Course content

Advanced knowledge of GIS-based methods and tools used for supporting integrated land use and transport planning analysis. Focused on network modelling and other methodology to describe accessibility for different modes of transport. Special emphasis is placed on analyzes and models that support planning for non-motorized modes of transport and public transport.

Learning outcome

Knowledge: The functionality of network models and network graphs in geographic information systems (GIS), used for supporting integrated urban land use and transport planning. Network data sources and reference systems, cartography and cartographic methods, relevant to the needs and scope of accessibility planning. Data structures, data models and analysis techniques. Also knowledge of raster models used for transport purposes ("path distance" tools). Skills: Facilitation of network data for various transport modes in a GIS, including transit networks with schedule data. Preparation for and implementation of network creation and analysis. Implementation of raster analysis. General competence: to make use of acquired knowledge and skills solving a variety of tasks related to accessibility analysis and linking transport and land use planning.

Learning methods and activities

Lectures. Instructor-led exercises. Individual exercises. Individual assignment.

Required previous knowledge

Basic knowledge about geographic information systems (GIS), and skills in using GIS software. The candidate must have passed the examination in either AAR4841, AAR4847, TBA4240, GEOG1012 or similar courses.

Course materials

Compendium and/or textbook will be announced before the beginning of the course. An additional syllabus consisting of articles and/or reports will be determined in consultation with the candidate.

It is advised, but not required, to use private PC for the exercises. The exercises will be relatively heavy tasks in GIS software (ArcGIS, ArcGIS Pro, QGIS), thus the following requirements specification is recommended:

Processor: Intel minimum i5, preferably i7 or xeon. AMD Ryzen 5, 7 or 9.

64 bit operating system (Windows 10)

At least 8 GB RAM, preferably 16 GB

Storage capacity HD or SSD (preferred) min. 500 GB

Dedicated video card of at least 4 GB, Nvidia or AMD

Students who do not have a PC that follows this specification will be allowed to use local data labs.

Credit reductions