Annotation

Standalone course validated by the micro-credential (paid course)

1. Introduction and infrastructure
2. Spatial data (geopandas)
3. Spatial relationships (libpysal)
4. Exploratory spatial data analysis (esda)
5. Point patterns (pointpats)
6. Clustering (scikit-learn)
7. Interpolation (tobler, pyinterpolate)
8. Regression (statsmodels, gwlearn)
9. Spatial evaluation (scikit-learn)
10. Space in modelling (scikit-learn)

Location

online (link bude zaslán všem přihlášeným / online, the link will be sent to all registered users

Results of learning

After finishing the course, students will be able to:

• Describe advanced concepts of spatial data science and use the open tools to load and analyze spatial data.
• Explain the motivation and inner logic of the main methodological approaches of open SDS.
• Critically evaluate the suitability of a specific technique, what it can offer, and how it can help answer questions of interest.
• Apply several spatial analysis techniques and explain how to interpret the results in the process of turning data into information.
• Work independently using SDS tools to extract valuable insight when faced with a new dataset.

Contact for applicants

martin.fleischmann@natur.cuni.cz