Digital Teaching
A comprehensive course to explore fundamentals related to statistics focused on spatial data analysis. Through cutting-edge methods and techniques, you will learn how to predict and infer spatial patterns effectively. Also, designed to be undertaken with R, a command line-driven program, free of charge, with powerful statistical and spatial analytical packages. Participants without knowledge of the R language are encouraged to study this introduction to R: <https://cran.r-project.org/doc/manuals/R-intro.pdf>
Course Contents
Statistics for spatial data analytics
Univariate statistics
Bivariate statistics
Spatial statistics
Spatial simulation
Cosimulation and model checking
Spatial data practice
Literature
Bivand, R. S., E. Pebesma, and V. Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. New York: Springer New York.
Kopczewska, K. 2021. Applied Spatial Statistics and Econometrics – Data Analysis in R. Routledge Advanced Texts in Economics and Finance. New York: Routledge, Taylor & Francis Group.
Oliver, M. A., and R. Webster. 2015. Basic Steps in Geostatistics: The Variogram and Kriging. Cham, Switzerland: Springer Cham.
Pebesma, E., and R. Bivand. 2023. Spatial Data Science: With Applications in R. London: Chapman and Hall/CRC. Available at https://r-spatial.org/book/
Tolosana-Delgado, R., and U. Mueller. 2021. Geostatistics for Compositional Data with R. Cham, Switzerland: Springer Cham.
Webster, R., and M. A. Oliver. 2007. Geostatistics for Environmental Scientists. London: John Wiley Sons Ltd.
Further literature will be announced at the beginning of the course
Preconditions
Recommended:
Geodatenbanken I (13-B1-M010)
Grundlagen der Ingenieurinformatik (13-F0-M009)
GIS and Applications to Urban Development (13-B2-J003)
Previous knowledge of basic statistics and coding using R are important but not indispensable requirements.
Expected Number of Participants
15
Online Offerings
moodle
A comprehensive course to explore fundamentals related to statistics focused on spatial data analysis. Through cutting-edge methods and techniques, you will learn how to predict and infer spatial patterns effectively. Also, designed to be undertaken with R, a command line-driven program, free of charge, with powerful statistical and spatial analytical packages. Participants without knowledge of the R language are encouraged to study this introduction to R: <https://cran.r-project.org/doc/manuals/R-intro.pdf>
Course Contents
Statistics for spatial data analytics
Univariate statistics
Bivariate statistics
Spatial statistics
Spatial simulation
Cosimulation and model checking
Spatial data practice
Literature
Bivand, R. S., E. Pebesma, and V. Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. New York: Springer New York.
Kopczewska, K. 2021. Applied Spatial Statistics and Econometrics – Data Analysis in R. Routledge Advanced Texts in Economics and Finance. New York: Routledge, Taylor & Francis Group.
Oliver, M. A., and R. Webster. 2015. Basic Steps in Geostatistics: The Variogram and Kriging. Cham, Switzerland: Springer Cham.
Pebesma, E., and R. Bivand. 2023. Spatial Data Science: With Applications in R. London: Chapman and Hall/CRC. Available at https://r-spatial.org/book/
Tolosana-Delgado, R., and U. Mueller. 2021. Geostatistics for Compositional Data with R. Cham, Switzerland: Springer Cham.
Webster, R., and M. A. Oliver. 2007. Geostatistics for Environmental Scientists. London: John Wiley Sons Ltd.
Further literature will be announced at the beginning of the course
Preconditions
Recommended:
Geodatenbanken I (13-B1-M010)
Grundlagen der Ingenieurinformatik (13-F0-M009)
GIS and Applications to Urban Development (13-B2-J003)
Previous knowledge of basic statistics and coding using R are important but not indispensable requirements.
Expected Number of Participants
15
Online Offerings
moodle
- Lehrende: Felipe Francisco De Souza
- Lehrende: Andreas Eichhorn
- Lehrende: Christian Hickel
- Lehrende: Hans-Joachim Linke
Semester: WT 2023/24