Data underlying the MSc research project: Human-nature connectedness of the Teplica stream of Senica

This dataset contains quantitative data on <strong>urban stream restoration of the Teplica River in Senica, Slovakia</strong>, as part of the research project <em>Human-Nature Connectedness of the Teplica Stream in Senica</em>.In this research, a<strong> </strong>combined approach across three themes —Biodiversity, Quality of Life, and Climate Adaptation—was used to assess the current condition of the Teplica stream.For this case study:The river was divided into <strong>stream segments at 200-meter intervals</strong>.In each segment, different criteria were measured to determine the current situation, using <strong>buffer zones of varying radii around the stream</strong>, depending on the specific criteria measured.Two data-driven methods were applied:<strong>Spatial Multi-Criteria Decision Analysis (S-MCDA). </strong>S-MCDA was used to <strong>weigh and compare the different measured criteria</strong> within the objectives of biodiversity, climate adaptation, and quality of life. This method supports decision-making by evaluating and ranking the criteria to identify priority areas for intervention.<strong>Typology Construction. </strong>Typology construction, using the k-clustering means, was used to <strong>group criteria into homogenous clusters based on similarities</strong>, allowing the identification of patterns within the dataset. These clusters help to understand which types of interventions would be most impactful within specific segments of the Teplica stream.In this dataset both the units of measure and the criteria measured can be found.

本数据集包含斯洛伐克塞尼察市特普利察河（Teplica River）城市河道修复的量化研究数据，相关研究项目为《塞尼察特普利察河人与自然连通性研究》（Human-Nature Connectedness of the Teplica Stream in Senica）。
本研究采用覆盖生物多样性、生活质量与气候适应三大主题的综合研究方法，对特普利察河的现状开展评估。本次案例研究中，研究人员将河道按200米间隔划分为若干河道区段（stream segments at 200-meter intervals）。针对每个区段，研究人员将依据具体待测指标的要求，以河道周边不同半径的缓冲带（buffer zones of varying radii around the stream）为研究范围，开展多项指标的现状测定。
本研究采用了两类数据驱动方法：其一为空间多准则决策分析（Spatial Multi-Criteria Decision Analysis, S-MCDA），该方法可在生物多样性、气候适应与生活质量三大研究目标框架下，对各项实测指标进行权重赋值与对比分析；通过对指标进行评估与排序，能够识别优先干预区域，为决策制定提供支撑。其二为类型学构建法（Typology Construction），该方法借助k均值聚类法（k-clustering means），基于指标间的相似性将其划分为同质聚类，从而识别数据集内的分布模式。此类聚类结果有助于明确针对特普利察河不同区段的最优干预类型。
本数据集涵盖了本次研究中所用的测量单位与各项实测指标。