Spatial and Temporal Pattern Analysis

Course content

The purpose of this course is to introduce students to the concepts, terminology and methods related to analysis of spatial and temporal patterns in digital data. The course will discuss and analyze how patterns can be identified, measured and tested statistically through a series of lectures, hands-on exercises and student presentations. At the end of the course students will be able to identify and describe geographical and temporal distributions, to measure the patterns formed by time series, point- and areal data, and to statistically analyze data for spatio-temporal clustering, hot spots, break points, and spatio-temporal variations in explanatory variables for the observed phenomena.

The course is composed of a mix of lectures, hands-on exercises and student presentations of studies using different indicators of spatial and temporal patterns. The main focus is on methodological issues and students’ involvement in classroom discussions is pivotal. A portfolio assignment developed through the course forms the basis for an oral examination.

Education

MSc Programme in Geography and Geoinformatics
MSc Programme in Geography and Geoinformatics with a minor subject

Learning outcome

Knowledge:

Geoinformatics, GIS, remote sensing, spatial analysis, spatial statistics, local indicators of spatial autocorrelation, pattern description and analysis, spatially weighted regressions, ordinary least squares regression, advanced time series analysis.

Skills:

  • Describe and discern spatial and temporal patterns
  • Presentation of own results and results published by others
  • Produce easily understandable maps
  • Describe advantages and disadvantages of different spatio-temporal pattern detection methods

 

Competences:

  • Understand the use of spatial metrics and time series analysis in science and society
  • Apply appropriate methods, indicators and metrics to data
  • Critically evaluate the use of spatio-temporal metrics and statistics
  • Presentation of advanced spatial and temporal analysis on simple maps
  • Critical understanding of studies using spatial statistics and time series analysis

The course will dominantly focus on hands-on exercises supplemented by student and teacher presentations and classroom discussions.
For the teaching plan, please see Absalon.

Please see Absalon.

BSc in Geography and Geoinformatics or equivalent is recommended.

Oral
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Written assignment, Ongoing
Oral examination, 20 minutes.
Type of assessment details
A written assignment is prepared during the course and must be handed in prior to the exam week. The oral exam uses the written assignment as its point of departure to explore the readings listed in the officially approved reading list. A combined grade is given after the oral exam.
Aid
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Identical to ordinary exam.

The student has the following options:

Is the quality of the written assignment not acceptable, the student can choose to either hand in a new or revised report.

Is the quality of the written assignment acceptable, the student can choose to either hand in a revised report or resubmit the original report from the ordinary exam.

The written assignment must be handed in prior to the re-examination week. The oral exam uses the written assignment as its point of departure. It includes the titles listed in the officially approved reading list.

Criteria for exam assessment

Please see learning outcomes

Single subject courses (day)

  • Category
  • Hours
  • Preparation
  • 171
  • Theory exercises
  • 35
  • English
  • 206

Kursusinformation

Language
English
Course number
NIGK17011U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 2
Schedulegroup
A
Capacity
22
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Geosciences and Management
Contracting department
  • Department of Geoscience and Natural Resource Management
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Stéphanie Marie Anne F Horion   (3-78726d456e6c7333707a336970)
Teacher

Martin Rudbeck Jepsen
Rasmus Fensholt

Saved on the 28-02-2023

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