My scientific interests include topics from different fields, among them geography, information and geographical information science, mathematics and physics, data visualization, cognitive science, and philosophy of science. This plurality has highly influenced my way of thinking and allows me to carry over methods between different fields of science. I am interested in finding answers to the following questions:

How to include fuzzy and individual aspects into geographical representations?
Formal concepts of space are so far limited by their unavoidable rigorousness. Fuzzy and vague information can in their most general sense so far not be included into more complex reasoning processes. This is why representations of individual and thus idiosyncratic geographies, if yet existent, cannot be fully included in a more general framework.

How do people conceptualize place?
The rather narrow notion used in GIS does not leave any place for individual and more complex representations of place. How can the existing notions of space, time, and theme be unified for obtaining a handy and expressive notion of place? How to even include novel representations of space that are used in human thinking? Answers will guide the way to ‘platial information systems’.

How to bridge representations of social and physical processes in the geographical domain?
Social processes intermesh with physical ones. The representations we use for social processes differ though in many ways from their physical counterparts, rendering these concepts, at least in parts, incommensurable. A common understanding of the underlying concepts of both the social and the physical domain will allow for a better understanding of how they are intermeshed.

Which universal laws exist for geographical information?
Tobler's first law of geography is one of very few and well-known examples of universal laws within the domain of geographical information science. Which additional laws and notions describe geographical information universally? What is the structural essence of what guides extensive geospatial datasets? An answer will help establishing novel concepts that are applicable to geographical information in a wide range of applications.

How can we handle the increasing amount and heterogeneity of information?
Big data, i.e., extensive and highly heterogenous data, confronts us with many issues regarding their meaning, their processing, and their visualization. Structural knowledge will enable the better interpretation of the data and, in consequence, their searchability and usability.

Which mathematical and physical concepts can be carried over to geographical information?
Theories discussing structures (i.e., relations between entities) rather than the entities themselves have turned out to be more stable under the evolutionary process of building and modifying theories, as is discussed in structural realism. Formal approaches including category theory and calculus of varations have enormously contributed to the success of mathematics and physics, because their descriptions are based on strong structures. Can such concepts be carried over to geographical information despite its often vague and less rigorous character? Being able to do so would help establishing a more stable and universal theory of geographical information science.

My toolbox to tackle these questions includes, besides methods from geography, also more formal methods, among them algebra and category theory, networks and graphs, methods from data science, spatial reasoning, ontologies, linked (open) data technologies, cartographic methods, and methods from information visualisation.