ReBioClim Dresden: Spatial analysis dataset for urban stream restoration

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.<br>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.<br><strong>Interactive Map Dashboard</strong>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.<br>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.<br><strong>How to use the dashboard</strong>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:<br>Prerequisite: Python must be installed on your computer.Open your command prompt/terminal and navigate to the project directory, e.g.: <strong>cd C:\Users\daans\Documents\GitHub\report-asa2025-groupe</strong>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.Open your web browser and navigate to <strong>localhost:8000/interactive%20map/</strong><br>More information, methodologies, etc. can be found in both the attached README and in the following GitHub repository, which also contains our code.https://github.com/sdgis-edu-tud/report-asa2025-groupe

本数据集为德国德累斯顿市提供了生物多样性、生活质量与气候适应指标的定量空间数据，采用500米×500米的网格系统进行组织。主数据集命名为"Grid_BIO_CLI_QOL.gpkg"，是一份地理数据包（GeoPackage），包含698个网格单元，所有上述指标均作为属性字段存储，坐标参考系统（CRS）为EPSG:25833。该单一空间图层的数据仅存储于一张数据表中，无外键关联关系。

本数据集的核心用途是通过对环境与社会因素开展多准则分析，为城市溪流修复项目的空间决策提供支撑，具体而言，即识别并优先排序溪流网络中最适合开展城市溪流修复的区段。本数据集整合了市政数据集的GIS空间分析、卫星遥感植被指数计算，以及定量的多准则评估框架所生成的数据。

<strong>交互式地图仪表盘</strong>
配套附带一份基于Leaflet的交互式Web地图（index.html），该地图使用了与地理数据包（GeoPackage）属性与数值完全一致的GeoJSON格式数据（GeoJSON），仅将网格的坐标参考系统转换为WGS 84坐标系（WGS 84）。在仪表盘内，用户可查看任意单一地图图层，也可叠加任意组合的图层，并结合多准则决策分析（S-MCDA）中提供的各图层重要性/权重进行可视化。例如叠加2个生物多样性图层与1个生活质量图层，以探索数据间的关联与模式。为支持该灵活可视化功能，仪表盘搭载了三变量分级统计图例，可实现任意组合图层的可视化展示。

此外，用户可点击任意500米×500米网格区块，查看展示该特定位置所有属性值（取值范围为0至1）的柱状图。该工具对于深入分析与科学决策极具价值，可辅助优先确定首批修复的溪流区段，并明确各网格区块所需开展的具体改进措施。

<strong>仪表盘使用方法</strong>
遗憾的是，受跨域资源共享（CORS）限制，用户无法直接通过打开HTML文件启动仪表盘。不过搭建临时本地服务器的操作十分简便：
前提条件：计算机需已安装Python。打开命令提示符/终端并切换至项目目录，例如：<strong>cd C:UsersdaansDocumentsGitHub
eport-asa2025-groupe</strong>
在同一终端窗口运行<strong>python -m http.server</strong>或<strong>python3 -m http.server</strong>，具体命令取决于你的Python安装版本。该命令将启动由终端托管的临时本地服务器。打开网页浏览器并访问<strong>localhost:8000/interactive%20map/</strong>

更多信息、方法论等内容可参见附带的README文件，以及下述GitHub仓库，该仓库同时包含我们的代码：https://github.com/sdgis-edu-tud/report-asa2025-groupe