My name is Franz-Benjamin Mocnik. I am a visiting researcher at the Bremen Spatial Cognition Center at the University of Bremen, and a Twin-Fellow at the Hanse-Wissenschaftskolleg, Institute for Advanced Study.

My research addresses the understanding of physical and social reality in respect to space and time by structural theories, as well as the analysis of space and time by formulating structural laws. I currently aim at improving visualizations of spatial and temporal data by understanding the data's structural properties, for example by the use of networks and algebraic structures.

I have finished my PhD studies at the Research Group Geoinformation at the Vienna University of Technology under the supervision of Andrew U. Frank. (You are welcome to read my thesis.) Before I moved to Vienna in 2012, I was part of the Münster Semantic Interoperability Lab (MUSIL) in the Institute for Geoinformatics (ifgi) at the University of Münster, where I was supervised by Werner Kuhn.


If you need a visualization of GTFS transit feeds, you are welcome to have a look at my open source visualization tool gtfs2graph.


My scientific interests include topics from different fields: information science, spatial information science, mathematics, 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:

Which properties have space and time, and how do they impact spatial information?
Space and time have a simple and uniform physical structure, amongst others influencing spatial information. How can we detect such a structure in spatial information? How can we model spatial information?

Which structural properties do maps expose?
Representations differ in many structural aspects: Which aspects of information can be represented inherently? Which assumptions are implicitly assumed for a representation? etc. By understanding structural properties of maps, and contrasting it with properties of other types of representations, we can understand how certain types of information, for example spatial information, can be represented.

How can visualizations of spatial and temporal data be improved by exploiting the data's structure?
A good understanding of the data's structure and the structural opportunities offered by representations, in particular visualizations, can be used to improve the representation and to flexibly adapt it to the represented data. How can, for example, public transport networks dynamically and task-dependendly be generalized, by using structural properties of the networks and the tasks?

How can we formalize social and physical processes simultaneously in space and time?
Space and time affect us in many ways resulting in a multitude of concepts. As social processes intermesh with physical ones, concepts have to be compatible in order to formalize such processes. Yet, many concepts are, at least in parts, incommensurable. How can we gain formalizations that describe many aspects appropriately?

How can we find universal laws for spatial information?
Tobler's first law of geography is one of very few examples of universal laws that we know of in spatial science. Which additional laws exist, and how do they reflect the statistical nature of the data? Which concepts and theories in mathematics and physics can be reused in spatial information science?

How can we handle the advancing amount and the increasing heterogeneity of information?
Big data, i.e. extensive and highly heterogenous data, confronts us with many questions: How can we represent such information? How can we process data of this complexity? How can we develop suitable algorithms based on the properties of space and time? How can we visualize big data?

How can we model spatial information using mathematical and physical concepts?
Theories discussing structures (i.e. relations between entities) rather than the entities itself have turned out to be more stable under the evolutionary process of building and modifying theories, as is discussed in structural realism. Category theory and other formal approaches have contributed to the success of mathematics and physics, because they describe strong structures. Spatial information is, in many cases, affected by uncertainty and exposes only weak structures. How can we nevertheless use formal approaches to describe spatial information?

My toolbox to tackle these questions includes algebra, category theory, networks and graphs, data science, spatial reasoning, ontologies, linked (open) data technologies, information visualisation, and many more.


A Scale-Invariant Spatial Graph Model PhD Thesis
Franz-Benjamin Mocnik
Vienna University of Technology, 2015
Modelling Spatial Structures Conference Paper
Franz-Benjamin Mocnik, Andrew U. Frank
Proceedings of the 12th Conference on Spatial Information Theory (COSIT), 2015, pages 44–64
Observatories, Laboratories and Experiments in Geographical Information Science Workshop Short Paper
Franz-Benjamin Mocnik
2nd Workshop on Geographic Information Observatories (GIO), 2015
A Literary Map of Turkey Workshop Paper
Lisa Maria Teichmann, Franz-Benjamin Mocnik
Proceedings of the Workshop on Data-driven Research in the History of Southeast Europe and Turkey, 2015
Modelling Spatial Information Conference Short Paper
Franz-Benjamin Mocnik
Proceedings of the 1st Vienna Young Scientists Symposium (VSS), 2015, pages 46–47
MapReduce Principle for Spatial Data Conference Short Paper
Franz-Benjamin Mocnik
Extended Abstract Proceedings of the 8th International Conference on Geographic Information Science (GIScience), 2014, pages 100–103
Bogomolovs Zerlegungssatz für Calabi-Yau-Mannigfaltigkeiten Diploma Thesis
Franz-Benjamin Mocnik
University of Bonn, 2010


Continuation of Doctoral studies in Geoinformation – final grade: sehr gut, passed with distinction
Thesis: A Scale-Invariant Spatial Graph Model
Advisor: Andrew U. Frank
Research Group Geoinformation at the Vienna University of Technology
Diploma in Mathematics (minor subject: Physics) – final grade: sehr gut
Thesis: Bogomolovs Zerlegungssatz für Calabi-Yau-Mannigfaltigkeiten
Advisor: Daniel Huybrechts
Mathematical Institute at the University of Bonn
Pre-diploma in Physics (minor subject: Astronomy) – final grade: sehr gut
Department of Physics and Astronomy at the University of Bonn



GIS Theory (tutor, lecture by Andrew U. Frank), fall 2014
Mobile GIS-Applications (tutor, lecture by Andrew U. Frank), fall 2013
Teaching activities for school students for GI@Schoool, fall 2012, spring 2012, fall 2011
Analysis IV (tutor, lecture by Werner Müller), spring 2010
Analysis III (tutor, lecture by Werner Müller), fall 2009
Linear Algebra II (tutor, lecture by Werner Ballmann), spring 2009
Linear Algebra I (tutor, lecture by Werner Ballmann), fall 2008
Algebra I (tutor, lecture by Daniel Huybrechts), spring 2008
Groups, Rings, Moduls (tutor, lecture by Michael Rapoport), fall 2007
Linear Algebra II (tutor, lecture by Daniel Huybrechts), spring 2007
Linear Algebra I (tutor, lecture by Daniel Huybrechts), fall 2006
Analysis IV (tutor, lecture by Jens Franke), spring 2006
Analysis III (tutor, lecture by Jens Franke), fall 2005
Analysis II (tutor, lecture by Jens Franke), spring 2005


Dipl.-Math. Dr.rer.nat. Franz-Benjamin Mocnik
Bremen Spatial Cognition Center
University of Bremen
Enrique-Schmidt-Straße 5
28359 Bremen, Germany
office room 3.46