n.MA.ENR.PTED.18FS (Patterns and Trends in Environmental Data) 
Modul: Patterns and Trends in Environmental Data
Diese Information wurde generiert am: 20.06.2024
Patterns and Trends in Environmental Data


Version: 3.0 gültig ab 01.08.2021
Degree Program
MSc in Environment and Natural Resources
90 hours (35 h contact lessons, 55 h self-study)
Module Coordinator
Patrick Laube
Patrick Laube and various lecturers
Entry Requirements
The studens have
  • basic theoretical knowledge and application skills in exploratory and confirmatory statistics,
  • basic knowledge in processing and visualizing data in R or any similar data processing and scripting environment (Python, Matlab, Java),
  • basic theoretical knowledge and application skills in spatial analysis and geoinformatics and the use of Geographic Information Systems (GIS).
Learning Outcome and Competences

The students will learn to

  • define the concepts of data mining and knowledge discovery in databases (KDD),
  • ​conceptualize domain-specific patterns in spatio-temporal environmental data, as well as design and implement analytical workflows for detecting and visualizing such patterns,
  • conceptualize patterns and trends in time series data, and design and implement computational procedures for their detection and visualization,
  • sketch the most important conceptual data models and data structures for movement spaces and respective movement traces, and evaluate the implications of such modelling decisions on the analytical process in computational movement analysis,
  • preprocess, filter, segment, aggregate, reshape, and visualize environmental data (exemplified with movement trajectory data and environmental data) with basic R, data science libraries (e.g. tidyverse), and additional domain-specific R libraries (in this case for the domain of movement ecology,
  • integrate R with further spatial analysis tools (e.g. ArcGIS, visualization tools) for fast and efficient prototyping,
  • design and implement basic pattern detection algorithms built upon given data structures.
Module Content
In this course the students will learn how to conceptualize and then detect patterns and trends in environmental data. These transferrable methodological skills will be trained in a signature applied ecology case study.

The module covers the typical data analysis workflow starting with data capture, then leading to preprocessing (cleaning, filtering, aggregating, reshaping data), analytical modelling, and finally visualization of outcomes. The module will address typical data quality issues of environmental, hence spatio-temporal data (accuracy and precision, uncertainty and vagueness).

The students will learn how to conceptualize domain-specific patterns (in this case movement ecology patterns). They will then study computational procedures for structuring the movement and the underlying environment, and then designing and implementing pattern detection algorithms operating on such structured data (segmenting trajectories, trajectory similarity). Furthermore, analytical procedures will be studied for linking the found patterns and trends to their constraining and enabling environmental context. The course is designed as a combination of the R statistics and visualization environment with conventional GIS software.
This module is part of a teaching exchange pilot project with the Geography Department at the University of Zurich. The module is listed at the University of Zurich as “Geo880 Computational Movement Analysis”.
Teaching / Learning Methods
Flipped classroom, group works, R labs
Assessment of Learning
The assessment of learning outcome is composed as follows
  • exercises (assessment "pass" or "no pass")
  • written report, summarizing semester project
  • Long, J. A., & Nelson, T. A. (2013). A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27(2), 292-318.
  • Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., ... & Weibel, R. (2015). Analysis and visualisation of movement: an interdisciplinary review. Movement ecology, 3(1), 1-24.
  • Laube, P. (2015). The low hanging fruit is gone: achievements and challenges of computational movement analysis. SIGSPATIAL Special, 7(1), 3-10.
  • Laube, P. (2014).Computational movement analysis. SpringerBriefs in Computer Science, Heidelberg: Springer. ISBN-13: 978-3319102672
  • Safi, K. & Kranstauber, B (2018 unplubl). Analysis and Mapping of Animal Movement in R, Chapman & Hall/CRC.