Curso

SPACE-TIME PREDICTIONS AND SOME APPLICATIONS IN R

Datos básicos

Fechas de inicio y fin

Del 17/07/17 al 28/07/17

Fecha de matrícula

Preinscripción desde el 10/05/17
Matrícula desde el 7/06/17 12:46 hasta el 15/07/17 9:00


Duración

Dispondrá de 6 días para realizar la actividad.
36 horas presenciales,
3,6 Créditos ECTS

Lugar de Impartición

Aula 2.13 CFP
VALÈNCIA

Objetivos

Modeling of spatio-temporal processes is critical in many fields such as environmental sciences, meteorology, hydrology and reservoir engineering. Nowadays, spatio-temporal analysis can be adequately faced by considering important issues, such as: a) modeling the spatio-temporal random field from which data might be reasonably derived, b) choosing suitable covariance models which describe the spatio-temporal correlation of the variables of interest, c) using of the space–time models for space-time prediction, d) using adequate software packages which tackle different inferential problems.
The course will be focused on these relevant issues, taking into account the various space–time covariance models available in the literature for prediction purposes. In particular, after a brief theoretical background on Geostatistics and spatio-temporal random fields, an overview of some classes of space–time covariance models will be provided and the spatio-temporal correlation analysis will be discussed. Finally, some computational aspects regarding R environment and specific packages available for variogram fitting and prediction purposes will be illustrated.

Horario

MAÑANA Y TARDE

Lunes, miércoles y viernes entre el 17 y el 28 de julio en horario de 11:00 a 14:00 y de 16:00 a 19:00.

Precio

0 €
0,00 € - Público en general



Temas a desarrollar

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

Más información

Acción formativa dirigida a:

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

Metodología didáctica:

Clases magistrales y prácticas de ordenador.

Conocimientos de acceso:

Conocimientos previos necesarios:

Knowledge of the English language.
Introduction to statistics (mandatory).
Introduction to geostatistics (recommended).

Otra información

Director

Jaime Gomez Hernandez

Profesorado

espacioClaudia Cappello
espacioSandra De Iaco
espacioSabrina Maggio

Contacto

Correo electrónico

Nuria Llobregat Gomez

Promovido por

INSTITUTO UNIVERSITARIO DE INGENIERÍA DEL AGUA Y D


Condiciones

Condiciones generales

Consulte las Condiciones generales de la actividad.

Condiciones específicas

Tutorías:
Las consultas de los alumnos a través de foros, correo electrónico, correo interno serán atendidas de lunes a viernes dentro de un plazo no superior a las 24h. Las consultas realizadas durante sábados, domingos y festivos nacionales en España, serán atendidas en un periodo de 24h a partir del siguiente día laborable.

Las consultas realizadas por los alumnos durante el periodo de vacaciones estivales en España (del 1 al 31 de agosto), se atenderán a partir del día 1 de septiembre.

imagen separador
Inscripción Online

Compartir:

Visita otros cursos relacionados con...

 heterogeneidad,  geoestadística,  procesos spaciotemporales,  R

Imagen espacio Imagen espacio
Inscripción Online

Compartir:



Elige la UPV
Cursos de matrícula flexible
Noticias: