Third Test

Third Test#

Please hand in your results by 2024-04-14 18:00.

Group

People

SDG / Topic

01

Simon, Phillip H.

Public Transport

02

Phillip T., Joel

Bicycle network analysis

03

Theo, Tim

tbd

04

Max, Tobi

Wasserversorung in informellen siedlungen

05

Clara, Hanna

SDG 6: Education

06

Vincent, Sebastian

Quality Education in the Alps

07

Maxi, Celina

SDG 11: accessibility public spaces

08

Johanna, Matthias

tbd

09

Lars, Amir

Müll / Toiletten / Ärzte

10

Lara, Melissa

Energieversorgung

Motivation#

Monitoring and achieving the United Nation’s Sustainable Development Goals (SDGs), e.g. “urban” SDG 11 (“Make cities and human settlements inclusive, safe, resilient and sustainable.”), can face challenges especially in the low-income countries where the data needed for the proposed SDG indicators are usually scarce [Sun et al., 2020]. There is an estimated gap of $1 billion USD globally in funding for national statistical offices, and consequently, baseline geospatial data that should be provided by these agencies are often not accessible, not up-to-date or not available in standard formats [Altay and Labonte, 2014, Braunschweig et al., 2020]. Tackling data scarcity requires moving beyond insufficient traditional data sources to utilizing new, non-traditional sources for measuring the SDGs [Fritz et al., 2019].

It has been shown that open data communities — such as OpenStreetMap (OSM) — are not only promising, but already contribute to filling existing data gaps [Fritz et al., 2019, Herfort et al., 2021, Scholz et al., 2018]. In addition to contributions by individual volunteers (mappers), there is an intensifying trend that organized corporate and humanitarian mapping communities contribute to OSM in general [Anderson et al., 2019, Herfort et al., 2021]. OSM is now used widely for applications such as web maps and navigation services at and data about buildings from OSM has been used in domains such as urban planning [Milojevic-Dupont et al., 2020], SDG monitoring [Van Den Hoek et al., 2021], disaster management [Scholz et al., 2018], public health [Bhatia et al., 2018, Yeboah et al., 2021], as well as during the COVID-19 pandemic [Marco Minghini et al., 2020].

Your Task#

Select a SDG goal and use data from OSM and other sources to run a geographic analysis to support it’s monitoring. Write a report about your analysis (~3000 words).

Provide some background information (use scientific references) on how geospatial data can help to monitor this SDG goal. Propose a geographic analysis using OpenStreetMap data and (at least) one other dataset. Pick two or more location for which you will run your analysis. Discuss for each location to what extent OSM data is fit-for-purpose to run the analysis. Run the analysis and describe (and compare) the results for each location and also provide information on the limitations of your analytical approach and datasets.

Grading#

Formalities (25 Points):#

  • language (e.g. sentences are complete, not too long, easy to understand, no spelling mistakes)

  • using scientific references in a correct way

  • tables, figures and maps etc. have a caption and are referred to in the text

  • length: 2500-3500 words

Introduction (15 Points):#

  • the relevance of the topic is described and demonstrated using scientific literature

  • a research question or analysis goal is phrased out

  • overview on the structure of the text

Main (45 Points):#

  • method description

  • description of datasets and OSM data quality aspects

  • result description and comparison for locations in regard to the research question / analytical goal

  • result visualisation and presentation (e.g. figures, maps, tables)

Discussion and Conclusion (15 Points):#

  • critical discussion of the results in regard to the methodological approach

  • discussion of potential consequences of the results, lesson’s learned