%0 Generic
%A Schlosser, Daan
%A Bastiaansen, Joost
%A Chandrasekaran, Aparnaa
%A van Dijk, Teun
%D 2025
%T ReBioClim Dresden: Spatial analysis dataset for urban stream restoration
%U 
%R 10.4121/3035126d-ee51-4dbd-a187-5f6b0be85e9f.v1
%K urban stream restoration
%K biodiversity
%K climate adaptation
%K quality of life
%K spatial analysis
%K urban planning
%K Dresden
%K multi-criteria analysis
%K GIS
%X <p>This dataset provides quantitative spatial data on biodiversity, quality of life, and climate adaptation metrics for Dresden, Germany, structured in a 500x500m grid system. The primary dataset, named "Grid_BIO_CLI_QOL.gpkg," is a GeoPackage containing 698 grid cells with all these metrics as attributes (CRS: EPSG:25833). The data within this single spatial layer is contained in one table, and no foreign key relationships exist.</p><p><br></p><p>The overarching purpose of this dataset is to support spatial decision-making for urban stream restoration projects through multi-criteria analysis of environmental and social factors. Specifically, to find and prioritise which parts of the stream network are most suitable for urban stream restoration. The dataset was developed using a combination of GIS-based spatial analysis of municipal datasets, satellite-derived vegetation indices, and a multi-criteria evaluation framework, all quantitative in nature.</p><p><br></p><p><strong>Interactive Map Dashboard</strong></p><p>There is also an interactive web leaflet map (index.html) attached, which used the attached GeoJSON, which contains the same attributes and values as the GPKG, only the used CRS for the grid itself is converted to WGS 84. Within the dashboard, you can view any individual map layer, or you can overlay any combination of layers with their importance/weights in mind, as provided in our S-MCDA analysis. For example, 2 BIO layers and 1 QOL layer, to find relationships and patterns. To support this flexibility the dashboard features a trivariate choropleth legend, so any combination of layers can be visualised.</p><p><br></p><p>Additionally, you can click on any 500x500m grid tile to display a bar chart showing all attribute values (ranging from 0 to 1) for that specific location. This tool is especially valuable for deeper analysis and informed decision-making, helping prioritise which stream sections should be restored first and identifying the specific improvements needed for each tile.</p><p><br></p><p><strong>How to use the dashboard</strong></p><p>Unfortunately, due to CORS restrictions, you cannot open the dashboard by simply opening the HTML file. Fortunately, setting up a temporary local server is quite straightforward:</p><p><br></p><ol><li>Prerequisite: Python must be installed on your computer.</li><li>Open your command prompt/terminal and navigate to the project directory, e.g.: <strong>cd C:\Users\daans\Documents\GitHub\report-asa2025-groupe</strong></li><li>Run <strong>python -m http.server </strong>or <strong>python3 -m http.server</strong> in the same terminal window, depending on your Python installation. This starts a temporary local server hosted by the terminal.</li><li>Open your web browser and navigate to <strong>localhost:8000/interactive%20map/</strong></li></ol><p><br></p><p>More information, methodologies, etc. can be found in both the attached README and in the following GitHub repository, which also contains our code.</p><p>https://github.com/sdgis-edu-tud/report-asa2025-groupe</p>
%I 4TU.ResearchData