Dataset for the Spatial Clustering of German Municipalities: Techno-Economic and Socio-Structural Indicators

This repository contains the dataset and the resulting spatial clusters of German municipalities developed within the research project "OptiEE" (Optimization of potential analyses for renewable energies by integrating socio-psychological parameters). The goal of this data processing step was to categorize all populated municipalities in Germany into distinct region types (clusters) based on 23 techno-economic and socio-structural indicators. This spatial structuring serves as the foundation for mapping acceptance parameters and conflict risks regarding renewable energy expansion across different landscape types. The clustering was performed using a two-step approach: hierarchical agglomerative clustering (Ward's method) to determine the optimal number of clusters, followed by a partitioning K-Means algorithm (k=9). Data Structure and Files: The Excel file includes the following datasheets: 01_cluster_input (Raw Input Data): Contains the aggregated, raw socio-structural and techno-economic indicators for the German municipalities used as the baseline for the clustering process. 02_cluster_converted (Pre-processed Data): The transformed dataset used for the K-Means algorithm. To ensure mathematical model assumptions, the raw data underwent a Box-Cox transformation (to approximate normal distribution) and a subsequent z-standardization to prevent variables with large absolute values from dominating the distance metrics. 03_kmeans_var_means (Variable Means / Variances): Contains the statistical variance and mean values of the underlying input variables, providing transparency on the data distribution before and after the pre-processing phase. 04_kmeans_cluster_means (Cluster Centroids): The final cluster means (centroids) for the 9 identified region types. This sheet shows the average characteristics of each cluster across all standardized indicators, allowing for a detailed interpretation of the specific spatial profiles (e.g., dense urban areas vs. rural agricultural regions). Context and Funding: This dataset was created at the Chair for Wind Power Drives (CWD) at RWTH Aachen University. The OptiEE project is funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the funding code 03EI5253.