## 4 Test Results  
We implemented our framework and all the spatial analysis and geoprocessing functions on the GIS ESRI ArcGIS Desktop 10.8 suite; the fuzzy rule-based inference system, constructed in C ++ language, has been encapsulated in the GIS platform. After executing the preprocessing phase, the value of the seven indicators were calculated by microzone. In Figure 5 is shown a thematic map of the indicator I\({}_{1}\), giving, in square meters, the built-up area by resident. The thematic map was obtained by setting five thematic classes and applying the thematic natural breaks classification method; this method is based on the Jenks thematic classification method [19; 20], an optimization algorithm that finds the breaks of each class minimizing the variance within classes and maximizing the variance between classes. Figure 5 shows that the indicator I\({}_{1}\) is particularly high in microzones consisting mainly of tourist areas with few residents, where the buildings consist mainly of villas that occupy a large surface area. In Figure 6 is shown a thematic map of the indicator I\({}_{6}\), giving, in percent, the ratio between the extension of the urban green regions in the microzone and the area of the microzone. The thematic map was obtained by setting five thematic classes and applying the equal interval thematic classification  
Figure 5: Thematic map of the indicator I\({}_{1}\) by microzone. method; this method finds the breaks of each class so that the range of values in each class has the same amplitude. This indicator has a high value especially in microzones significantly covered by natural parks. After the fuzzification process each indicator was fuzzified considering the fuzzy sets constructed by the expert shown in Table 2.  
In Figure 7 is shown the thematic map of the fuzzified indicator I\({}_{1}\), in which the name of the class is the label of the fuzzy set to which the microzone belongs with the highest membership degree. The microzones classified as High are tourist areas and densely populated areas. Figure 6: Thematic map of the indicator I\({}_{6}\) by microzone. Figure 7: Thematic map of the fuzzified indicator I\({}_{1}\). Figure 8 shows the thematic map of the fuzzified indicator I\({}_{6}\). The microzones in which a significant part of the surface is covered by urban green areas are classified as Very High and High. After executing the fuzzy rule-based system, the classification of the microzones by UGA class was performed. This result is shown in Figure 9. The microzones covering tourist residential areas with a high presence of greenery and the microzones covering park areas are classified as class 5. The microzones that fall in the historical centers or in densely populated areas, and in which there are no public green areas, belong to class 2. Finally, spatially adjoint microzones belonging to the same UGA class were dissolved to form a UGC. The final map of the UGCs is shown in Figure 10.  
The largest UGC, which covers over 60% of the municipal area, is classified as UGA class 4, i.e., an urbanized area with urban greenery. This result shows that in the municipality of Pozzuoli, on average, there is a high ratio between the urban green area and the population, which can facilitate the design of eco-sustainable transformations of the city. The UGCs classified as 1 (densely urbanized or industrial area with no urban greenery and high presence of built surfaces and waterproof open spaces) are densely populated areas with popular housing. Figure 8: Thematic map of the fuzzified indicator I\({}_{6}\). Figure 10: Final urban green context (UGC) map thematized by UGA class. Figure 9: Thematic map of the microzones thematized by urban green area class (UGA class). The thematic map in Figure 11 shows the spatial distribution of the reliability. The reliability assigned to the UGCs varies in a range between 0.58 and 0.95. The UGCs with highest reliability values are UGCs classified in the UGA class 5, consisting of a single microzone.