## 4 Materials and Methods
### Classification of Strategies into Profiles  
For the systematic analysis of multidimensional data sources, such as the data available from the strategies framed in the MA, various approaches have been used for decades, among which the paradigm of artificial neural networks (ANN), coming from the field of Artificial Intelligence (AI), stands out as a data mining technique which is clearly heuristic [82], and specifically the Self-Organizing Maps (SOM) is noteworthy as a type of this that stands out for its power for variable analysis as opposed to classical linear methods and especially for its representations [83]. The ANN is a type of machine learning method that has been used profusely in the prediction, classification and recognition of patterns [22], highlighting the SOM for their ability, from non-ordered data, to create profiles and representations as a landscape of the phenomenon described by the data [25]. SOMs together with K-means are the most popular clustering methods [82]. Some authors attribute better results to the former, adding the advantage of presenting topological relationships graphically between data [84], which is what the methodology was initially created for [21]. SOMs have been widely used in engineering, among others to pre-process data, extract properties, process analysis or for pattern recognition [23]; its own author, [PERSON], used it to obtain patterns and structures of world poverty and welfare [24], opening the methodology seminally to the social sciences and geography. Gradually, the SOM methodology has been used in a constructive way in several areas close to urban \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**County**} & \multirow{3}{*}{**EU28**} & \multicolumn{3}{c}{**Local Adaptation Strategies (LAS)**} & \multirow{3}{*}{**Mayons Adapt Initiative’s Local Adaptation of Good Practices (MA-GP)**} \\  & & & & **([PERSON] et al., 2018)** & & & **(Ma-GP)** & **of Total** & **Population** & **County** \\  & & **(LAS)** & **\& MA** & **(MA-GP)** & **LAS** & **of MA** & **Included** & **Population** & **Population** \\ \hline Belgium & \(\bullet\) & 2 & 0 & 6 & 6 & 7.14 & 2,703,803 & 11,209,420 & 24.04 \\ Bulgaria & \(\bullet\) & 0 & 0 & 2 & 2 & 2.38 & 270,941 & 7,177,991 & 3.77 \\ Croatia & \(\bullet\) & 1 & 0 & 1 & 1 & 1.19 & 10,500 & 4.203,604 & 0.25 \\ Czech Rep. & \(\bullet\) & 3 & 0 & 0 & 0 & - & - & - \\ Denmark & \(\bullet\) & 9 & 1 & 4 & 3 & 4.76 & 775,222 & 5,683,483 & 13.64 \\ Finland & \(\bullet\) & 2 & 0 & 0 & 0 & - & - & - \\ France & \(\bullet\) & 12 & 1 & 2 & 1 & 2.88 & 825,000 & 66,538,391 & 1.24 \\ Germany & \(\bullet\) & 24 & 4 & 10 & 6 & 11.90 & 4,397,711 & 8,679,769 & 5.38 \\ Greece & \(\bullet\) & 3 & 0 & 4 & 4 & 4.76 & 169,108 & 10,820,883 & 1.56 \\ Hungary & \(\bullet\) & 11 & 0 & 0 & 0 & - & - & - \\ Iceland & \(\bullet\) & 0 & 1 & 1 & 1.19 & 120,000 & 330,815 & 36.27 \\ Ireland & \(\bullet\) & 2 & 0 & 1 & 1.19 & 1,660,000 & 4,643,740 & 35.75 \\ Italy & \(\bullet\) & 3 & 1 & 16 & 15 & 19.05 & 1,896,945 & 607,582 & 3.12 \\ Latvia & \(\bullet\) & 3 & 0 & 1 & 1.19 & 990,90 & 197,5727 & 0.50 \\ Macedonia & \(\bullet\) & 0 & 0 & 1 & 1.19 & 3,892 & 2,078,453 & 1.83 \\ Netherlands & \(\bullet\) & 2 & 0 & 2 & 2.28 & 320,000 & 16,939,923 & 1.89 \\ Norway & 4 & 0 & 0 & 0 & - & - & - & - \\ Portugal & \(\bullet\) & 28 & 6 & 12 & 6 & 14.29 & 1,144,440 & 10,350,076 & 11.02 \\ Romania & \(\bullet\) & 1 & 0 & 0 & - & - & - & - & - \\ Slovakia & \(\bullet\) & 5 & 1 & 1 & 0 & 1.19 & 422,932 & 5,423,801 & 7.80 \\ Spain & \(\bullet\) & 7 & 3 & 11 & 8 & 13.10 & 5,995,902 & 46,443,994 & 12.91 \\ Sweden & \(\bullet\) & 7 & 1 & 2 & 1 & 2.38 & 960,000 & 9,798,186 & 10.16 \\ Turkey & 0 & 0 & 1 & 1 & 1.19 & 478,500 & 78,665,300 & 0.61 \\ U. Kingdom & \(\bullet\) & 20 & 6 & 6 & 0 & 7.14 & 4,503,920 & 65,128,861 & 6.92 \\ \hline EU28 & 149 & 24 & 84 & 60 & 100 & 26,099,398 & 489,874,329 & 5.33 \\ \hline Total & & & & & & & & 509,507,762 & 5.12 \\ \hline \hline \end{tabular}
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