autoSTK

• Description: Automatic spatio-temporal kriging inspired by automap (Hiemstra et al. 2010)
• Inherits most of automap functions, but extensively revised
• Includes spatiotemporal variogram fitting and Kriging analysis

Installation

• remotes::install_github('sigmafelix/autoSTK') in R (The remotes package is required)
• Before you install the package, please make it sure you have gstat, spacetime, and automap packages in your machine

Supported functions

• autofitVariogramST: automatically fit the spatiotemporal variogram
• autoKrigeST: automatically estimate the spatiotemporal variables for specified period ahead (the default value is 6 temporal units)
  • Currently support STFDF and STSDF class and ordinary spatiotemporal Kriging (e.g., y~1)
• autoKrigeST.cv: automatically cross-validate the spatiotemporal data by spatial, temporal, spatiotemporal, and random slicing

Update plan

• Add support for STIDF class and universal spatiotemporal Kriging: necessitates the gstat function fix for variogramST.STIDF
• Transition to sf and sftime in preparation of the retirement of sp in October 2023

Main features

• Split data into spatial and temporal dimensions which are compatible to be fitted as components of a spatio-temporal variogram
• Find the optimal theoretical variograms with BFHS algorithm following the autofitVariogram function of automap package; but several changes were applied

Please note

• I strongly recommend users to convert STIDF to STSDF before running functions
  • When the input is sftime, convert the input into STIDF then into STSDF. It is done automatically in most cases.

Working example

• Please consult the help page of the main functions autoKrigeST and autoKrigeST.cv.

library(autoSTK)
library(gstat)
library(spacetime)
library(stars)
library(sp)

data(air)
deair = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
deair_sf = st_as_stars(deair) %>%
    st_transform('+proj=longlat +ellps=sphere')
deair_sf = st_transform(deair_sf, 3857)
deair_r = as(deair_sf, 'STFDF')
deair_r@sp@proj4string = CRS('+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs')

deair_rs = deair_r[,3701:3800]
deair_rss = as(deair_rs, 'STSDF')

## autoKrigeST
akst_stk = autoKrigeST(formula = PM10~1, 
                       input_data = deair_rss, 
                       cutoff = 300000, width = 30000,
                       type_stv = "sumMetric",
                       model = c("Exc", "Mat", "Ste", "Exp", "Wav"),
                       tlags = 0:7, cores = 8,
                       verbose = TRUE)

akst_stk_stars = st_as_stars(akst_stk[[1]])
plot(akst_stk_stars[1,])

## autoKrigeST.cv
akst_cv_t = autoKrigeST.cv(formula = PM10~1, data = deair_rs,  nfold = 3, fold_dim = 'temporal', 
                         cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
akst_cv_s = autoKrigeST.cv(formula = PM10~1, data = deair_rs,  nfold = 3, fold_dim = 'spatial', 
                         cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
akst_cv_spt = autoKrigeST.cv(formula = PM10~1, data = deair_rs,  nfold = 4, fold_dim = 'spacetime', 
                         cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)