ltsk using cumulatively expanding time space thresholds. This function is useful when predictions are needed using data points at different spatiotemporal intervals.
For example, if predictions are needed at a given location for the past 30 days at an interval of 3 days.
Instead of using ltsk 10 times, cltsk can compute all 10 values simultaneously.Function calls ltsk using cumulatively expanding time space thresholds.
cltsk(query, obs, th, nbins, xcoord = "x", ycoord = "y", tcoord = "t",
zcoord = "z", vth = NULL, vlen = NULL, llim = c(3, 3),
verbose = T, Large = 2000, future=T,cl = NULL)krig Kriging estimates at each space and time neighborhood
legend The legend for space and time neighborhood
data frame containing query point (X,Y,T i.e. XY coordinates and time) where predictions are needed
data frame containing sample data with XY coordinates, time and observed (measured) values
a priori chosen distance and time thresholds for neighbor search
a vector, number of distance and time bins for cumulative neighbor search and kriging.
a character constant, the field name for x coordinate in both query and obs
a character constant, the field name for y coordinate in both query and obs
a character constant, the field name for time coordinate in both query and obs
a character constant, the field name for data in obs
thresholds for local spatiotemporal variogram (default 75% of the max lag difference)
numbers of bins for local spatiotemporal variogram(default, space 15, temporal for each day)
lower limits for number of regions and intervals with observed data to calculate Kriging (default 3 spatial regions, 3 temporal intervals)
logical, whether print details information
a numeric constant, upper limit of neighbor points, beyond which subsampling is performance
logical, whether including observed points in future relative to query points.
a parallel cluster object (default number of cores in the local PC minue one), 0 means single core.
Naresh Kumar (nkumar@med.miami.edu) Dong Liang (dliang@umces.edu)
Function performs automatic variogram estimation for each query location using the observed data within th thresholds. The estimated variogram is used for ordinary kriging, but using data in expanding local neighborhoods for ordinary kriging.
For example, if predictions are needed at a given location for the past 30 days at an interval of 3 days,data within 3 days are used first, followed by 6 days and so on until data within 30 days. The same applies for distance thresholds.
Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
Kumar, N., et al. (2013). "Satellite-based PM concentrations and their application to COPD in Cleveland, OH." Journal of Exposure Science and Environmental Epidemiology 23(6): 637-646.
Liang, D. and N. Kumar (2013). "Time-space Kriging to address the spatiotemporal misalignment in the large datasets." Atmospheric Environment 72: 60-69.
## load the data
data(ex)
data(epa_cl)
## apply log transformation
obs[,'pr_pm25'] = log(obs[,'pr_pm25'])
## run kriging
system.time(out <- cltsk(ex2.query[1:2,],obs,c(0.10,10),
zcoord='pr_pm25',nbins=c(4,5),verbose=FALSE,cl=0))
table(out$flag)
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