Theses

Eine Vielzahl verfügbarer Apps soll ihre Nutzer*innen dabei unterstützen, sich auf nachhaltigere Art zu verhalten.  Dies kann zum Beispiel das Mobilitätsverhalten betreffen (Rad statt Auto), den Energiekonsum (Pulli statt Heizung) oder auch Aktivitäten (Mitwirkung bei Urban Gardening).

Ziel dieser Bachelorarbeit ist es, eine repräsentative Auswahl dieser Apps systematisch zu analysieren, um einen Überblick über die verschiedenen Arten von Apps, der Hauptthemen und -strategien zu gewinnen. Von Interesse sind insbesondere auch welche Techniken aus dem Bereich "Nudging" und "Persuasive Technology" zum Einsatz kommen.

Das Thema kann auf unterschiedliche Weise bearbeitet werden, je nach Wunsch der bearbeitenden Person, z.B. als reine Analyse basierend auf detaillierter Betrachtung der Apps, als Nutzerstudie oder einer Kombination aus beidem. Denkbar ist auch der Entwurf einer App als Mockup/Prototyp, die bestehende Schwächen von verfügbaren Apps überwindet. 

Contact: Christian Kray

Author: Felix Disselkamp

Supervisor: Chris Kray

Co-supervisor: Simge Özdal-Oktay

This idea behind this comes from Luescher and Weibel (2010) - "we might for example present a different answer to the question ‘shopping opportunities in the city centre?’ to elderly people than to young people (the former avoiding the night club district because they might perceive it unsafe)."

Despite the notion of place being widespread in natural language, it is still difficult to model it in a GIS (which is more concerned with location). The student can identify a suitable site, and attempt to study how the notion of place differs among participants of different age groups (young vs. old), residence (e.g living in the city vs. suburbs) etc. The analysis could offer insights into whether there are differences in the way a place is perceived based on these factors and has potential implications for contextualized location based services.

Contact: Imad Humayun

Author: Tobias Tresselt

Supervisor: Angela Schwering

Co-supervisor: Imad Humayun

In the WayTo research project, we developed a prototype to visualize global landmarks which are outside the map section displayed on a mobile device by visualizing them at the edge of screen to indicate directions.

An amendment to this design is to incorporate distance into the design of icons. The thesis could explore different techniques (e.g. halo (circles at the edge), wedge (triangle at the edge), or distance-encoding arrows). These methods distinguish only two or three levels (or four maximum) of icons size (that means we only have 4 different sizes of each global landmarks to indicate very close, close, far, and very far) on screen. Although there is hardly any empirical data to support that, drawing from cartographic theories regarding visual variables in animated map making, humans are not able to distinguish changes on map representation at fine levels.

Possible bachelor and master thesis could address:

1.    Implementing distance and direction indicating landmarks (DDL) display on mobile phones

2.    Investigating the effects of DDL on mobile screens on spatial orientation and spatial knowledge

3.    Comparing the variations of DDL design (gradient to true distance vs. categorical distance)

4.    Comparing the effects of DDL with Halo and Wedge design

Reference: Li, R.; Korda, A.; Radtke, M.; Schwering, A. (2014): Visualising distant off-screen landmarks on mobile devices to support spatial orientation. https://www.uni-muenster.de/forschungaz/publication/99385?lang=en

Contact: Angela Schwering

Author: x

Supervisor: Angela Schwering

Powerful methods in an area called "dynamic geometry" provide a rapid way of solving a wide range of spatial reasoning problems. For example, as a user manipulates some shapes (circles, lines, points) in an "intelligent" sketch, the sketch automatically updates itself to maintain certain spatial constraints (e.g. lines being tangent to circles, lines being parallel to each other, and so on).

In medicine these shapes could represent different types of cells that have been automatically recognised from images of a tissue section, and the task could be to determine whether the cells are cancerous (histopathology). In geographic information systems these shapes could represent streets, buildings, and landmarks recognised from satellite images or directly from a hand-drawn sketch.

In this thesis project you will be:

• extending the spatial language of dynamic geometry to work with common-sense qualitative spatial relations (near, left of, inside, etc.)
• integrating dynamic geometry into a knowledge representation (artificial intelligence) framework

You will then use this enhanced technology for a range of exciting tasks, such as:

• improving computer-based image recognition by automatically correcting errors based on knowledge about objects in the domain
• providing new ways of interacting with complex data using "intelligent" sketches

Through this research project you will develop skills and experience in the application of methods in artificial intelligence (AI). You will be introduced to the necessary tools and existing projects to build on. No prior experience with methods in AI is necessary (you will be given considerable support in this area).
 

Contact: Carl Schultz

Author: Nikolai Gorte

Supervisor: Angela Schwering

Co-supervisor: Carl Schultz

"Learning by induction" is the ability to take a number of observations or examples and discover rules that account for the observations - it's the ability to generalise from examples.

Being able to generalise spatial patterns from observations is essential for artificial intelligence systems to perform a variety of tasks in a flexible and robust way. We don't want to tell the computer system exactly how to solve every specific problem it will encounter. Instead, we want to give it some examples and have the system use common sense and background knowledge to figure out ways of solving new problems that have a similar structure.

In this thesis project you will be:

(a) integrating a new powerful "common sense" spatial reasoning method called Enhanced Geometric Constraint Solving within a more general artificial intelligence framework for inductive spatial learning;

(b) evaluating the system in a range of exciting applications in geographic information science, architectural design, cognitive psychology (analogical reasoning), and medicine (histopathology).

Through this project you will develop skills and experience in the application of methods in artificial intelligence (AI). You will be introduced to the necessary tools and existing projects to build on. No prior experience with methods in AI is necessary (you will be given considerable support in this area).
 

Contact: Carl Peter Leslie Schultz

Author: Lars Syfuß

Supervisor: Edzer Pebesma

Co-supervisor: Carl Schultz

A major task in environmental science is to obtain spatially comprehensive data from limited field samples (e.g. climate stations, soil profiles, vegetation records,...). This is often done using machine learning algorithms that learn the relationships between field data and remotely sensed predictor variables (e.g. from satellites). The developed model is then used to make spatial predictions for the entire area of interest (i.e. create a "map" of the variable of interest).
Such a map is only valuable when the error of the model is known. The error assessment however causes major difficulties for models with spatial dependencies and cause standard validation methods to fail. There is increasing consent in literature that spatial validation is necessary and several strategies have been proposed (e.g. Roberts 2018, Meyer 2018, Valavi 2018, Brenning 2012). However, little research is done on how different strategies compare, which however is important to know for model comparisons and for finding the "right" validation strategy for a dataset.
This project aims at comparing and (as far as possible) evaluating different validation strategies for machine learning based spatial mapping of environmental variables. Usually in most projects the "true" performance is hard to assess due to the fact that the reference data are limited. Therefore we want to adress the problem using reference data that are available in a spatially continuous way hence providing a continuous reference. Such data are rare but a possible research task would be available in the field of rainfall monitoring: The RADOLAN dataset contains high quality rainfall data from a radar network in 1km spatial resolution and serves as an excellent reference set. We then want to model rainfall for Germany using data from the the geostationary satellite sensor MSG SEVIRI and auxiliary predictor variables such as elevation (see Kühnlein 2014 for the idea of mdoelling rainfall based on MSG SEVIRI).
Using the RADOLAN data we simulate different sampling designs (hence we simulate raingauges in different constellations: random, clustered,...) and train machine learning models to predict rainfall from MSG SEVIRI in a spatial way. Different strategies for spatial model evaluation are then compared.

Requirements:

R programming is an advantage, interest in working with machine learning algorithms

References:

Brenning, A. (2012): Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest. IEEE International Geoscience and Remote Sensing Symposium.

Kühnlein, M., T. Appelhans, B. Thies, and T. Nauss, 2014: Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests. Journal of Applied Meteorology and Climatology, 53 (11), 2457–2480.

Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software 101: 1-9.

Roberts, D. R., V. Bahn, S. Ciuti, M. S. Boyce, J. Elith, G. Guillera-Arroita, S. Hauenstein, J. J. Lahoz-Monfort, B. Schröder, W. Thuiller, D. I. Warton, B. A. Wintle, F. Hartig, and C. F. Dormann, 2017: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, doi:10.1111/ecog.02881.

Valavi R, Elith J, Lahoz‐Monfort JJ, Guillera‐Arroita G. blockCV: An r package for generating spatially or environmentally separated folds for k‐fold cross‐validation of species distribution models. Methods Ecol Evol. 2018;00:1–8. https://doi.org/10.1111/2041-210X.13107
 

Contact: Hanna Meyer

Author: Marc Dragunski

Supervisor: Hanna Meyer

Co-supervisor: Edzer Pebesma

Data cubes are an efficient representation for spatiotemporal data as from Earth observation satellites. By multidimensional chunking, they allow highly parallel execution of complex analysis such as time series change detection. The aim of the thesis is to create an architecture that allows for scaling such operations in a distributed computing environment using containerized processing (Docker) and tools for container orchestration such as Kubernetes. A prototypical implementation as extension to the gdalcubes library shall be developed and used for a detailed analysis of the scalability of the proposed architecture.

Contact: Marius Appel

Author: Maria Hidalgo

Supervisor: Edzer Pebesma

Co-supervisor: Marius Appel