Course ObjectiveThe objective of this course is to become familiar with more advanced
approaches for the analysis of large spatial and temporal datasets, such
as related to earth observation, climate change, and land cover change.
Course ContentThe first four weeks of the course will provide students with the tools
to conduct spatial, time-series, or spatio-temporal analysis. These will
be presented in class, and practiced using computer exercises. Exercises
start with the basics in scripting (using Python), and focus on those
tools that are useful for the above-mentioned analyses: reading and
writing spatial data and time-series data, loops and conditional
statements, libraries for handling spatial data and time series data
(such as arcpy and numpy) and visualizing data and results.
In the second period, students will form groups of 3 to 4 persons and
work together to conduct a spatial, time-series, or spatio-temporal
analysis under the supervision of one of the lecturers. Topics for these
assignments will be provided and are tailored towards the various study
tracks. In addition, we will have a presentation and discussion sessions
on selected topics, based on student-presentations.
Teaching MethodsThe course requires 8 weeks of part-time study of approximately 20 hours
a week. The first four weeks will consist of a combination of lectures
and computer exercises in which various tools and techniques will be
presented, discussed and practiced. The second half of the course will
consist of group assignments in which the tools that are discussed
before will be used to conduct a spatial, temporal, or spatio-temporal
analysis under the supervision of one of the lecturers. Tuition in this
period will include opportunities for consulting lecturers with respect
to the final assignment and student-presentations on selected topics.
Method of AssessmentStudents will be evaluated based on submitted exercises in weeks 1-4
(50%) and a group assignment in week 8 (40%) including group
Entry RequirementsThis course assumes a basic knowledge of GIS and GIS-based spatial
analysis (such as GIS and Digital Spatial Data (AB_1076), or
equivalent). This requirement is met by all students that completed
their BSc in Earth Sciences or Earth and Economics at VU University.
Prior knowledge of and experience in scripting/programming would be an
asset but is not a requirement.
LiteratureSelected literature will be provided on Canvas.
Target AudienceMaster students in Earth Sciences (ESPCaR track, Earth and Climate
track, Global Environmental Change & Policy track), Master students in
hydrology and other relevant studies.
Dr. J van Vliet, Prof. Dr. G. van der Werf, Dr. S. Veraverbeke, and Dr.
Recommended background knowledgeAny additional experience with GIS, spatial analysis, and
programming/scripting would be an asset.
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. ir. J. van Vliet|
|Examiner||dr. ir. J. van Vliet|
dr. ir. J. van Vliet
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Computer lab, Study Group|
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