## 4 Data Visualization  
Spatial-temporal data visualization helps in the construction of the big picture by providing a visual structure that can be easily understood and be used in all phases of emergency management. In order to understand the variables and how they relate to each other, we set to make visualizations that explore the following perspectives: temporal (both in the sense of perceiving whether the time (months) have an impact on the type and number of incidents that occur and in the sense of understanding if the building's age influences the occurrence of events), spatial (perception of zones of concentration of incidents), and construction characteristics of the building (relation between the number of building floors and the type of structure with which they were built and the occurrence of incidents). Aiming to understand how the variables behave, an exploratory analysis was conducted, as shown in Figure 2. For prediction purposes, only variables with a correlation with incident type higher than 0.1 or lower than \(-0.1\) were considered. The top-left graphic in Figure 2 relates the number of incidents to the number of emergency vehicles used. Data show that fires are the event requiring a greater number of vehicles. This shows that the least frequent incidents (fires) are the ones requiring more resources (emergency vehicles). Figure 1: Correlation between the variables with the strongest associations. Figure 2: Distribution of occurrences in each variable by incident type. The top-right graphic in Figure 2 shows the number of persons that were involved in solving the incident. This resource (human resource) is needed in larger quantities in technological and industrial incidents, and accidents register the lowest number. These findings show that the incidents that occur the most are the ones requiring more human resources, stressing the pressure on the availability of this type of resource. Regarding precipitation values (bottom-left graphic in Figure 2), it is shown that events related to infrastructure are the ones that occur the most with the increase in precipitation. The average age of the buildings (bottom-right graphic in Figure 2) highlights that older buildings have a higher incidence of occurrences related to industrial-technological events and infrastructure events. Additional analysis shows that accidents are the most common occurrences among buildings with a lower average age. In the heat map of Figure 3, which distributes all occurrences by their location, it is possible to see that these are concentrated in downtown Lisbon, Olaias, Parque das Nacoes, Lumiar, and Benfica. Heat maps are intuitive spatial data representations where different colors are used to represent areas with different concentrations of points--showing overall shape and concentration trends [27]. From a technical point of view, it is a visualization of the areas of influence of each point and further summation in places where areas overlap. The color gradient shows the power of influence at a certain point. For a non-cartographer user, the map is attractive, easily readable, and the visualization is more comprehensible. Overall, it is concluded that the four types of incidents under analysis have a greater occurrence in downtown Lisbon, which is, as can be seen in Figure 4, the area of Lisbon with a higher value of buildings antiquity--the historical area of the city. In these zones, streets are narrow. Figure 5 shows the building age distribution in the city and there is a correlation of building age (most of them in the city center) with fire, accidents with equipment, and infrastructure. In order to understand the spatial distribution of each type of incident, we have created four heat maps, as shown in Figure 4. It is known that occurrences like accidents have more concentration points, whereas industrial/technological occurrences occupy a greater spot in downtown Lisbon.