# Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services
## 7 Discussing the Results  
The information saved in the DB can be cataloged as data, and by filtering it, we obtain information usable to prepare reports. If we apply one or more types of analysis, i.e., multiple correspondence, spatio-temporal or statistical [12], we obtain knowledge that characterizes the FRIs, the emergencies, their geographical location, the temporal distribution, and periodical behaviour. If this information is given as feedback to the PSAP, the decision level could implement different actions oriented to warn, prevent, relocate resources, modify procedures, or combine resources, all of them oriented to improve the emergency services response, as can be observed in Figure 11.  
In the above-mentioned cases, feedback can be considered as an additional element to be taken into account when implementing actions oriented to prevent or to improve the emergency response. In this setting, MCA results are used to identify relationships among emergencies. As an example, let us consider Police and its two specific categories: suspicious person and theft. We observe that these two emergencies are closely related, according to Figure 12, because in most cases, the sighting of suspicious persons has been followed with theft reports. This phenomenon could be considered to modify the response procedures or to relocate police units when this type of alerts arrive at a PSAP. Figure 10: Categories of fire brigades, risk management secretary, military, and municipal services. (**a**) Eigenvalues of the FRIs analyzed, according to the selection criteria, none of the dimensions are used to describe this group of categories. (**b**) All the categories plotted together. (**c**) Events concentrations of three FRIs, when a forest fire overpasses the capacity of fire brigades, then military forces move personnel, vehicles, or aircraft, and municipal services coordinate the water supply for helicopters and tank trucks. (**d**) The eigenvalues plot shows us the relative relevance of each dimension in MCA. When combined institutions are analyzed, emergencies or categories as a person asking for help (police) are represented with green points, transit accident (health services) with brown triangles, and crashes (transit) with yellow stars can be noted to have a close relation, as shown and explained in Figure 13. The concentrated group of persons asking for help is located close to crashes with a similar spreading, and both emergencies are close to transit accidents attended. If this is an usually related behaviour, when a PSAP receives an alert of any of these type of emergencies, it is possible to modify the procedures to attend them under specific conditions as time or geographical location taken when we apply the analysis proposed in [12], and then those taken actions may result in relocated resources, combined resources, or starting a preventive procedure to warn the divers when vehicles are moving under specific conditions, i.e., a holiday, long weekend, local regional event, or similar ones. By analyzing health services categories as plotted in Figure 14, we can observe traffic accidents and traumatism represented with blue and yellow points, respectively. Note that most of the attended  
Figure 11: If the data saved in the DB are processed, they become information, and by applying different types of analysis it can be transformed into knowledge to give feedback to the management, so that the PSAP can take actions oriented to warn the users, to prevent accidents, or to improve the response in case of emergency. Figure 12: Plots of Police emergencies, the suspicious persons reported are represented in yellow points, and theft reported are represented with orange stars. By analyzing the information shown in the 3D and 2D plots, we infer that the two types of plotted emergencies are very closely related.