## 4 Outcome  
The AHP multi-criteria aggregation rules, as implemented by MASCOT, allowed for the mapping of the distribution of UES per function. The results of the applied approach focused on:  
* Assessing the multi-functionality levels per MMU of 25 hectares (500 \(\times\) 500 m cell size, depending on the average dimension of the Naples districts; the overall dimension of the focus area, which is a multiple of the square cell; and the PC computational power);
* Visualising the spatial distribution of services by applying the Euclidean distance method;
* Planning scenarios for the spatial implementation of UES by considering the degree of suitability per MMU. Table 2 shows the minimum (Min), maximum (Max), and standard deviation (St. Dev.) values of UES function for each municipality within the focus area. A comparison of the data highlights that the Municipality of Naples had the maximum value of services for all three categories. In the case of Regulation functions, while it had the highest value, the standard deviation was also high, which implies deviation from the very significant average value (i.e., the areas with high regulative ecosystem values are interspersed with more urbanised areas with low levels of green features). Moreover, it is interesting to observe that the Municipality of Bacoli, which borders Naples city, had a very high value for the Regulation functions, with a slightly lower standard deviation, in comparison with Naples. This implies a more even distribution of such services within its boundaries. All the other municipalities had much lower values. The following figures show the 3D UES mapping results. The Regulation functions were evenly spatially distributed over the overall investigation area, but higher values can be detected in the south-western zones of the city, as shown in Figure 6. Figure 7 highlights the peaks of Carrier functions as being in the downtown of Naples. In these zones, transportation and tourism facilities are the densest, while the suburban areas are lacking in these types of facilities. Figure 5: Excerpt of the 3D city model with the UES grid overlapped. The spatial relationships among building z-values and Regulation Services cell values are visualised. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Municipality**} & \multicolumn{4}{c}{**Regulation Function**} & \multicolumn{4}{c}{**Carrier Function**} & \multicolumn{4}{c}{**Information Function**} \\ \cline{2-10}  & **Min** & **Max** & **St. Dev. ** & **Min** & **Max** & **St. Dev. ** & **Min** & **Max** & **St. Dev. ** \\ \hline \multirow{2}{*}{Acerra} & 0.000 & 0.460 & 0.104 & 0.000 & 0.298 & 0.051 & 0.000 & 0.142 & 0.036 \\  & 0.000 & 0.372 & 0.105 & 0.000 & 0.271 & 0.078 & 0.015 & 0.298 & 0.084 \\  & 0.008 & 0.259 & 0.057 & 0.004 & 0.214 & 0.062 & 0.120 & 0.343 & 0.064 \\  & 0.127 & 0.987 & 0.184 & 0.003 & 0.178 & 0.044 & 0.042 & 0.202 & 0.034 \\  & 0.000 & 0.255 & 0.061 & 0.000 & 0.272 & 0.063 & 0.000 & 0.188 & 0.044 \\ Calvizzano & 0.000 & 0.476 & 0.130 & 0.042 & 0.206 & 0.053 & 0.044 & 0.133 & 0.022 \\  & 0.064 & 0.361 & 0.084 & 0.080 & 0.232 & 0.037 & 0.152 & 0.213 & 0.017 \\  & 0.000 & 0.372 & 0.095 & 0.005 & 0.115 & 0.028 & 0.025 & 0.116 & 0.022 \\  & 0.012 & 0.296 & 0.084 & 0.004 & 0.252 & 0.076 & 0.121 & 0.250 & 0.030 \\  & 0.017 & 0.268 & 0.074 & 0.119 & 0.259 & 0.040 & 0.253 & 0.387 & 0.037 \\  & 0.000 & 0.462 & 0.108 & 0.007 & 0.260 & 0.075 & 0.025 & 0.315 & 0.091 \\  & 0.004 & 0.324 & 0.092 & 0.050 & 0.164 & 0.029 & 0.135 & 0.253 & 0.032 \\  & 0.000 & 0.221 & 0.066 & 0.001 & 0.261 & 0.083 & 0.055 Figure 8 highlights the comparison among the UES values per function by overlapping the z-values of the grid. The three coloured gradients represent Regulation (green), Carrier (blue), and Information (red) services. The north-eastern areas showed low values (or a lack of them) for Carrier and Information functions, while moderate values for Regulation functions balanced this gap.