Análisis territorial de la actividad emprendedora en Sevilla

In T6, the AFO is directly connected to the ProRail Coordinator. The ProRail Coordinator informs the AFO that there are three trains in tunnel tube 2A but none in tube 1A, which they can explore. The AFO acts on this information and orders his two fire engine crews (FE) to explore tube 1A. The ProRail Coordinator is now aware that he cannot move the trains because fire fighters are in the tunnel.

Next, in T7 and T8, the smoke clouds are spreading throughout the tunnel, causing a panic in the trains. The train drivers request the RTC to provide clearance to leave the tunnel immediately. However, the RTC and ProRail Coordinator are unsure where the fire fighters are located in the tunnel and do not execute this request. In T7, the fire fighters communicate to the AFO that they are surprised to still find trains in the tunnel, and they pull back to let the trains be evacuated. Finally, in T9, 45 min after the start of the fire, the trains are evacuated from the tunnel.

The analysis of the information pathways informs us about the influence of direct and indirect information flows. The indirect information flow between the AFO and the EOC in T5 and T6 shows how the lack of communications can seriously impede the emergency response. This can only be seen if we connect the direct and indirect flows

between the different time slices to each other, by using information pathways. This also provides a new perspective to study the connectedness between nodes across time.

5.7 Conclusion and Discussion

As it has been argued in the theory and methods sections of this paper, it is crucial to incorporate timeliness of information exchange in social network studies of emergency response to more accurately represent incident response dynamics. We have shown that the reconstruction of communication and interaction between parties relevant to the emergency operations is not necessarily straightforward (Abrahamsson et al. 2010). We have added to the network tools available and have shown different aspects of the dynamics of one specific emergency event.

In summary, we have now the following information derived from the chain of communication events. From the evolving structure of the communication network it was clear that in this brief time span, emergence was dominant over other models proposed by Topper and Carley (1999). With regard to the consistency of communication it can be made clear, on the basis of the two-mode analysis, that different time periods in the middle of sorting out the emergency exhibit characteristics of a distribution over actors, as well as critical moments in information flow. This corroborates the pervasive qualitative story of the incident report, which showed confusing periods during the incident. Our analysis adds a more detailed insight into the patterns and actors present during these critical moments.

Analysis of the information pathways shows how sequences of connections between actors are important to explain communication patterns in the network. Information pathways illustrate how the timeliness of communication can seriously impede the effectiveness of networked collaboration. Not only do direct connections between actors explain the flow of information, but also indirect paths appeared crucial to information flow and for the explanation of confusion in the network. Thus, in emergency situations, different phases with different communication problems and their effects need to be discerned.

The origin and spread of the misunderstanding in the network supports our argument that we need a more dynamic network toolset to understand the development of the communication network. Using just a single overall measure for centrality fails to depict the emergent nature in which the network develops. Actors are connected differently to each other in different time periods that result in irregular flows of information

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through the network. In our case, this irregular synchronization of information allows the misunderstanding between fire fighters and rail traffic controllers to reverberate throughout the network.

The toolset provides us with a more fine-grained insight in when and where (in the network) sources for confusion emerged. As such, we indicated the patterns of communication that could help to analyze and counteract well-known problems in the first half hour of community response, such as employing the first acting responder as a coordinator instead of the strategically most logical alternative, given the situation (Berlin and Carlström 2011). Also, in reconstructing the information pathways we have shown in what manner various organizations were lacking access to information. Reconstructing information pathways that take the timeliness of information into account are essential to understand how the miscommunication could spread through the network.

Our solution to analyze the process of information sharing during emergency response operations has been to look at the temporary nature of the networks and characterize the manner in which this temporality influenced the availability of information in an actual incident situation. In this respect, timeliness is an important construct that can be analyzed further by looking at the information pathways in the network (Kossinets et al. 2008). We approached the network analysis not solely through a static set of (centrality) measures, but stressed a more process-oriented analysis that better captures the dynamic nature of information sharing in crisis situations.

This does not only hold for the scientific community, but we also want to extend this message to the inspectorates and incident evaluation community. When network studies are incorporated in formal incident evaluation reports, these often lack recognition for incident response dynamics in their network depictions. The research report we analyzed in our case study is also an example of this. Employing only static (degree) centrality measures obscures the real incident dynamics and origin of the communication and coordination problems. Using static network analysis diagrams to represent the emergence of connections might lead towards the inaccurate assertion that improving the direct information flow is key to an effective response. Yet, the analysis of information pathways shows us that it is also important to better understand the indirect information flow, since indirect paths might prove to be the quickest connections across time periods.

There are of course also limitations to our work. First, we have used only one case study that functions as an illustration of our network toolset, and not as a validation of the toolset. This choice is justifiable, because each network tool is tested and used in the

fields of network studies and computer science. Therefore, we feel that the results of one case study can be useful in creating awareness of the issue of dynamics and for applying this emerging dynamic method in the field of emergency response studies. Still, applying the toolset to more and different kinds of cases is necessary to see whether new inferences can be made about incident response dynamics. First, this is necessary to shed a new light on the previous network analysis of incident response scenarios that we discussed in our theory section. Second, although there were no victims, the sensitivity of potential mass disruption of a densely traveled network and the anxiety with the potential incidents in a tunnel warranted a high level of scrutiny of which the report itself is a witness. This sensitivity might also have colored both the interviews with the actors in the crisis and the reconstruction. Third, in reconstructing we have solved the ambiguity in the crisis by independent coding of the information elements contained in messages. This certainly has influenced the way we could construct the networks. Lastly, in this unique case some of the processes seem to have been influenced strongly by the conflicting organizational and institutional positioning of key actors. The report has resulted in recommendations and ensuing actions that redress these imbalances. Moreover, train drivers are now better trained than before to handle tunnel incidents.

We have shown that a broader set of network tools can help to understand the dynamics of communication and collaboration in a crisis situation. Applying different methods from the social network analysis and network science toolbox may shed a different light on the role of actors and communication flows during emergency situations. Our toolbox adds to the useful insights that are already made with structural analysis of the organizational networks and the people working in them (Uhr and Johansson 2007). We add dynamics in our toolbox, while acknowledging the necessity to include elements such as trust and the ability to work with the information provided by others. Of course applying such an analysis to one case with limited complexity does not do justice to the potential. Moreover, in complex situations it might be helpful to address the emerging patterns of interaction with other tools such as those applied to large networks and addressing processes aspects, such as event-based analysis (Butts et al. 2007).

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5.8 Appendix

Abbreviation Organization Function Main Responsibility in network

(collector, aggregator or disseminator of information)

AAS Schiphol Amsterdam Airport Schiphol

Employees

Collect information

AFO Schiphol Airport Fire Officer Aggregate information

AMS Medics Airport Medical Services Collect information

CCS Schiphol Coordination Center Schiphol Disseminate information

DC_SR_K Dispatch

Center

Dispatch Center Safety Region Kennemerland

Disseminate information

FE Fire service Fire engines Collect information

FF_HO Fire service Fire fighters Head Officer Aggregate information

FF_O Fire service Fire fighters Officer Aggregate information

KMar RMP Royal Military Police District

Amsterdam Airport Schiphol

Collect information

KMar_CR RMP Royal Military Police Control

Room

Disseminate information

NPSA Police National Police Services

Agency in Driebergen

Disseminate information

NPSA_RP Railway

police

National Railway Police Collect information

NS_SC NS Rail National Railway Command

Center

Disseminate information

NS_Service NS Rail National Railway service

personnel

Collect information

PSGR Passengers Passengers Collect information

ProRail_BO ProRail ProRail Back Office Disseminate information

ProRail_EOC ProRail Emergency Operations

Coordinator

Aggregate information

RTC1 ProRail Rail Traffic Controller 1 Disseminate information

RTC2 ProRail Rail Traffic Controller 2 Disseminate information

SRC ProRail Switching & Report Center Disseminate information

SRC_ Technician

ProRail Switching & Report Center

Technician

Collect information

TD NS Traindriver(s) Collect information

Number Actor Degree Centrality Closeness Centrality Betweenness Centrality Eigenvector 1 FFE 0.692 0.923 0.036 0.242 2 TDs 0.769 1.000 0.116 0.245 3 AAS 0.692 0.947 0.033 0.243 4 KMAR 0.615 0.878 0.010 0.236 5 NS_R 0.615 0.878 0.010 0.236 6 PSGR 0.269 0.720 0.002 0.104 7 RTC1 0.115 0.486 0.002 0.005 8 RTC2 0.654 0.923 0.028 0.231 9 CCS 0.654 0.923 0.028 0.231 10 SRC 0.577 0.857 0.008 0.224 11 SRC_Technician 0.500 0.818 0.005 0.199 12 RTCs 0.500 0.818 0.005 0.199 13 KMAR_CR 0.500 0.818 0.005 0.199 14 AFO 0.692 0.923 0.036 0.242 15 AMS 0.615 0.878 0.014 0.228 16 ProRail_BO 0.615 0.878 0.014 0.228 17 DC_SR_K 0.654 0.900 0.017 0.240 18 NS_SC 0.615 0.878 0.010 0.236 19 NPSA_Driebergen 0.577 0.857 0.008 0.223 20 AAS_HD 0.577 0.857 0.008 0.223 21 ProRail_EOC 0.500 0.818 0.006 0.194 22 NPSA_RP 0.231 0.706 0.001 0.089 23 FF_HO 0.115 0.667 0.000 0.041 24 FF_O 0.115 0.667 0.000 0.041

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1.a. T1 0-5 minutes 1.b. T2 6-10 minutes

1.c. T3 11-15 minutes 1.d.T4 16-20 minutes

1.e.T5 21-25 minutes 1.f. T6 26-30 minutes

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