PREDICCIONES ESPACIOTEMPORALES Y ALGUNAS APLICACIONES EN R

Environmental engineers, hydrologists and hydrogeologists, reservoir engineers, civil engineers

Course Schedule:
Geostatistics and spatio-temporal random functions
Theoretical framework on spatio-temporal random functions
Properties of the spatio-temporal covariance function and semivariogram
Classes of space-time covariances functions: an overview on some theoretical covariance models
Spatio-temporal structural analysis
Semivariogram and covariogram estimation and model fitting
? Empirical spatio-temporal semivariogram and covariogram
? Fitting spatio-temporal semivariogram and covariogram models
? Validation and comparison of spatio-temporal semivariogram and covariogram models
Characteristics of some classes of space-time covariance functions
Some statistical tests on semivariogram and covariogram characteristics
Spatio-temporal prediction
Spatio-temporal kriging
Spatio-temporal kriging equations
R Environment for space-time predictions
Introduction to R code
Spatial and Spatio-temporal data in R
- Construction of Spatio-Temporal Objects in R: Data formats (classes) and methods for spatio-temporal data (R packages required: sp, spacetime)
- Reading and writing spatial and spatio-temporal data (R packages required: sp, spacetime)
Spatio-temporal structural analysis in R
- Semivariogram and covariogram estimation and model fitting (R packages required: gstat)
- Scripts in R to test some features of spatio-temporal covariance functions

Spatio-temporal prediction in R (R packages required: gstat)

Case studies by using spatio-temporal datasets (R packages required: sp, spacetime, gstat).

Calendar Course (6 hours per day: 3 hours - theory, 3 hours - laboratory)
DAY 1:
? Geostatistics and spatio-temporal random functions: theoretical framework on spatio-temporal random functions; properties of the spatio-temporal covariance function and semivariogram
? Introduction to different formats of spatial and spatio-temporal in R: spatio-temporal full data frame; spatio-temporal sparse data frame; spatio-temporal irregular data frame
DAY 2:
? Geostatistics and spatio-temporal random functions: an overview on some theoretical space-time covariances models
? Reading and writing spatial and spatio-temporal data. Subset a spatio-temporal object. Graphical representation of spatio-temporal data
DAY 3:
? Spatio-temporal structural analysis: semivariogram and covariogram estimation and model fitting
? Semivariogram estimation. Fitting a spatio-temporal variogram model in R
DAY 4:
? Spatio-temporal structural analysis: validation and comparison of spatio-temporal semivariogram and covariogram models; some statistical tests on semivariogram and covariogram characteristics
? Cross-validation procedure in R
DAY 5:
? Spatio-temporal prediction: spatio-temporal kriging; spatio-temporal kriging equations
? Scripts in R to test some features of spatio-temporal covariance functions
DAY 6:
? Spatio-temporal interpolation in R
Case studies by using spatio-temporal datasets