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Course module: 800880-M-3
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; TSH: Department Cognitive Science and AI;
Is part of
M Communication and Information Sciences
M Data Science and Society
M Cognitive Science and Artificial Intelligence
Lecturer(s)
Lecturer
C.D. Emmery, MSc
Other course modules lecturer
Lecturer
dr. L.L. Ong
Other course modules lecturer
Academic year2020
Starting block
BLOK 2
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 14/10/2020 up to and including 20/08/2021
Aims
Learning Outcomes:

Upon successful completion of the requirements for this course, students will be able to:       
  • describe fundamental concepts, statistical models and techniques (e.g. autocorrelations) in temporal and spatiotemporal data analysis,
  • implement solutions to real-world temporal and spatiotemporal analysis problems,
  • describe and evaluate widely used temporal and spatiotemporal analysis algorithms,
  • use existing functions and software packages (e.g. statsmodels) and develop their own code to automatically analyze time series and spatiotemporal data.
Content
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.

Evaluation:
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 assignment counts for 40% of the grade
 
Final Exam:
The final exam 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
DescriptionTestBlockOpportunityDate
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 2228-01-2021
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 2122-12-2020
Required materials
-
Recommended materials
-
Tests
Assignment (40%)

Written (60%)

Final Result

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Kies de Nederlandse taal