## 3 Methodology
### Methodology towards SCCI Knowledge Extraction  
The methodology follows the CRISP-DM method [26] to use CIC data and transform these into knowledge. Firstly, and to serve the overall purpose of this research, we detailed the analysis to pinpoint the risks faced by buildings located in Lisbon. We intended to understand what are the main incidents occurring in Lisbon, which ones happen more often, and what are the influencing factors of those incidents. In order to achieve this, we were provided with a report of the occurrences in the city created by firefighters. This report contained data such as the location of the incident, its category, a brief description of the occurrence (when necessary), and how many elements and vehicles were deployed to resolve the incident. Secondly, we performed \"Data Understanding\" by exploring all the variables in the dataset with the data related to incidents in buildings. In \"Data Preparation\", we analyzed the null values from certain entries. The data only included values between 2011 and 2018. The entries with more nulls had 45 nulls. With 6386 entries in the database, those entries were eliminated since they would not have a relevant impact on our dataset. We ended up with a workable dataset of 6341 entries. The \"Type of Occurrence\" was one of the variables in the dataset. This variable has already been classified by the system into 20 pre-defined types of CIC incidents. These are as follows:  
* Infrastructures and Communication Routes--Falling Structures;
* Infrastructures and Communication Routes--Flood Private Space;
* Industrial-technological--Suspicious Situations--Check Smoke;
* Industrial-technological--Suspicious Situations--Check Smells;
* Industrial-technological--Gas Leak--Plumbing/Conduct;
* Industrial-technological--Gas Leak--Bottle;
* Industrial-technological--Gas Leak--Deposit/Reservoir;
* Accidents--Equipment--Lifts;
* Accidents--Equipment;
* Fire--Building (Infrastructures/Installation)--School;
* Fire--Building (Infrastructures/Installation)--Empty/Degraded Building;
* Fire--Building (Infrastructures/Installation)--Commercial/Shops/Fairs/Transport Station;
* Fire--Building (Infrastructures/Installation)--Performance/Recreation Religious Worship;
* Fire--Building (Infrastructures/Installation)--Services;
* Fire--Building (Infrastructures/Installation)--Hospital/Home;
* Fire--Building (Infrastructures/Installation)--Industry/Workshop/Warehouse;
* Fire--Building (Infrastructures/Installation)--Housing;* Fire--Building (Infrastructures/Installation)--Hospitality and Similar;
* Fire--Building (Infrastructures/Installation)--Parking;
* Fire--Building (Infrastructures/Installation)--Military/Security Forces. These 20 types of CIC incidents belong to 4 main categories of incidents, also already defined by the experts for civil protection:  
* Fire (building fires);
* Infrastructure (floods and falls);
* Industrial-technological (gas leaks and suspicious situations);
* Accident (with equipment or with elevators). Based on these four categories, the different subcategories were clustered into these four major categories, to have a better perception of data variation and to have a greater volume of data for each incident category. Findings showed that the volume of data is well distributed among the four options. We found that industrial technological accidents represent 36.57% of the observations in the database, the occurrences of infrastructure incidents represent 26.94% of the data, accidents represent 21.28% of the data, and fires represent 15.21% of the data under analysis. It is concluded that although industrial and technological situations are the category with the most incidents, the remaining categories are also well represented. To gain more understanding on the buildings of different municipal zones/neighborhoods (hereafter named \"zones\"), we imported another dataset with the structure of the building in the different zones of the city.