Be able to map conceptual spatial relations (topological and geometrical) to the body of analytical methods.
Recognize the value of different metrics in the spatial as well as attribute domains (e.g. fuzzy algebra).
Describe shape characteristics of spatial features as well as complex landscape structures with the aim of diagnosing change.
Identify, select (including SQL clauses) and statistically describe spatial features based and their distance to and/or topological relations with a target feature.
Estimate values of a continuous (real or thematic) surface based on sample points through interpolation methods.
Select adequate interpolation methods (based on characteristics of surface theme, measurement level, sample density) and assess quality of results.
Derive characteristics of continuous surfaces as a basis for advanced models.
Develop and adequately parameterized basic models of surface runoff, groundwater dynamics, visibility, solar irradiation and diffusion / spreading over inhomogeneous surfaces.
Apply topological relations for combination of spatial themes (overlay analysis), derive and implement weighting schemes.
Find best routes (paths across fields and networks.
Allocate areas and features to service centres, distinguish from (‘optimal’) location analysis.
Choose classification and regionalization methods according to specific requirements and contexts.
Design, implement and validate complex workflows and process models built from individual methods and operations.
Move from data analysis to generation of context-specific information and the creation of higher level domain knowledge.
