Kies de Nederlandse taal
Course module: 800880-M-3
Research Skills: Spatiotemporal Data Analysis
Course info
Course module800880-M-3
Credits (ECTS)3
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSHD: Department Cognitive Science and AI;
Is part of
M Communication and Information Sciences
M Data Science and Society
M Cognitive Science and Artificial Intelligence
dr. L.L. Ong
Other course modules lecturer
Academic year2019
Starting block
Course mode
Registration openfrom 15/01/2020 up to and including 21/08/2020
Learning Outcomes:

Upon successful completion of the requirements for this course, students will be able to:       
  • understand and apply statistical models to analyse data over space and time,
  • use these models to summarize data and predict outcomes,
  • select appropriate methods to analyse temporal and spatiotemporal data,
  • use existing functions and software packages to implement these methods and develop their own code for the problems,
  • evaluate statistical models and communicate the results. 
In pursuit of the “why” questions, the “when” and “where” questions often arise. Spatial data specify “where” and temporal instances specify “when” data is collected.  The demand for spatiotemporal analysis is increasing due to the rapid growth and widespread collection of spatiotemporal data across various disciplines. Spatiotemporal analysis can illuminate any unusual patterns and interesting information or allow the study of persistence of patterns over time. 
This course is an introduction to the challenges and techniques to analyse spatiotemporal data, This course aims to provide students with the fundamental knowledge of spatiotemporal modelling. It will cover the similarities and differences in spatial and temporal data, techniques to visualize spatiotemporal data, linear methods to interpolate, extrapolate and smooth temporal and spatiotemporal data and linear generative models such as autoregression. Additional topics may include Markov and hidden Markov models.
Lectures will be complemented with interactive demonstrations and hands-on exercises to provide students with practical experience in spatiotemporal data analysis.

Practical Sessions:
Students are required to submit at least 4 out of 6 resulting scripts from the practical session worksheets. These scripts will not be graded.
Individual take home assignment:
The individual final exam for 40% of the grade
Final Exam:
The final exam which counts for 60% of the grade

“Due to limited capacity, this course is currently not open for external students.”
Contact person
dr. L.L. Ong
Timetable information
Research Skills: Spatiotemporal Data Analysis
Written test opportunities
Written test opportunities (HIST)
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 3129-05-2020
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 3226-06-2020
Required materials
Recommended materials
Assignment (40%)

Written (60%)

Final Result

Kies de Nederlandse taal