## 6 Empirical Results  
To demonstrate the analysis using KPIs, 57 KPIs for European countries for the year 2021 were considered. The KPI list is not limited to those in Table 1 but is supplemented with additional KPIs, some of which are shown in Table 4.  
Figure 4: Comparative analysis of urban planning KPIs for Milan, Rome, and Italy across four sustainability dimensions. Figure 3: Number of KPI-recorded years for each city’s country. Different types of analysis could be applied to KPIs. [PERSON] et al. [17] demonstrated the use of such techniques to support policymakers. In this section, clustering techniques are used. This clustering enables policymakers and other stakeholders to make more informed and targeted decisions, optimizing resources and efforts to achieve a positive and sustainable impact. Machine learning offers a variety of clustering algorithms, such as k-means, hierarchical clustering, and DBSCAN. Clustering algorithms assign observations to clusters while minimizing the Euclidean distance between the cluster's observations and the centroid. The k-means clustering algorithm starts with the random selection of \(k\) centroids, representing the centers of the initial clusters [35]. Then, it reassigns each observation to the cluster with the closest centroid during each iteration. Subsequently, the centroids are recalculated as the mean of all observations within each cluster. This iterative process continues until the centroids converge, i.e., when they do not show significant changes in successive iterations, thus ensuring the optimality of the data partitioning [36]. In addition to k-means, hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN) are widely used clustering methods. On the one hand, hierarchical clustering builds a hierarchy of clusters either through an agglomerative (bottom-up) or divisive (top-down) approach, which is particularly useful when the number of clusters is not known a priori [37]. On the other hand, DBSCAN identifies clusters based on areas of high density and is particularly effective in discovering clusters of arbitrary shapes while also handling noise in the data [38]. All clustering experiments were conducted using Python version 3.11. The implementation of k-means, hierarchical clustering, and DBSCAN was carried out with standard libraries available in Python's machine learning ecosystem. After applying the three algorithms to the dataset, it was observed that the clustering results were identical across all methods, with each algorithm returning the same cluster assignments. Given this consistency in the results, it was decided to present only the k-means results in this paper, as they are representative of the overall clustering performance. This study applies the aforementioned clustering algorithm to a dataset that includes socioeconomic and environmental indicators from several European countries. This approach allows the identification of homogeneous clusters, facilitating the analysis of patterns and trends within each group. Incremental principal component analysis (PCA) was applied to the data to reduce the data dimensionality. PCA helps to identify the main influencing factors by extracting relevant characteristics and eliminating redundant parameters that do not contribute significantly to decision-making [39]. This technique improves the analysis by focusing on the variables with the greatest impact. This study revealed that most of the variability in the data was captured in the first four principal components. For visualization purposes, the dimensionality was reduced to two components. This reduction helps to represent the clusters in a two-dimensional space clearly. To carry out the analysis of which KPIs have most influenced the construction of the clusters, the importance of each