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）的城市河流修复**相关量化数据，该数据隶属于研究项目《塞尼察特普利察河的人与自然连通性》（Human-Nature Connectedness of the Teplica Stream in Senica）。本研究采用涵盖生物多样性、生活质量与气候适应三大主题的复合评估路径，对特普利察河的现状开展系统性评估。针对本案例研究，研究人员将该河流划分为**200米间距的河段单元**；在每个河段单元中，研究人员依据具体测量指标的要求，以河道周边**不同半径的缓冲带（buffer zones）**为调查范围，开展各项指标的测量以明确河段现状。本研究应用了两类数据驱动型分析方法：其一为**空间多准则决策分析（Spatial Multi-Criteria Decision Analysis，S-MCDA）**，该方法用于在生物多样性、气候适应及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**，通过对指标开展评估与排序以识别优先干预区域，为修复决策制定提供支撑；其二为**类型学构建（Typology Construction）**，研究采用k聚类方法（k-clustering means），基于指标间的相似性将实测指标划分为**同质聚类簇**，从而挖掘数据集内的分布模式。此类聚类簇有助于明确在特普利察河的特定河段中，何种类型的修复干预措施可产生最优效益。本数据集涵盖所有测量单位及实测指标的相关信息。