Data Science / Visualization / R Foundation / R / Graphical analysis of spatiotemporal data

R - Graphical analysis of spatiotemporal data


ID 2858712
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 Graphical analysis of spatiotemporal data Training


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


R Graphical analysis of spatiotemporal data TrainingSpace-time datasets are indexed both in space and in time. Their one- or two-dimensional analysis will typically start displaying the data in diagrams revealing the inner nature and relationships of the underlying variables. This training is organized into three parts, each devoted to different types of data. Each part comprises several topics and hands-on labs according to the various visualization methods or data characteristics. In the first part of the training, you will see how you can visualize time series data by using packages like zoo and xts for the analysis of time series data and packages like ggplot2, latticeExtra, and googleVis for their presentation. The next part of the training focuses on visualisation techniques for spatial data and presents packages like raster, rasterVis, maps, and googleVis. The third and last part finally combines variables which measure time and spatial data and teaches you how to create diagrams for such complex datasets.

Training Dates

  • 2021-Mar-08 - Mar-11
  • 2021-May-17 - May-20

850 EUR +VAT

Location | Enrollment


R Graphical analysis of spatiotemporal data Seminar
Visualization of Time Series

Introduction to Displaying Time Series - Time on the Horizontal Axis - Time as a Conditioning or Grouping Variable - Time as a Complementary Variable - R Packages for time series data: zoo and xts - R Packages for visualization: ggplot2, latticeExtra, and googleVis

Visualization of Spatial Data

Introduction to Displaying Spatial Data - Thematic Maps: Proportional Symbol Mapping, Choropleth Maps, Raster Maps, Vector Fields - Reference and Physical Maps - Packages for working with OpenStreetMap - R Packages for spatial data: sp, maptools, gstat, and rgdal - R Packages for visualization: raster, rasterVis, maps, and googleVis

Visualization of Space-Time Data

Introduction to Displaying Spatiotemporal Data - Spatiotemporal Raster Data - Spatiotemporal Point Observations - R Package spacetime for spatiotemporal data


R Graphical analysis of spatiotemporal 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.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- 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 Graphical analysis of spatiotemporal data Trainer