## 4 Materials and Methods
### Classification of Strategies into Profiles

Table 2: Comparison between the case studies of [PERSON] et al., 2018 [18] and the Mayors Adapt (MA) initiative’s local Good Practices [19]. Distribution of case studies by country and population and population coverage of the Mayors Adapt initiative’s local Good Practices [19]. Source of population data, World Bank [81]. and spatial planning, such as the analysis of urban systems [85], the identification of urban dispersion dynamics [26], the characterization of urban fabrics [27], the evaluation of economic and financial conditions over time [28], the characterization of exemplary European neighborhoods by means of profiles [29] or in assisting decision-making by means of digital government tools [86]. The SOMs use unsupervised training and therefore require little operator intervention, making them very useful in the exploratory and knowledge discovery phases [87]. From the data of the study, called the input layer, which in our case are the features of the selected local adaptation initiatives, their organization is obtained by means of a representation in a two-dimensional map of each local initiative, maintaining topological relations and similarity with the rest of initiatives, locating those instances that present features with greater similarity among themselves in the map. The construction of the SOM incorporates all the information obtained from the variable files (Table 1) that can be compared with the rest of the initiatives, managing a multitude of entities that have to be coded from a contextual and semantic analysis of the available data. Qualitative variables, making up most of the information obtained, are not directly incorporated into the SOM as numerical values [88], but the categories are coded by means of dummy variables, i.e., representing in a binary way the presence or absence of each quality. In this work, the software Viscovery SOMine 5.0.2.t. was used for the construction of the SOM model, chosen for its excellent visual representations [28]. Once the SOM model is created, clusters of local adaptation initiatives are defined through a Ward-Cluster analysis [89]. The number of profiles to be reached can be defined by means of different methods and well-differentiated criteria [90], and even the use of several of them at the same time is frequent [91]. There are methods to define the number of clusters or profiles with a statistical approach, using cohesion and/or separation metrics, generally based on dispersion measurements based on the sum of squares [92], with the Ball and Hall index [93] or Calinski and Harabasz [94], the Davies-Bouldin index (DB) [95], the Silhouette Coefficient [96], the Cubic Clustering Criterion (CCC) [91] or the dendrogram observation method [91] particularly worth highlighting. A complementary or alternative to statistical approaches is the a priori method of [PERSON] et al. [97], who proposed the establishment of an adjusted range of clusters based on the researcher's experience and on manageability, simplicity and efficiency, taking into account a practical judgment based on common sense and technical foundations. This criterion tends to finally consolidate an adjusted range based on the experience of the researcher, which is estimated to allow the interpretation of the clusters. Finally, by means of a practical judgment, based on theoretical foundations and common sense, the final number of profiles is determined, usually resulting in a better solution than one based exclusively on statistical criteria [97]. In view of the above, and uniquely due to the exploratory and heuristic nature of the work, it is considered pertinent to restrict the solution of the number of profiles to a conceptual criterion of the problem, reaching a number of profiles for which a useful and relevant interpretation of local adaptation strategies can be made.