## 3 Resources and Techniques
### Techniques
#### 3.2.2 Machine Learning Applied to Data Analysis  
In this section, the relevant aspects of the two machine-learning techniques used for data analysis are described, with the rationale for using these techniques, as well as details of the implementation, selected.  
* Decision Tree Learning: This supervised classification method classifies instances through a sequence of questions related to the value of one of the features describing the pattern. The learning consists of building a set of questions that can be represented as a decision tree, hence the name. The goal is that each question can be used not only to describe an instance but a set of them within the same class ensuring generalisation. This structure makes the decision tree an easily explicable machine-learning algorithm.  
The rationale for the decision taken is in line with [5; 6]. Decision trees are used to highlight core features and their interactions, describing each type of STP based on experimental and actual data. This technique enables the exploration of different feature sets that can explain with accuracy the STP categories. Despite the limitations of decision tree learning, such as not working well in cases where decision boundaries are not aligned with the data axis [7], its non-parametric nature makes it an attractive technique in the absence of assumptions about the properties of the data. Another potential drawback is they are prone to overfitting when the ratio of features to instances is high. To alleviate this problem, strategies were used, such as constraining the tree's depth or allowing pruning. Different subsets of the features were evaluated.  
The optimised version of the CART algorithm [8; 9] implemented in scikit-learn was used. This technique has been applied to different fields such as risk management and green building, e.g., [10; 11].  
* Cluster Analysis: K-means [12; 13; 14] was used, a well-known non-parametric technique for cluster analysis that has numerous fields of application such as healthcare, coronavirus, and urban hotspots, e.g., [15; 16; 17]. In k-means, each instance is assigned to its nearest centroid, which represents an average of the instances in the cluster. The centroids are updated iteratively until no instance changes its cluster. It may not be well-suited if the expected structure is other than spheric clusters. Not being tied to a specific algorithm, the suitability issue can be addressed by evaluating the results, identifying signals that show the algorithm is not performing as expected and proceeding to study alternatives. Different values for the number of clusters (K) and the silhouette score (s) were used to evaluate the cluster quality. Features were min-max scaled before analysis.  
This approach was used to shed light on the structure of the data in contrast to the expert annotation, study the importance of the key indicators, and highlight possible anomalies. The scikit-learn implementation of the k-means was leveraged.  
The study presents a knowledge-based theoretical framework supported by a robust dataset and a practical interface dashboard [3] that enables the visualising of different resources and techniques to allow an exploratory and critical analysis, together with intrinsic evaluation. This analysis permits the empirical contrast of the proposed objectives, including the confirmation of different types of STPs in Spain and the determination of their core features.