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Spatial Data Science with R - Einzelansicht

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Veranstaltungsart Seminar Langtext
Veranstaltungsnummer 142970 Kurztext
Semester SoSe 2023 SWS 4
Erwartete Teilnehmer/-innen 30 Studienjahr
Max. Teilnehmer/-innen 40
Credits 5/6 Belegung Belegpflicht
Sprache englisch
Belegungsfrist Institut für Geoinformatik    16.01.2023 - 22.01.2023   
Einrichtung :
Institut für Geoinformatik
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Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer/-innen
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Fr. 10:00 bis 12:00 woch 07.04.2023 bis 14.07.2023  Heisenbergstr. 2 - GEO1 242        
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Fr. 12:00 bis 14:00 woch 07.04.2023 bis 14.07.2023  Heisenbergstr. 2 - StudLab GEO1 126        
Gruppe [unbenannt]:
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Zugeordnete Person
Zugeordnete Person Zuständigkeit
Pebesma, Edzer, Prof. Dr. verantwort
Abschluss - Studiengang Sem ECTS Bereich Teilgebiet
Master - Geospatial Technologies (88 994 7) - 5
Master - Geoinformatics (88 E62 12) - 5/6
Master - Geoinformatics and Spatial Data Science (88 F26 21) - 5
Prüfungen / Module
Prüfungsnummer Modul
16002 Course Electives 2 - Master Geoinform and Spat. Data Version 2021
16001 Course Electives 1 - Master Geoinform and Spat. Data Version 2021
13004 Course Spatial Data Science - Master Geoinform and Spat. Data Version 2021
17001 Advanced Topics in Geographic Information Science Course, Institute for Geoinformatics (ifgi) - Master Geoinformatics Version 2012
18001 Selected Topics Computer Science - Master Geoinformatics Version 2012
19002 Course Interdisciplinary Aspects Geographic Information Science 1 - Master Geoinformatics Version 2012
24002 Course Interdisciplinary Aspects Geographic Information Science - Master Geoinformatics Version 2012
Zuordnung zu Einrichtungen
Fachbereich 14 Geowissenschaften


The goals of this course are to get familiar with data science, with spatial data in data science problem, with techniques and tricks to using R for solving smaller problems, and with programming R to solve larger problems. The final assignment is writing an R package for solving a particular spatial data science problem that passes all checks cleanly, and that contains one or more vignettes illustrating the

problem(s) solved. The grade is composed of the grade for the final assignment, and a grade for active participation (weekly assignments).


Carrying out a proper scientific data analysis exercise involves not only understanding appropriate statistical methods, but also understanding the data, of the domain from which the data and the research question originate, and familiarity with tools that are needed or that still need to be developed to carry out the analysis. Ideally, the analysis should be carried out such that the report does not only give full insight in all details of the analysis, but also makes it relatively effortless to reproduce or modify the complete analysis, and by that recreate the report itself, or modify it. The combinations of all these skills can be called data science.

Spatial data science is a flavor of data science where spatial locations play a role, not only to hold records togehter, but also to compute and analyse distances, consider spatial autocorrelation, or compute spatial relationships between features with different geometry types, such as distances from points to linestrings. Quite often, temporal considerations play a role too when considering spatial properties.

R is a language and environment for statistical computing that plays a large role in data science, because it is open source, it is easily extendible, and it used a lot by non-computer programmers to carry out programming tasks.

For the course organization, see https://github.com/edzer/sdswr

Die Veranstaltung wurde 6 mal im Vorlesungsverzeichnis SoSe 2023 gefunden:
Spatial Data Science  - - - 2
Electives  - - - 3
Computer Science  - - - 5