time slot in space-time sparse data frame (spacetime package)

Hi Jochen,
Hi:
1. I am working with the spacetime package and have successfully ingested a
dataset into its *irregular* space-time data frame using coordinates for
space, an xts object for time and auxiliary data for the attribute data.
Next I tried to create a sparse space-time data frame (i.e. a grid) using
the same pieces of data is successfully used before, plus the additional
index. The STSDF function choked on the xts data that I tried to provide it
with. The documentation on the sparse space-time data frame is, excuse the
pun, sparse. There is no indication that the data structure to represent
time is any different. The exact error message I get is:
Error in checkSlotAssignment(object, name, value) :
  c("assignment of an object of class \"POSIXct\" is not valid for slot
\"time\" in an object of class \"ST\"; is(value, \"xts\") is not TRUE",
"assignment of an object of class \"POSIXt\" is not valid for slot \"time\"
in an object of class \"ST\"; is(value, \"xts\") is not TRUE")
this has been improved; updating the package should be sufficient.
2.  Unrelated to this I have a more general question of understanding. After
creating and plotting the irregular space-time data frame, I tried to coerce
it into a full grid, alas without success. Section 7.2 of the vignette
illustrates how to move from a full grid to a sparse or irregular one and
then back again. Could it be that starting with an irregular one does not
work? I kind of would understand that because mere coercion does not provide
all the information needed - which is why I ended up trying to create the
gridded data frames directly (my first question).
This is indeed a bug; it will "work" in the next version (now in svn).
The bug occurs when no duplicate locations or time points are present;
if this is the case, coercion to the sparse or full grid representations
may not give what you want, it does not interpolation / aggregation /
density or whatever analysis you have in mind.

The following will work, but as you see, object sizes explode for the
STFDF representation if the data ARE irregular.
library(spacetime)
n = 1000
x = runif(n)
y = runif(n)
t = Sys.time()+ runif(n)
x = STIDF(SpatialPoints(cbind(x,y)), t, data.frame(x = runif(n)))
object.size(x)
43236 bytes
object.size(as(x, "STSDF"))
51404 bytes
object.size(as(x, "STFDF"))
8031244 bytes

You may want, as an initial analysis, use the method over (see the
over.pdf vignette) to overlay your data with a regular ST layout, and
e.g. count or average something from your irregular data in the new ST
"blocks" you define.
3.  Finally, the data that I am trying to massage are buoy paths expressed
as a good 22 million individual space-time points. I was thinking of using
the irregular space-time data frame structure to create the 14,855 buoy
paths (all buoys send their location information every six hours, some buoys
survive for up to eight years) and then to interpolate ocean current
surfaces similar to the Irish wind example in the spacetime vignette. The
vignette example uses full grids; will I be able to do that with the sparse
space-time data frames because the full matrix of the full grid would
certainly go beyond the capabilities of 'R'?
That depends on your soft- and hardware setup and the interpolation you
have in mind.

There is also a vignette describing how to work on subsets of larger
tables in a PostGIS data base -- admittedly a proof of concept work, but
it might be provide a starting point in case capacity is your problem.

Your email has been unanswered for a month; if you're still trying this
path (or perhaps found another), I'm interested to hear your experience.

Best regards,
Cheers,
     Jochen

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Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics      e.pebesma at wwu.de