Figure 1.
Representation of the First Response Institution (FRI) activity level along time when an emergency is detected. Activity level has four phases (Situational Analysis (SA), Initiation Response (IR), Emergency Response (ER), Restoration Response (RR)) and five decision points (Initiating Event (IE), Control Event (CE), Restoration Event (RE), Normalizing Event (NE), Termination Event (TE)) [
4].
Figure 1.
Representation of the First Response Institution (FRI) activity level along time when an emergency is detected. Activity level has four phases (Situational Analysis (SA), Initiation Response (IR), Emergency Response (ER), Restoration Response (RR)) and five decision points (Initiating Event (IE), Control Event (CE), Restoration Event (RE), Normalizing Event (NE), Termination Event (TE)) [
4].
Figure 2.
Schemes of the three situations to be scrutinized in the toy example (a,c,d), where with is the variable and with and the category corresponding to the variable. The joint probability of occurrence is shown with a line of different thickness according to its value. The individual probabilities are obviated, as they are equiprobable. In (b,d,f) the projections of the original vectors onto the three most significant eigenvectors are depicted together with their Bootstrap-Parzen confidence regions for each category.
Figure 2.
Schemes of the three situations to be scrutinized in the toy example (a,c,d), where with is the variable and with and the category corresponding to the variable. The joint probability of occurrence is shown with a line of different thickness according to its value. The individual probabilities are obviated, as they are equiprobable. In (b,d,f) the projections of the original vectors onto the three most significant eigenvectors are depicted together with their Bootstrap-Parzen confidence regions for each category.
Figure 3.
Details of the toy example. (a) Analysis of the obtained eigenvectors in the three cases of the toy example and their bootstrap confidence intervals. (b) Eigenvalues for the three cases. (c) Projections of the different occurrences on the eigenvectors. The 6 lines represent the 6 dimensions of the eigenvectors, which cross through each of the categories taking place individually and represented by points and asterisks. Crossed triangles represent the joint occurrences of categories represented by their colors. These joint occurrences are in the subspace delimited by the lines from each of the involved categories in the linear combination of said categories.
Figure 3.
Details of the toy example. (a) Analysis of the obtained eigenvectors in the three cases of the toy example and their bootstrap confidence intervals. (b) Eigenvalues for the three cases. (c) Projections of the different occurrences on the eigenvectors. The 6 lines represent the 6 dimensions of the eigenvectors, which cross through each of the categories taking place individually and represented by points and asterisks. Crossed triangles represent the joint occurrences of categories represented by their colors. These joint occurrences are in the subspace delimited by the lines from each of the involved categories in the linear combination of said categories.
Figure 4.
Flowchart of the extended Multiple Correspondence Analysis (MCA) applied to the emergency Data Base (DB).
Figure 4.
Flowchart of the extended Multiple Correspondence Analysis (MCA) applied to the emergency Data Base (DB).
Figure 5.
Example of reading the results from extended MCA in fire brigades: (a) Eigenvalues represent the importance of each eigenvector. (b) Eigenvectors and their confidence intervals, where the x-axis represents the number of categories included in the data matrix, and the y-axis is the amplitude of that category in the projected subspace. (c) Joint representation of the projected categories and their confidence areas, with respect to three eigenvectors, and their paired view projections.
Figure 5.
Example of reading the results from extended MCA in fire brigades: (a) Eigenvalues represent the importance of each eigenvector. (b) Eigenvectors and their confidence intervals, where the x-axis represents the number of categories included in the data matrix, and the y-axis is the amplitude of that category in the projected subspace. (c) Joint representation of the projected categories and their confidence areas, with respect to three eigenvectors, and their paired view projections.
Figure 6.
Fire-brigade categories and relation between paired projected categories plots and their eigenvectors: (a) Detail on eigenvector , and projected categories in terms of and (left), and of and (right); (b) Detail on eigenvector , and projected categories in terms of and (left) and and (right); (c) Detail on eigenvector , and projected categories in terms of and (left) and of and (right).
Figure 6.
Fire-brigade categories and relation between paired projected categories plots and their eigenvectors: (a) Detail on eigenvector , and projected categories in terms of and (left), and of and (right); (b) Detail on eigenvector , and projected categories in terms of and (left) and and (right); (c) Detail on eigenvector , and projected categories in terms of and (left) and of and (right).
Figure 7.
Individual MCA results of fire brigades, police, health service, and transit. See text for detailed explanation.
Figure 7.
Individual MCA results of fire brigades, police, health service, and transit. See text for detailed explanation.
Figure 8.
MCA results of paired combinations for fire brigades, police, health service, and transit. Each row shows the first eigenvector, a general 3D plot, a 3D plot of the related categories, and a 3D pot of the unrelated categories (eigenvalue plot is omitted).
Figure 8.
MCA results of paired combinations for fire brigades, police, health service, and transit. Each row shows the first eigenvector, a general 3D plot, a 3D plot of the related categories, and a 3D pot of the unrelated categories (eigenvalue plot is omitted).
Figure 9.
Combined emergencies of health services, transit, and fire brigades: (a) Eigenvectors; (b) All the visible categories; (c) Only five categories visible, with two concentrations well defined (health services and transit) separated from fire brigades; (d) Eigenvalues and relative relevance of the eigenvectors.
Figure 9.
Combined emergencies of health services, transit, and fire brigades: (a) Eigenvectors; (b) All the visible categories; (c) Only five categories visible, with two concentrations well defined (health services and transit) separated from fire brigades; (d) Eigenvalues and relative relevance of the eigenvectors.
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.
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.
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 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. With this information in mind, decision makers of this institution would consider dispatching personnel when they receive a report of suspicious persons, to prevent the commission crimes against the property or those in which the lives of the involved citizens may be threatened.
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. With this information in mind, decision makers of this institution would consider dispatching personnel when they receive a report of suspicious persons, to prevent the commission crimes against the property or those in which the lives of the involved citizens may be threatened.
Figure 13.
Representation of person asking for help (police) in green points, traffic accidents (health services) with brown triangles, and crashes (transit) with yellow stars. The three categories or emergencies plotted appear close one to another in both 3D and 2D plots. This distribution of emergencies and the overlapped elliptical shapes lead us to infer that these categories or emergencies types are significantly related.
Figure 13.
Representation of person asking for help (police) in green points, traffic accidents (health services) with brown triangles, and crashes (transit) with yellow stars. The three categories or emergencies plotted appear close one to another in both 3D and 2D plots. This distribution of emergencies and the overlapped elliptical shapes lead us to infer that these categories or emergencies types are significantly related.
Figure 14.
Representation of health services emergencies related to traffic accidents in blue, and with traumatism events in yellow. We observe that most of the traffic accidents are related to traumatism reported, but not all the traumatism events are related to traffic.
Figure 14.
Representation of health services emergencies related to traffic accidents in blue, and with traumatism events in yellow. We observe that most of the traffic accidents are related to traumatism reported, but not all the traumatism events are related to traffic.
Table 1.
Emergencies attended by the Public Safety Answering Point (PSAP) of Quito in 2014.
Table 1.
Emergencies attended by the Public Safety Answering Point (PSAP) of Quito in 2014.
Order | Month | Emergencies |
---|
1 | January | 74,358 |
2 | February | 70,010 |
3 | March | 88,119 |
4 | April | 86,192 |
5 | May | 91,265 |
6 | June | 84,627 |
7 | July | 89,300 |
8 | August | 98,531 |
9 | September | 99,878 |
10 | October | 94,322 |
11 | November | 93,897 |
12 | December | 108,347 |
| Total | 1,078,846 |
Table 2.
Categories or specific emergencies considered in the Data Base (DB) for Fire Brigades and its percentage of occurrence.
Table 2.
Categories or specific emergencies considered in the Data Base (DB) for Fire Brigades and its percentage of occurrence.
Order | Specific Emergency or Category | Occurrence |
---|
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% |
Table 3.
Categories (C) ordered by their percentage of participation in each First Response Institution (FRI).
Table 3.
Categories (C) ordered by their percentage of participation in each First Response Institution (FRI).
FRIs | C ≥ 30% | 30% > C ≥ 20% | 20% > C ≥ 10% | 10% > C ≥ 5% | 5%> C ≥ 1% | 1% > C | Total |
---|
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 |
# Cat. | 3 | 2 | 8 | 8 | 12 | 94 | 127 |
Total% | 31.78% | 3.99% | 20.06% | 21.29% | 12.70% | 10.18% | - - - |
Cum.% | 31.78% | 35.77% | 55.83% | 77.13% | 89.82% | 100% | - - - |
Table 4.
Percentage of emergencies attended during 2014, and the number of categories per each FRI in Quito PSAP.
Table 4.
Percentage of emergencies attended during 2014, and the number of categories per each FRI in Quito PSAP.
Order | FRIs | Participation | Categories |
---|
1st | Police | 64.2% | 45 |
2nd | Health Services | 15.0% | 41 |
3rd | Transit | 12.8% | 13 |
4th | Fire Brigades | 3.0% | 15 |
5th | Municipal Services | 2.8% | 8 |
6th | Military | 1.3% | 2 |
7th | Risk Management Secretary | 0.9% | 3 |
| Total | 100.0% | 127 |
Table 5.
FRIs combination to obtain Multiple Correspondence Analysis (MCA) results.
Table 5.
FRIs combination to obtain Multiple Correspondence Analysis (MCA) results.
FB | RMS | M | P | HS | MS | T |
---|
| - | - | | | - | |
| - | - | | | - | |
| - | - | | | - | |
| | | - | - | | - |
Table 6.
Categories used in the experimental results, ordered by FRIs.
Table 6.
Categories used in the experimental results, ordered by FRIs.
| Fire Brigades | Police | Health Services | Transit |
---|
1 | Rescue | Consumption/Drug Sale | Disease | Crashes |
2 | Gas Leak | Public Road Scandal | Accidental Poisoning | Collisions |
3 | Structural Fire | Suspicious Person | Traumatism NT | Vehicle Bad Parked |
4 | Fire Conatus | Family Brawl | Other NT Accidents | Vehicular Congestion |
5 | Forest Fire | Person Requesting Help | Traffic Accidents NT | Hit by a Car |
6 | Open Dept. | Excess of Noise | Fallen Same Height | Motorcycle Accident |
7 | Floods | Police Guard | Violation | Damaged Vehicle |
8 | Hazardous Materials | Non Typified Complaint | Exposure to Cold | Overturned Car |
9 | Vehicular Fires | Theft | Run over by a Car | Closed Way |
10 | Close Hydrant | Capture Bulletin | Wounded Head | Traffic Light damaged |
11 | Pers./Mat. Accident | Escort of Values | Convulsion | Fall of a Passenger |