. However
> computing distances from lat-long can be slower than from x-y
> cartesian coords.. so...
> 
>  If your data are over a relatively small area then convert to a
> cartesian coordinate system in metres for units and then all the
> Kriging packages will work, and they'll compute distances using
> Pythagoras.
> 
>  Now, which projection? Depends on the region you have data for. UTM
> is a fairly safe bet, unless you're close to the poles, in which case
> I think there must be polar projections. Check
> www.spatialreference.org and see what turns up.
> 
> 
>> c) A rectangular grid will include many locations on land, where no fish
>> could have been recorded anyway. How do I get a grid I can use to
>> meaningfully calculate the center of mass?
> 
>  I think most of the Kriging packages can mask the output region by a
> polygon, or you can mask raster objects with rasters or clip them to
> polygons.
> 
>> Thanks for your time, and any pointers will be highly appreciated.
> 
>  The Spatial Task View is the place to start!
> 
> Barry
> 
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