## 3 Case Study Application
### Data and Characteristics Used within the Vulnerability Conceptual Framework
#### 3.2.1 Essential Components and Strategic Spaces  
The components were thoroughly analyzed in the context of Guimaraes' historic city center, and the collected data were integrated into a comprehensive database. Vulnerability factors were visualized and assessed using QGIS.  
The initial map generated for this analysis provided a comprehensive representation of all the acquired layers within the case study. These layers included assessed buildings, infrastructure, hydrants, water networks, and the defined study area. Subsequently, a vulnerability zones map was created by dividing the area into seven distinct zones based on its urban development [19; 30]. Delimiting these zones was crucial to quantify the concentration of infrastructures. For this purpose, the \"Count Points in Polygon\" analysis tool was utilized, resulting in a new layer with an additional column indicating the number of infrastructures within each zone. The vulnerability levels of these zones were then represented using a color gradient, ranging from green to red, based on the number of infrastructures present.  
By applying Equation (3), as outlined in Section 2.1.1, the vulnerability levels were derived. The maximum number of critical infrastructure parts within a zone was found to be 22, while the minimum was 5. Considering that this analysis utilizes only three classes (low, medium, and high), a threshold value of 5.6 was obtained. This threshold categorizes zones with a number of critical infrastructure (CI) from 5 to 11 as low vulnerability, zones with 11 to 16 CI as medium vulnerability, and zones with 16 to 22 CI as high vulnerability.  
To illustrate the intrinsic vulnerability of CI, a map was generated by overlaying the infrastructure layers with the pre-categorized building layers, using their corresponding Fire Risk Index (FRI). The infrastructure layers were merged into a single layer using the \"Merge Vector Layers\" tool, and each infrastructure was assigned a specific type in an explicit column, facilitating the subsequent clustering process based on infrastructure type.  
The \"Inf&Buildings\" layer, containing information about both buildings and the infrastructure, was created by unifying the layer with all buildings in the study area and the layer containing all infrastructures. To address conflicts within the buildings layer, the \"Check Validity\" tool was employed to identify and resolve polygon errors hindering the unification process. After resolving these conflicts, the unification was successfully performed, resulting in the \"Inf & Buildings\" layer.  
For the categorization of buildings based on their criticality, additional columns were added to the \"Inf&Buildings\" layer, allowing for their classification according to their criticality. The criticality rankings specified in Table 2 were used for this purpose. To enhance the output, a 2.5D symbology tool was employed to provide a three-dimensional perspective for buildings containing infrastructure in the two-dimensional representation. Furthermore, a classification was assigned based on the criticality of each infrastructure's building, prioritizing the most critical ranking for buildings offering multiple services(e.g., pharmacy and accommodation). Two maps were then created, one for each criticality ranking (Driving Power and Hierarchy of Needs), which also displayed the type of infrastructure. To improve readability, the WMS (Web Map Service) from Bing Satellite was replaced with the WMS from Bing Map, providing a clearer and more user-friendly appearance for the maps.