Monday, July 16, 2018

Participatory modeling of malaria risk with system dynamics - integrating the spatial-temporal dimension regions

Suggested by:Christian Neuwirth, Stefan Kienberger

Short Description

In this master thesis you will develop a dynamic simulation model of malaria risk for in Eastern Africa. The malaria system is made up of interrelations and feedbacks among a number of environmental and social factors. For instance, human beings get infected by mosquitos and vice versa, and are part of the malaria cycle. Additionally, societal and human conditions, such as poverty, awareness and education, influence human malaria risk. Your task will be to identify that causal relations and feedbacks in close collaboration with experts in this field. Based on this knowledge you will develop a system dynamics model to investigate the dynamic behavior of this system. One of the key objectives will be the analysis of spatio-temporal patterns of malaria risk in this region.

References, suggested reading:
Flessa, S., 1999. Decision support for malaria-control programmes–a system dynamics model. Health Care Manag. Sci. 2, 181–191.

Kienberger, S., Hagenlocher, M., 2014. Spatial-explicit modeling of social vulnerability to malaria in East Africa. International Journal of Health Geographics, 13(29), http://dx.doi.org/10.1186/1476-072X-13-29

Start: ASAP

Prerequisites/qualification:
interest in simulation modeling and systems science, experience in system dynamics modeling (successfully finished “Spatial Simulation” course) is a plus, commitment to actively participate in a research project

Questions and further ideas can be send to stefan.kienberger@sbg.ac.at and christian.neuwirth@sbg.ac.at.

Tuesday, July 3, 2018

Habitat model of grouse

Suggested by: Susanne Reichhart (ANL), Gudrun Wallentin

Background

European grouse populations have their main habitats at the fringe of forest and grasslands in Scandinavia and the Alps. Today, large parts of the original habitat is affected through land-use change and anthropogenic disturbance. Protected areas thus are of great importance in the preservation of viable grouse populations.

In the Eastern Alps, the transboundary Natura 2000 bird sanctuary "Karwendel" with a total area of 900km2 plays a key role in the preservation of large habitats with the required structural diversity. Currently, a EU project investigates the grouse populations in the Karwendel protected area. The aim of the project is to model the fine-scaled habitat suitability of the two grouse species black grouse and capercaillie. Existing models end at the respective national border and thus hinder an evaluation of the habitats in a larger context.  

Description

This thesis will model the suitability of the habitats in the transboundary Natura 2000 area "Karwendel" for black grouse and capercaillie. The methodological approach is species distribution modelling (SDM), which is a family of statistical and machine learning methods that map ecological niche affordances of a species to geographic space. The thesis project involves 1) identification of adequate parameters that describe habitat suitability for each of the two species, 2) data harmonisation to integrate German and Austrian data, and 3) identification of an adequate algorithm to model habitat suitability. The Karwendel habitat suitability map will be the basis for the implementation of a biodiversity monitoring scheme and forest management measures to improve the overall habitat quality.

References, suggested reading
  • Elith, Jane, and John R. Leathwick. "Species distribution models: ecological explanation and prediction across space and time." Annual review of ecology, evolution, and systematics 40 (2009): 677-697.
  • Schweiger, Anna-Katharina, Ursula Nopp-Mayr, and Margit Zohmann. "Small-scale habitat use of black grouse (Tetrao tetrix L.) and rock ptarmigan (Lagopus muta helvetica Thienemann) in the Austrian Alps." European Journal of Wildlife Research 58.1 (2012): 35-45.
  • Zohmann, Margit, et al. "Modelling habitat use of Tetrao urogallus L. in Austria for conservation issues." Journal for nature conservation 22.3 (2014): 223-234.

Project context
EU-Interreg Project in cooperation with TUM, LWF, Land Tirol, Land Salzburg and ANL: https://www.anl.bayern.de/projekte/basch_projekt/index.htm

Prerequisits
Interest in wildlife management and nature conservation.

Wednesday, November 15, 2017

Riparian forest habitat structure by hierarchical class modelling - Donauauen

Suggested by: Barbara Riedler, Stefan Lang

Background

Riparian forests are among the most complex ecological systems and most important biodiversity hotspots worldwide, but are at the same time amongst the most threatened European ecosystems. Management of such protected areas requires detailed knowledge about the occurring habitats. Earth Observation (EO) data can thereby compliment in-situ data and support the monitoring of protected areas through their advantage of area-wide coverage, overcoming the problem of accessibility and the option of regular, cost-efficient updates. Habitat classification based on species classification, their composition and age classes were applied for a variety of vegetation types using different EO data. Such habitat classification and modelling can be the basis for further analyses such as the assessment of habitat quality, its conservation status, monitoring of biodiversity or the evaluation of conservation measurements like water restoration projects.

Short description

The aim of the MSc thesis is to automatically classify habitats of the riparian forest of the Donauauen, Austria using very high resolution (VHR) satellite imagery. Previous work in the Salzachauen showed that single tree species classification based WorldView-2 imagery is possible using an OBIA (object based image analyses) approach. Based on such a single tree detection, habitat modelling following different habitat classification schemes (Habitats directive, EUNIS) leads to area-wide coverage and harmonized trans-boundary results of habitat classification, which in turn can be the basis for the assessment of habitat quality. As every riparian forest has its own characteristics (e.g. different tree species composition, age structure, water regime,….) the transferability of existing work and rulesets has not been proven and should be implemented in this work.
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Literature
  • Strasser, T., Lang, S., Riedler, B., Pernkopf, L., Paccagnel K., 2014, Multiscale Object Feature Library for Habitat Quality Monitoring in Riparian Forests, Geoscience Remote Sens. Lett. IEEE, 559-563.
  • Riedler, B., Pernkopf, L., Strasser, T., Lang, S., Smith, G., 2015, A composite indicators for assesing habitat quality of riparain forests derived from Earth observation data, Int. J. Appl. Earth Observ. Geoinf. 37, 114-123.
  • Riedler, B., Lang, S., in press, A spatially explicit model of habitat quality, integrating spatio-structural indicators, Ecological Indicators, http://dx.doi.org/10.1016/j.ecolind.2017.04.027

Start
any time

Prerequisites / qualification

basic knowledge in e-cognition / OBIA
interest and knowledge in ecology / botany is helpful


Investigation of weather and bicycle counting data

Suggested by: Martin Loidl, Bernhard Zagel

Short description: Bicyclists are more exposed to temperature, precipitation and wind than public transit passengers or car drivers. Thus, we can hypothesize that weather and seasonal effects impact the number of bicyclists. In a previous study we investigated the attitude of bicyclists toward winter cycling (see http://gimobility.sbg.ac.at/paper_uploads/umfrage_winterradfahren.pdf for a report in German language). We are now interested in the spatial, temporal and spatio-temporal correlations between weather and bicycle counting data. Moreover, causalities that explain potential correlations should be investigated through surveys and/or qualitative research.
We can provide data from permanent counting stations in Salzburg in close to real-time and weather data in very high resolution. Alternatively, available data from other cities can be used as wel. The master thesis project is aimed to be integrated in the research efforts of the GI Mobility Lab.

References, suggested reading:
  • BÖCKER, L., DIJST, M. & PRILLWITZ, J. 2013. Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review. Transport Reviews, 33, 71-91.
  • ORCORAN, J., LI, T., ROHDE, D., CHARLES-EDWARDS, E. & MATEO-BABIANO, D. 2014. Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events. Journal of Transport Geography, 41, 292-305.
Start/finish by: Anytime

Prerequisites/qualifications: Profound skills in data management and processing. Statistical knowledge.

Evidence-based planning and optimization of bicycle infrastructure

Suggested by: Martin Loidl, Bernhard Zagel

Short description: The provision of adequate infrastructure is crucial for promoting active mobility, especially bicycling. Consequently, cities that aim to increase the modal share of bicyclists are planning to build and improve dedicated bicycle infrastructure. However, the positive effect of these efforts can only be gained when the infrastructure is connected and embedded into a seamless network. In contrast to this “ideal world vision”, decisions in infrastructure planning and building are often made based on pragmatic reasons and personal/subjective judgments.
Geographic information systems can help to provide an evidence base for infrastructure planning and optimization. For this the GI Mobility has already developed an indicator-based assessment model, which allows quantifying the quality of the road space in terms of bicycle safety and comfort. Based on this model a workflow should be conceptually developed and tested that consists of status-quo analysis, suggestions for corridors and network hierarchies as well as of effect estimation. The model interaction and the communication of results should preferably be implemented in a web environment.
Additionally to the model and pre-processed road data, we provide highly detailed socio-demographic data. Linking this research to the current FamoS (development of a high resolution bicycle flow model) project is possible and appreciated.

References, suggested reading:
Start/finish by: Anytime.

Prerequisites/qualifications: Skills in data management and analysis as well as in scripting/programming are required. Experiences from any planning domain are advantageous.

Personalized and adaptive bicycle and/or pedestrian routing

Suggested by: Martin Loidl, Bernhard Zagel

Short description: Routing applications for public transit and cars consider road users as “machines”. Sometimes this approach is transferred to routing applications for bicyclists and pedestrians. In doing so, the personal abilities and preferences (which usually play a role for motorized road users) are largely neglected. Common routing applications optimize routes for average bicyclists and pedestrians. However, this average user doesn’t exist and the range of preferences is huge (compare a bike courier with a child on a bicycle for instance). In this master thesis research models and routing environments should be developed that account for individual preferences of users and have the ability to adapt to specific situations (e.g. heavy rain). The identification of a suitable approach (multi-criteria routing, model-based impedance manipulation, consideration of user feedback etc.) is subject to research.

References, suggested reading:
  •  PRIEDHORSKY, R., PITCHFORD, D., SEN, S. & TERVEEN, L. 2012. Recommending routes in the context of bicycling: algorithms, evaluation, and the value of personalization. ACM 2012 conference on Computer Supported Cooperative Work. Seattle, Washington, USA: ACM.
Start/finish by: Anytime.

Prerequisites/qualifications: Skills in data management and analysis (especially network analysis) as well as in scripting/programming are required.

MSc: Destroyed villages monitoring using Copernicus Fire service, OSM data and VHR satellite imagery

Suggested by: Lorenz Wendt, Stefan Lang, Martin Sudmanns

Background

The aim of the EO4HumEn+ project is to develop and demonstrate innovative applications for satellite image interpretation and GIS to meet the diverse information needs of the humanitarian community. One task is to detect, to quantifiy, and to qualify the destroying of human settlements in conflict situations, as an indicator for population displacement. The current Rohingya crisis in Myanmar and Bangladesh impressively shows how the relation between settlement destroyment and people movement may look like.

Short description
This tool combines locations of villages, from OSM, global urban footprint or other sources like the Bill and Melinda Gates data from Nigeria with alarms from the Copernicus EFFIS system, which detects fires. Then, it selects cloud-free before and after images from S-2, and makes an automatic analysis for burnt areas at a better spatial resolution than EFFIS (with SemEO tools) . If there is an overlap, the system could then check for available VHR imagery from commercial providers. As test data, we have village points from Darfur via MSF and AI, from Myanmar (HRW), or the point data of villages of Nigeria could be used, although I do not know if burning down villages occurs there.

Project context
EO4HumEn+ (Extended EO-based Services for Dynamic Information needs in Humanitarian Action)

User organisation(s)
Médicins Sans Frontières (MSF), Amnesty International (AI), Human Rights Watch (HRW), and other NGOs active in the humanitarian domain

Start
any time

Prerequisites / qualification
Keen to collaborate in the GeoHumanitarian Action team at Z_GIS, passionate in using geospatial technologies for the humanitarian domain, interest in integrating EO data with other geospatial data sources, and using web-based processing tools.