Pump track analysis

Supervisor

Assoz Prof Dr Hermann Klug (hermann.klug@plus.ac.at)

Short description

The given topic is related to spatial analysis in the domain of mountainbiking. Within mountainbike sports, pump tracks became increasingly popular and have recently been established in many regions. Among them are two local pump tracks; one in Wals Siezenheim and one in Koppl. In Koppl, a recent drone flight has been organised to capture the pumptack in very high-resolution pictures. The numerous pictures should be used to create a very high-resolution digital elevation model (cm resolution) with e.g. Agisoft PhotoScan. Afterwards, the pump track should be analysed according to spatial parameters. A representation as a representative 3d model accessible via a web browser would also be an option.

Objectives

• To parametrise a pumptrack based on drone made pictures
• To examine the pumptrack setup geomorphologically
• To analyse the parameters e.g. using eCognition rulsets or other technologies
  
• To extract the parameters via an automated workflow
• To ensure transferability of analysis to other pumptracks (e.g. the one in Wals Siezenheim)
• To establish a 3D model virtually accessible via a browser interface

Hypothesis

• Spatial relations and related topology matter in pump track design
• Automated processing of DEM with eCognition is possible for parameterisation

Research questions

• What is the surface sealed area measured in 2D versus 3D?
• Assuming that 20 cm of asphalt is used for the pump track design; what is the total volume of asphalt needed to build up the pump track?
• Pump tracks are a sequence of waves with valleys and peaks; what is the distance between two valleys and/or two peaks?
• Are these distances between two valleys and/or two peaks changing with the relative height of a wave?
• Is there a particular relation between the distance between two valleys and/or two peaks and their relative height?
• Are there deviations from this relation on the course of the established pump track in Koppl?
• What are the horizontal and vertical radiuses applied to the pump track? Are they all the same or do they vary within the overall course?
• For sport enthusiasts

o   Hypothesis: The ideal riding line on a pump track depends on parameters like bike wheel/bike size (e.g. 29"), riding speed, radius/curvature, bike angle to the underground, and centrifugal forces. This riding line can be retrieved automatically from a digital elevation model (DEM) using eCognition rulesets!

o   Question: What is the ideal riding line at maximum speed on the pump track with a 29" bike?

• Before mentioned questions are based on a cm resolution DEM; what would be the minimum (coarser) spatial resolution required to perform above-mentioned analysis? Is there a particular spatial (threshold) resolution, at which analysed parameters are changing significantly?

Preconditions

Interest in (automated) raster based spatial analysis and solid knowledge in geoinformatics. Particular interest should be available in working with aerial pictures and digital elevation models. Knowledge with Agisoft PhotoScan and eCognition would be good, but is not mandatory.

Planned start

Any time

Accessibility analysis of urban green and blue space

Suggested by: Martin Loidl

Short description: The positive functions of ecosystems-services (ES) and nature-based solutions (NSB) regarding livability, health and climate effects are undisputed and therefore at the core of cities’ strategies to adapt to global warming, compact urbanization, risk of respiratory disease and obesity. However, aspects of social justice are hardly explicitly considered in sustainable urban development strategies.
In a first step, we are interested in analysing the access of every household in an exemplary city (or several cities) to green and blue space. Moreover, we want to learn whether the access is evenly distributed or spatially clustered. These insights can then be further contextualized in the socio-economic landscape of the city.

Research for this master thesis should address one or more of the following questions (but is not limited to):

• Which measures are suitable for analysing accessibility to green and blue space?
• How to design an index that represents one or more aspects of accessibility at various scale levels (from single household to entire city)?
• How to detect and quantify spatial patterns of accessibility?
• Is there a correlation between socio-economic status and accessibility to green and blue space?
• What are unintended rebound-effects of providing nature-based solutions, such as green gentrification etc.?
  

References, suggested reading:

• Labib, S. M., Lindley, S., & Huck, J. J. (2020). Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review. Environmental Research, 180, 108869. https://doi.org/10.1016/j.envres.2019.108869
  
• Ekkel, E. D., & de Vries, S. (2017). Nearby green space and human health: Evaluating accessibility metrics. Landscape and Urban Planning, 157, 214-220. https://doi.org/10.1016/j.landurbplan.2016.06.008
  
• Reyes-Riveros, R., Altamirano, A., De La Barrera, F., Rozas-Vásquez, D., Vieli, L., & Meli, P. (2021). Linking public urban green spaces and human well-being: A systematic review. Urban Forestry & Urban Greening, 61, 127105. https://doi.org/10.1016/j.ufug.2021.127105
  
• Anguelovski, I., Connolly, J., Cole, H., Garcia-Lamarca, M., Triguero-Mas, M., Baró, F., Martin, N., Conesa, D., Shokry, G., Pérez del Pulgar, C., Argüelles Ramos, L., Matheney, A., Gallez, E., Oscilowicz, E., López Máñez, J., Sarzo, B., Beltrán, M., Martinez Minaya, J. (2022). Green Gentrification in European and North American Cities. Nature Communications, 13. https://doi.org/10.1038/s41467-022-31572-1

Start/finish: anytime

Prerequisites/qualifications: Interest in planning and mobility research as well as in advanced spatial (network) analysis. Data management and analysis skills are ultimately required. Scripting and coding skills are benefitial.

 

Micro-economic Innovation Indicators in Web Data

Suggested by: Bernd Resch

Short description:

The location pattern of any industry is the product of a large number of individual decisions. Industrial location analysis investigates these location decisions and seeks to detect location determinants that trigger and influence such decisions. These determinants are generally referred to as location factors. A thorough understanding of the impact of location factors on firms’ location decisions and firm performance can have important implications for stakeholders. Managers and entrepreneurs can integrate valuable information into the decision-making process when choosing the location of a new venture. Some location factor-firm relationships which are relevant at the macro level (aggregate) may not be so at the micro level (ecological fallacy). Suitable data for microgeographic analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI) and the increasing availability of official (open) geodata.

Kinne and Resch (2018) combined open geodata, Volunteered Geographic Information (VGI), and a comprehensive firm dataset (the Mannheim Enterprise Panel - MUP) containing approximately three million firm observations to empirically estimate the relationship between a set of location factors and the number of local software firms in Germany (see figure).  They concluded that the microgeographic level of analysis provided new insights into the firm site selection process. However, they also pointed out the particular requirements to the statistical model and the data employed in a microgeographic location analysis, like the need for high resolution geodata, which was not available in all domains. They showed that this problem was most severe in cities, which often feature segregated populations and districts with very different socio-economic profiles. In the context of this master thesis, the research conducted by Kinne and Resch in Germany shall be extended to the geo-economic context of the USA, where higher-resolution socio-economic geodata are available. Furthermore, a comparison between the results for Germany and the USA shall be carried out.

Literature:

Kinne, Jan und Bernd Resch (2018), Analyzing and Predicting Micro-Location Patterns of Software Firms, ISPRS International Journal of Geo-Information 7, 1. http://www.mdpi.com/2220-9964/7/1/1/pdf

Arifi, D., Resch, B., Kinne, J. and Lenz, D. (2023) Innovation in Hyperlink and Social Media Networks: Comparing Connection Strategies of Innovative Companies in Hyperlink and Social Media Networks. PLOS ONE, 18(3), pp. e0283372, DOI: https://doi.org/10.1371/journal.pone.0283372.

 

Rammer, Christian, Jan Kinne und Knut Blind (2019), Knowledge Proximity and Firm Innovation: A Microgeographic Analysis for Berlin, Urban Studies.

Start date: ASAP

Prerequisites/qualifications: experience with analysing web data and social media, interest in economic geography/economics, OpenStreetMap, Regression analysis (optional)