The course gives knowledge and experience in theory and practice of geographic information systems (GIS) – a set of computer tools to handle spatial (georeferenced) data. During the course the students will get familiar with the raster and vector view of the world and practice data visualization and analysis tasks applied to real environmental datasets. The course comprises collection of field data using GPS, data input and conversion, data visualization and statistics, data quality and integration, georeferencing, Remote Sensing data, multispectral classification, image enhancement and analysis, data interpolation.

After the course the students should be able to:

•          Describe the way geographical information systems (GIS) are built up and operate.

•          Compare and evaluate data collection methods and input techniques.

•          Interpret remote sensing data and describe related physical phenomena.

•          Characterize different types of spatial data using visualization and statistics.

•          Choose and justify appropriate data processing and analysis approaches according to the characteristics of data and intended analysis outcome.

•          Build models comprising sequences of GIS operations, and perform and evaluate sensitivity analysis.

•          Interpret and relate the GIS analysis outcomes to source data quality, discuss errors and uncertainty and suggest ways of improving the result.

•          Design a GIS case study to solve a specific task.

•          Document and communicate the results of a GIS study.

This is an activity-oriented course, organized as lectures/Q&A and computer labs. There is one short field exercise, followed by data upload.

The course can be followed online through Zoom, except for September 7 – the field exercise and data upload – which need to be done on campus. Note that all other computer labs take place both on campus (in the computer room booked for the exercise) and online (remote access).

There are a total of 8 lectures and 8 exercises. 

Participation in lectures/Q&A is recommended (most will be recorded) while completing all exercises is obligatory.

Each exercise is followed by a written assignment (group or individual).

Size of a group during the computer labs is maximum 2 students. 

Test remote access to ArcGIS10.8.1 before Exercise 1.

ArcGIS10.8.1 and TerrSet - both available in computer rooms and via remote access.

Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

A, B, C, D, E, FX, F

• DAT1 - Computer Assignment, 3.0 credits, Grading scale: P, F
• LEX1 - Continuous Assessment, 4.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Computer Assignment ( DAT1 ) includes completion of all computer labs and submitted lab reports.

Continuous Assessment ( LEX1 ) includes approved reports, optional Final Quiz and optional Project Assignment.

Written open book examination (TEN1 4,5 credits), participation in obligatory computer labs, submitted lab reports (LAB1 3,0 credits).

(Information above - Other Requirements for Final Grade - is not correct).

All reports are graded and contribute to the final grade.

Group reports are graded E-C, individual report E-A.

Final grade B or A is possible after successful completion of an optional Final Quiz and/or Project Assignment.

Improving the approved grade will only be possible through Final Quiz and/or Project Assignment next course round (October 2022).

If you miss the field exercise, it cannot be compensated later (but will not affect your grade).

If you miss a computer lab you can catch up yourself or join someone else who missed it (sign up to form a group on Canvas).

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.