Unpacking Dresden, data underlying the MSc research project: Applied Spatial Analytics for Sustainable Urban Development

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.<br>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.<br>These concerns and identified potentials beg the question that, <strong>how can urban streams be restored and integrated in Dresden's fabric, such that there is a synergy between human activities and the natural environment?</strong><br>This is investigated by adopting an integrated approach for <strong>biodiversity</strong>, <strong>climate adaptation</strong> and <strong>quality of life</strong>.<br>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.<br>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.<br>Two data-driven methods were used to conduct the analysis:<br><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.<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.<br>This dataset contains both the values computed for the attributes in each spatial unit and the final results of the two methods.

有关研究背景与方法论的详细信息，请参阅README.md文件，或访问以下在线链接：https://github.com/sdgis-edu-tud/fair-data-publication-groupf。
德累斯顿境内除易北河（Elbe River）外，还遍布着密集的溪流水系，贯穿城市全域。当前，这些溪流尚未被充分利用，未能成为城市的宝贵组成部分。现存问题主要包括生态缺陷、居民土地使用不当以及人工渠化改造。依托这些溪流与易北河，通过重塑滨水区历史风貌、恢复自然栖息地，有望将其打造为整合城市生态与社会功能的核心要素。因此，亟需探索如何将这些溪流融入城市防护绿地与公共空间网络，在最大化其对生物多样性（biodiversity）贡献的同时，适配城市内外的洪涝风险。
基于上述关切与已识别的研究潜力，本研究提出核心问题：<strong>如何对德累斯顿的城市溪流进行修复与整合，以实现人类活动与自然环境的协同共生？</strong>
本研究围绕<strong>生物多样性（biodiversity）</strong>、<strong>气候适应（climate adaptation）</strong>与<strong>生活品质（quality of life）</strong>三大目标，采用整合式研究方法展开探究。
基于确定的三大评估准则，本研究构建了用于量化评估的数值指标，这些指标被称为属性（attributes）。所有属性需进行归一化处理，本研究中将其归一化至0至1区间，以便进行横向比较、权重赋值，并依据相关性与相似性进行合理聚类。
本研究采用250米边长的六边形作为空间分析单元。首先以六边形格网全覆盖德累斯顿研究区域，随后叠加来自开放街道地图（OpenStreetMap）的溪流水系与河道图层，仅保留与至少一条溪流相交的六边形格网，最终移除孤立的六边形单元。
本研究采用两种数据驱动方法开展分析：
<strong>S-MCDA（空间多准则决策分析，S-MCDA）</strong> — S-MCDA用于对不同属性进行权重赋值，通过在生物多样性、气候适应与生活品质三大目标框架内评估并排序备选属性（即指标），为决策提供支撑。
<strong>类型学构建（Typology Construction）</strong> — 类型学构建依据相似性将属性归类为同质类型，用于识别数据内在模式，将具有相似特征的属性聚为类群，进而明确可产生实效的干预措施类型。
本数据集包含各空间单元的属性计算值，以及上述两种分析方法的最终结果。