TY - DATA
T1 - Unpacking Dresden, data underlying the MSc research project: Applied Spatial Analytics for Sustainable Urban Development
PY - 2025/06/26
AU - Alexandre Bry
AU - Adriano Mancini
AU - Alankrita Sharma
AU - Grase Stephanie Stuka
AU - Soroush Saffarzadeh
UR - 
DO - 10.4121/48e04672-93f4-49a4-9c7b-76c57a844e24.v1
KW - Stream Restoration
KW - Spatial Analysis
KW - FAIR Data
KW - MCDA
KW - Typology Construction
KW - Urban Planning
KW - Ecological Issues
N2 - <p>More information about the context and the methodology can be found in the README.md file and online at this link: https://github.com/sdgis-edu-tud/fair-data-publication-groupf.</p><p><br></p><p>Along with the Elbe river, Dresden comprises a dense network of streams, which are spread out across its fabric. Presently, the streams are secluded from being a valuable part of the city. The problems are characterised by ecological issues, inappropriate land use by residents, and artificial channeling. They, along with the Elbe river hold potential to become elements of integrating the ecological and social functions of the city by reclaiming the historical identity of waterfronts and restoring natural habitats. Therefore, there arises a need to understand how to integrate these streams into the network of protected green areas and public spaces, while maximising their contribution to biodiversity while adapting to the risk of flooding within and around the city.</p><p><br></p><p>These concerns and identified potentials beg the question that, <strong>how can urban streams be restored and integrated in Dresden&#x27;s fabric, such that there is a synergy between human activities and the natural environment?</strong></p><p><br></p><p>This is investigated by adopting an integrated approach for <strong>biodiversity</strong>, <strong>climate adaptation</strong> and <strong>quality of life</strong>.</p><p><br></p><p>Based on the three criteria that we decided to tackle, we came up with numerical indicators that we could use to evaluate them. These numerical indicators are called attributes and have to be normalised—in our case between 0 and 1—so that they can be compared, weighted and thereafter clustered properly depending on their relevance and similarities.</p><p><br></p><p>The spatial units used in this study are hexagons with a dimension of 250 meters. The study area of Dresden is divided using a complete surface of a hexagonal pattern. Then it is overlaid with the water stream network and river body from OpenStreetMap to keep only the hexagons that intersect with at least one stream. Finally, the isolated hexagons were removed.</p><p><br></p><p>Two data-driven methods were used to conduct the analysis:</p><p><br></p><ul><li><strong>S-MCDA (Spatial Multi-Criteria Decision Analysis)</strong> — S-MCDA was used to weigh the different attributes against each other. The method supports decision-making by evaluating and ranking alternatives (the attributes) within the three objectives of biodiversity, climate adaptation and quality of life.</li><li><strong>Typology Construction</strong> — Typology construction is used to group attributes into homogenous types based on similarities. This was used to identify patterns in data and make clusters of attributes that show similarity, which can thereafter be used to understand the type of interventions which would be impactful.</li></ul><p><br></p><p>This dataset contains both the values computed for the attributes in each spatial unit and the final results of the two methods.</p>
ER -