## 3 Results
### LISA and Hot-Spot Analysis  
Once the existence of geostatistical patterns in the spatial distribution of the phenomenon had been verified, the level of numerical correlation between the different indicators of territorial anthropization and the spatial distribution of the damages derived from the latest DANA was studied. This evaluation was carried out by taking into account an evolutionary approach in the 1956-2016 timeframe to analyze the incidence of each of the indicators of territorial transformation in the global anthropization process. Subsequently, the level of statistical correlation of each of those anthropic subphenomena with the damage caused by floods was assessed. For this second analysis, we used the LISA statistic and a bivariate evaluation of hot and cold spots. The analysis of bivariate spatial statistical correlation between the damage index \(I_{EDS}\) and the territorial anthropization indices is shown in Table 5. As can be seen, the three anthropization indicators show a positive correlation with the damage indicator, with higher values being observed for the cases of agricultural and urban land transformations than for the execution of infrastructures. Figure 10: (**a**) Overlapping of the simulated drafts through the flow network of the existing theoretical model in the watershed and the flow network observed in the DANA of 13 September 2019 (above). Detail of said contrast for a theoretical simulation with (**b**) T = 100 years (bottom left) and (**c**) T = 500 years (bottom right). Data sources: National Floodplain Cartography System (Ministry of the Environment of Spain, [42]) and Sentinel 2 satellite. When we delved deeper into the previous spatial analysis through an ordinary least squares (OLS) regression model of the relationship between the three levels of damage established and the three anthropization indicators, we obtained the results shown in Table 6.  
Finally, if we parameterize the values obtained to the numerical level in a two-dimensional LISA analysis of hot and cold spots from a spatial perspective, we can observe the clearly differentiated areas in the watershed, as shown in Figure 11. The spatial values \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Damage Level**} & \multicolumn{4}{c}{**Minor Damage**} \\ \cline{2-5}  & **B** & **Std. Error** & **t** & **Sign.** \\ \hline \(I_{LID}\) & 0.265 & 0.003 & 6.687 & 0.000 * \\ \(I_{SA}\) & 0.107 & 0.005 & 2.245 & 0.000 * \\ \(I_{ATO}\) & 0.202 & 0.011 & 1.710 & 0.000 * \\ \hline \end{tabular} \begin{tabular}{c c c c} \hline \multirow{2}{*}{**Damage Level**} & \multicolumn{4}{c}{**Relevant Damage**} \\ \cline{2-5}  & **B** & **Std. Error** & **t** & **Sign.** \\ \hline \(I_{LID}\) & 0.136 & 0.003 & 3.026 & 0.000 * \\ \(I_{SA}\) & 0.215 & 0.009 & 2.937 & 0.000 * \\ \(I_{ATO}\) & 0.179 & 0.010 & 2.109 & 0.000 * \\ \hline \end{tabular} \begin{tabular}{c c c c} \hline \multicolumn{5}{c}{Number of observations: 817} \\  & **AIC: 23,762.1** \\  & **Multiple R-squared: 0.22** \\  & Adjusted R-squared: 0.22 \\  & F-statistic: 71.04 Prob (>F) (3,6) DF: 0 \\ \hline \end{tabular} \
\begin{tabular}{c c c c} \hline \multicolumn{5}{c}{**Catastrophic Damage**} \\ \cline{2-5}  & **B** & **Std. error** & **t** & **Sign.** \\ \hline \(I_{LID}\) & \(-\)0.017 & 0.004 & 1.102 & 0.000 * \\ \(I_{SA}\) & 0.087 & 0.010 & 1.434 & 0.000 * \\ \(I_{ATO}\) & 0.117 & 0.012 & 1.811 & 0.000 * \\ \hline \end{tabular}
\begin{tabular}{c c c c} \hline \multicolumn{5}{c}{Number of observations: 138} \\  & **Akaike\({}^{\prime}\)s** information criterion (AIC): 21,038.5 \\  & Multiple R-squared: 0.24 \\  & Adjusted R-squared: 0.23 \\  & F-statistic: 32.69 Prob (>F) (3,6) degrees of freedom: 0 \\ \hline \end{tabular}
\end{table} 
Table 6: Detailed multiple regression models (OLS) for LISA bidimensional analysis of the different levels of damage.