# Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services
## 5 Statistical and Graphical Interpretation  
Figure 4 illustrates the procedure designed to obtain MCA and its operative and statistical representations from the event DB. A specific time period is read (e.g., a month) and then we select those variables of our interest. Data preprocessing starts by replacing the event name contained in the DB with a binary code to be used in MCA. It is convenient to set a minimum percentage of participation to hide unnecessary or low represented categories of events, given that keeping the most representative categories yields more statistically accurate results. As explained, the data matrix is resampled, yielding a cloud of replicated projections of the categories on the eigenvector space, and with this data, confidence regions are estimated by using Parzen windowing techniques as explained. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Order** & **Specific Emergency or Category** & **Occurrence** \\ \hline
1 & Structural Fires & 24.63\% \\
2 & Fire Rescue & 18.12\% \\
3 & Rescue & 17.69\% \\
4 & Forest Fires & 12.86\% \\
5 & Gas Leaks & 9.71\% \\
6 & Floods & 5.99\% \\
7 & Open Department & 3.93\% \\
8 & Hazardous Material & 3.20\% \\
9 & Vehicular Fire & 2.68\% \\
10 & Personal or Accident Material & 0.39\% \\
11 & Close Hydrant & 0.33\% \\
12 & Unit crashed & 0.17\% \\
13 & Vehicular & 0.17\% \\
14 & Water supply & 0.09\% \\
15 & Domiciles & 0.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Categories or specific emergencies considered in the Data Base (DB) for Fire Brigades and its percentage of occurrence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**FRIs** & **C**\(\geq\)**30\%** & **30\% \textgreater{} C \textgreater{} 20\%** & **20\% \textgreater{} C \textgreater{} 10\%** & **10\% \textgreater{} C \textgreater{} 5\%** & **5\%\textgreater{} C \textgreater{} 1\%** & **1\% \textgreater{} C** & **Total** \\ \hline FB & – & 1 & 3 & 2 & 3 & 6 & **15** \\ RMS & – & – & – & – & 3 & **3** \\ M & – & – & – & – & 2 & **2** \\ P & 1 & – & 1 & 4 & 7 & 32 & **45** \\ HS & 1 & 1 & 2 & 1 & – & 36 & **41** \\ MS & – & – & – & – & 8 & **8** \\ T & 1 & – & 2 & 1 & 2 & 7 & **13** \\ \hline
**\# Cat.** & **3** & **2** & **8** & **8** & **12** & **94** & **127** \\ \hline
**Total\%** & **31.78\%** & **3.99\%** & **20.06\%** & **21.29\%** & **12.70\%** & **10.18\%** & **– \\ \hline
**Cum.\%** & **31.78\%** & **35.77\%** & **55.83\%** & **77.13\%** & **89.82\%** & **100\%** & **– \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categories (C) ordered by their percentage of participation in each First Response Institution (FRI). For better making our visual analysis, the results are presented in three different types of representations, as explained next for the case example of fire-brigade events in Figure 5. We have filtered the number of categories represented, in such a way that those with participation below 0.1% of the total were hidden, thus leaving only six categories. Panel (a) depicts a diagram of six normalized eigenvalues, which represent the relevance of the information contained by each eigenvector. Note that in this case, values above 0.9 provide more relevant information than the others, but it is not easy to establish a threshold. Panel (b) is a graphical representation of the obtained projection directions, constrained in this case to be 6. The use of bootstrap resampling allows us to determine which categories are relevant for each factor (vertical red lines in these subpanels). We can interpret each eigenvector as a factor combining the weighted presence of the different categories. Panel (c) shows the joint projections of the categories on the three first directions, together with the point cloud yielded by bootstrap resampling and the confidence regions yielded by Parzen windowing. We immediately note the bootstrapped concentrations, which in some cases are well defined, and in other cases, they spread out without any recognizable pattern.