Data Science / Geostatistics / R Foundation / R / Geostatistics and the Analyis of Spatial Data

R - Geostatistics and the Analyis of Spatial Data


ID 2859111
Classroom 2 days 9:00-16:30
Webinar 4 days 9:00-12:30
Method Presentation with examples and hands-on labs.
Prequisite Basics in R and Statistics
Audience Data Analysts

R Geostatistics and the Analyis of Spatial Data Training


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until one day before the training


R Geostatistics and the Analyis of Spatial Data TrainingGeostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. Such spatial and spatio-temporal data are everywhere. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. These questions refer to hypothetical processes that generate the observed data. Statistical inference for such spatial processes can be done using the statistical programming language and environment R. This training show beginners in geostatistics and participants working in the various domains of geoscience how to use R for their geostatistical analyses, visualization and plotting, model fitting, and inferential statistics. The first part of the training covers diverse techniques for handling spatial data in R, functions for import and exports of spatial data and creating diagrams and maps. The second part introduces time as a second dimension for spatio-temporal data. The third part shows you how to analyze spatial data and presents methods and functions for the anylsis of spatial point patterns and spatial point processes, interpolation, spatial prediction, the analysis of correlation, the variogram analysis as well as kriging, filtering or smoothing. This part also deals with modeling areal data and the analysis of spatial autocorrelation or fitting models.

Training Dates

  • 2021-Jan-11 - Jan-14
  • 2021-Mar-22 - Mar-25
  • 2021-May-31 - Jun-03

750 EUR +VAT

Location | Enrollment


R Geostatistics and the Analyis of Spatial Data Seminar
Handling Spatial Data in R

Classes for Spatial Data in R - Visualising Spatial Data: The Traditional Plot System, Trellis/Lattice Plots, Interactive Plots, Colour Palettes and Class Intervals - Spatial Data Import and Export: Coordinate Reference Systems, Vector File Formats, Raster File Formats, Google Earth, Google Maps, Geographical Resources Analysis Support System (GRASS) - Map Overlay or Spatial Join - R-Packages: rdgal, spplot and ggplot, latticeExtra, raster, rgeos

Spatio-Temporal Data

Types of Spatio-Temporal Data - Handling Time Series Data - Selection, Addition, and Replacement of Attributes - Overlay and Aggregation - Visualization: Multi-panel Plots, Space-Time Plots, Animated Plots, Time Series Plots - R-Packages: xts, spacetime

Analyzing Spatial Data

Preliminary Analysis of a Point Pattern: G Function (Distance to the Nearest Event), F Function (Distance from a Point to the Nearest Event) - Statistical Analysis of Spatial Point Processes: Homogeneous and Inhomogeneous Poisson Processes, Estimation of the Intensity, Likelihood of an Inhomogeneous Poisson Process - Applications in Spatial Epidemiology: Case–Control Studies, Binary Regression, Accounting for Confounding and Covariates - R-Packages for the Statistical Analysis of Spatial Data: spatial, maptools, splancs, spatstat,

Interpolation and Geostatistics

Exploratory Data Analysis - Non-geostatistical Interpolation Methods - Estimating Spatial Correlation using the Variogram: Exploratory Variogram Analysis, Cutoff, Lag Width, Direction Dependence, Variogram Modelling, Multivariable Variogram Modelling - Spatial Prediction: Universal, Ordinary, and Simple Kriging, Kriging in a Local Neighbourhood, Multivariable Prediction: Cokriging, Trend Functions and Their Coefficients, - Kriging, Filtering, Smoothing - Model Diagnostics: Cross Validation Residuals, Cross Validation z-Scores, Multivariable Cross Validation - Geostatistical Simulation

Modelling Areal Data

Spatial Neighbours and Spatial Weights - Testing for Spatial Autocorrelation - Fitting Models of Areal Data


R Geostatistics and the Analyis of Spatial Data Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

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- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

R Geostatistics and the Analyis of Spatial Data Trainer