Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Geographic information systems

Te lang geleden voor docent- en roosterinformatie
Contents:Geographic Information Systems (GIS) are elaborate software packages for the input, manipulation, analysis, and presentation of geographic data. With analysis of data we mean the possibilities for combining different thematic data sets or map layers (like population density and political preference), or generating best solutions (for the planning of a new airport), or modelling and computing consequences (for example, of global warming). Geographic Information Systems are use by geographers, politicians, geologists, civil engineers, etc. In this course the emphasis is on representation, algorithms, and cartographic computations.
Literature:Reader in library/infotheek, handouts or slides from web page.
Course form:Lectures, combined with a project.
Exam form:Written exam(60%) and project (40%). Both items must be evaluated with at least a 5.
Minimum effort to qualify for 2nd chance exam:To qualify for the second test (retake) you need to have a grade of at least 4 for the first test (final exam).
Description:For a typical GIS application, data must first be acquired (measured, bought, ...), next adapted or transformed, then stored, after which the data is ready for querying and analysis. In the end the result of a query or analysis can be visualized, often using maps. During the GIS course the nature of geographic data is treated first. Data sources, acquisition, and representation and the topics to be treated next. Raster and vector representation are the two most important forms. Of both, an example data structure will be discussed. The analysis of data can consist of map overlay, buffer computation (neighborhood analysis), cluster computation, auto-correlation, and model computation. Visualization is usually done by maps. Cartographic functionality like label placement and map generalization will be treated. Finally, the topic of digital elevation models and algorithms will be treated. The project deals with the operationalization of a cartographic problem, where appropriate modeling of the problem is very important. After modeling, efficient algorithms will be developed.