Spatial data analysis and simulation modelling

Course goals

After completing this course the student can:

• understand concepts and apply methods of geo-spatial data analysis and geodata models
• understand concepts, design and apply models for field-based and agent-based simulation
• understand spatial error models and apply them for error propagation and for data calibration
• analyze existing studies applying spatial data analysis and simulation modeling
• choose appropriate geo-analytic and simulation models for spatial analytical tasks

Assessment

• computer practicals (successful participation and submission)
• written open book exam on theory (consisting of mandatory readings and lecture content)
• case study report on a self-selected geo-analytical problem including a literature review. Mark is given using criteria for academic research papers. The case study will be done towards the end of the course.

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.
 

Content

The first half of the course is an introduction to geo-spatial data analysis, the second half is an introduction to spatial simulation modeling.
This includes the following content:

• reference systems and transformations, geodata quality and online geodata sources
• core concepts of spatial information and geodata types, and basic geospatial transformation methods, including spatial overlay, distance-based and network-based methods (using QGIS and spatial Python libraries like Geopandas, Rasterio, Folium)
• field-based and agent-based simulation modeling (using simulation Python libraries like PCRaster and Campo)
• stochastic modeling and uncertainty
• calibration of space-time simulation models

Course form

• the course is organized as a flipped classroom. Students prepare themselves with pre-recorded lectures and reading materials on theory/models/concepts. 
• each week there is an interactive Question & Answer session to discuss prepared materials interactively.
• each week there are corresponding computer practicals that contain exercises to apply the theory to practical examples of spatial data analysis and simulation modeling. Practicals are executed independently with teaching staff support. Presence is not a requirement.
• students present a case study proposal and results to the class. 

Course material
Hardware and software: you should make sure that you have access to a computer capable of running QGIS, and on which a Conda environment can be installed with python libraries including Geopandas, Fiona, Folium, Rasterio, rasterstats, PCraster, and Campo.
As a programming interface, we recommend Visual Studio Code.
Installation instructions will be provided.

Literature

• Chrisman 2002, "Exploring Geographic Information systems", 2nd edition
• Burrough, McDonnell, Lloyd 2015, "Principles of Geographical Information Systems", 3rd edition
• Crooks, Malleson, Manley, Heppenstall 2019, "Agent-based modelling and geographical information systems"

Additional information

Enrolment in this course is only available by using the pre-enrolment form that will be forwarded to all ADSM students in the second week of September.