## 4 Data Visualization
This hot spot analysis uses heat maps to determine hazard risk, and it is a piece of important information for disaster planning management [28]. The temporal distribution of occurrences is shown in Figures 6-8, which depict the number of each type of incident in each month of the year. Figure 6 shows that fires happen mostly in the summer and the first and last month of the year. This last observation may be due to the use of fireplaces and candles. Figure 3: Heatmap—Distribution of incidents in the city of Lisbon. In the case of industrial-technological accidents, it is concluded that the monthly variation is irregular, as presented in Figure 8. As for infrastructure accidents, as can be seen in Figure 8 (A graphic), it appears that they have a higher frequency in the first trimester of the year, probably due to a greater intensity of precipitation, as we can see in Figure 8 (B graphic). Considering the five types of structure by which the buildings are built (concrete, slab, without a slab, stone, and other types of materials), 100% stacked column charts were created--as represented in Figure 9. This allows analyzing of the relation the type of construction has with the registered incidents. Figure 4: Heatmap—Spatial Distribution of each type of occurrence group. The **A** subfigure shows fire (building fires), **B** subfigure shows industrial–technological (gas leaks and suspicious situations). **C** subfigure shows accidents (with equipment or with elevators), and **D** subfigure shows infrastructure (floods and falls). Figure 5: Heatmap—Buildings’ antiquity distribution, where yellow colors represent older buildings, mainly in the city center. Figure 6: Number of fires per month of the year, where \(1\) is the 1 st month, January, and 12 th, the last, December. Figure 1:0 Number of industrial incidents per month of the year, where \(1\) is the 1 st month, January, and 12 th, the last, December. Visualization was also made regarding the number of floors in the buildings. This was made in order to check if it was possible to highlight any associations between the types of incidents and the number of floors in the building. Regarding these incidents, it is possible to say that they are more present in buildings with fewer floors, as shown in Figure 1:1  
Analyzing Figures 9 and 10 and crossing these conclusions with the distribution of the number of floors by building age, we can see in Figure 11 that older buildings have fewer floors, and as seen previously, older buildings have more incidents. The data visualization allows the identification of space and temporal buildings and disaster effects in a faster and more intuitive way. Table 2 shows the main incident type versus variable importance, and this is a piece of useful information for management, less variable to handle. Results were presented to the Risk Management Department of Lisbon Municipality. This data analysis allows identification of the most relevant factors (variables) for the report of incidents, and a big picture was produced. Associated data visualization improves decision making and situation awareness. Additionally, it is possible to automatically generate incident reports in real-time, improving the quality of the decisions made by the relevant authorities. Consequently, available resources can be better managed along with an improvement in the management of the entire process, from planning to the smart allocation of means, following the prediction and analysis of threat evolution. This visualization process helps in planning the availability of intervention teams through time (for instance, adjust holiday periods or increase capacity in certain months) and space (improve the allocation of teams to specific zones). The output of this work will be used as a new tool by this department from the Lisbon Municipality.