## 6 Discussion  
The different analyses showed that the management of the emergency resources of the city (vehicles and persons) is a challenge. Fires and industrial-technological incidents, by their nature, are the most difficult to predict but are also the ones that require the most resources, both in terms of vehicles and in terms of persons. The randomness of the location would be an additional layer of difficulty. However, it was possible to perceive where (the zones of the city) these incidents occur the most, therefore providing the critical incident managers with the possibility to locate these resources in advance for a more timely response. The reduction in response time allows for limiting of the expansion of the incident, which in turn reduces the cascading effects of these incidents.  
Downtown Lisbon emerges as the most critical area of the city. Although other areas of the city also require preparedness, this area has older and more fragile buildings, i.e., the buildings more exposed to risk. Taskforces should be prepared at all times for interventions, with appropriate vehicles and trained personnel.  
Using Random Forest algorithms with variables replaced by the values will allow the risk management department to be better prepared to respond to incidents in the city.  
From spatial visualization analytics, this is useful information to allocate intervention teams for big catastrophes and place material near critical areas.  
With this study we can conclude that a big part of the occurrences is largely concentrated in the city center, the zone of Benfica also being one of the foci. The most common occurrence is technological and industrial incidents, taking place equally all year, except during December. Occurrences related to fire happen mainly during the summer, December, and January, where we believe the reason for having a more significant number of fires in these latter two months is the presence of fireplaces and  
Figure 15: Algorithm accuracy comparison for Dataset 2, with fourteen independent variables.  
fire pits. It is part of our knowledge that infrastructure-type occurrences tend to occur when there is more precipitation, having a reduction in these occurrences during the summer.  
Occurrences of the accident type tend to occur in the areas with worse conservation levels, and a lower average age of buildings, which we believe is one of the reasons for the presence of lifts in these newer buildings.  
Historical data about incidents have considerable potential as data show patterns, allow predictions, and highlight the city's recurrent problems. The study of these data allows better future city planning and data integration among different systems and creates an important tendency, allowing this information to be used for future problems. By being able to respond in a more adjusted way to an incident, it might reduce the consequences of that incident and prevent a cascading effect on other subsystems of the city. Data mining can be used for incident management, and it is important to identify spatial and temporal patterns to manage these incidents in the city context.  
Visualizing these data using a visualization process allows identification of the city's patterns and is an important tool for city incident management. The prediction process allows for better planning and improves the response time of intervention teams.  
This research was based on a single city, and the specific conclusions are adjusted solely to the city of Lisbon. Nonetheless, the overall process can be replicated for other cities and adjusted to their specific characteristics. Future research should be conducted on this replication.