# Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services
## 1 Introduction  
In order to improve a centralized and opportune emergency response [1], Ecuador created in 2012 the integrated security service, named ECU-911 [2]. Within its structure, a Public Safety Answering Point (PSAP) is a center in which emergency alerts are received and processed. If an alert is classifiedas an actual emergency by the PSAP, some of the First Response Institutions (FRIs), i.e., fire brigades, police, health services, transit agency, and others, will dispatch their units to attend that event. In this case, FRIs and emergency units work under centralized coordination and permanent feedback all the time. In addition, information from all emergencies received by a PSAP is saved on a centralized Data Base (DB). On the other hand, data analysis capabilities are a crucial component of the standard model of Emergency Response Systems [3]. Knowledge management is an additional factor which uses information from previous experiences to improve emergency responses, their procedures, and their normative [4]. However, when reviewing the information from the DB in integrated security services, we often find descriptions based on a mixture of quantitative and categorical variables. In the first case, it is feasible to apply statistical techniques to obtain the information that we need to feedback to the FRIs. In the second case, the use of traditional statistical tools and techniques could become hard and complex. Some previous works have already tackled the problem of emergency response systems and how data analysis can help to improve them. For instance, an overview was presented in [2] of how technology can support several new models of emergency attention services based on Enhanced-911 platforms. The idea of data treatment and analysis is also discussed therein, but no real analysis is actually performed. In the same line, [PERSON] proposed a model for emergency response systems [4]. This work suggested that there are failings in emergency response systems that knowledge management could prevent, or at least mitigate. However, no knowledge management system is proposed or evaluated therein. Moreover, a data model is also proposed in [5; 6] for the management of dynamic data during emergency response procedures in the Netherlands. Their spatio-temporal model allows to maintain operational and situational information in emergency response, but the model was never proven as an input for analyzing or inferring emergency behaviours. There are some other works oriented to specific analyses of emergency responses, like sentiment analysis during a hurricane [7] or response-time analysis of subway fire emergencies [8]. [PERSON] et al., in [9; 10], proposed risk decision analysis methods based on cumulative prospect theory and fault tree analysis, respectively, to solve the risk decision-making problem in emergency responses. Emergency evacuation from barrier lake downstream villages and H1N1 infectious diseases, respectively, illustrated the proposed methods. Unlike these specific analyses, our interest is oriented to emergencies reported by a 911-_like_ service, looking for a more general and simpler approach. Previous works have also proposed spatio-temporal analysis of emergency alerts. For instance, [PERSON] et al. [11] presented a spatio-temporal analysis of call-stream data corresponding to emergency responses in the State of California. This work points to the possibility of emergency event detection based on outlier detection. In the same direction, [12] proposed a statistical, spatial, and temporal analysis of emergency responses by using simple, yet robust and compact, event representations. In order to reach a better understanding of the existing qualitative information, we propose here a method that, while maintaining its simplicity, allows us to obtain statistically meaningful results and to support the managers to inspect the emergencies attended by FRIs, through the analysis of the reports routinely stored in the PSAP DB.