Data modelling for emergency response

Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)

Modeling emergency management data by UML as an extension of geographic data sharing model: AST approach

Applying GIS functionality provides a powerful decision support in various application areas and the basis to integrate policies directed to citizens, business, and governments. The focus is changing toward integrating these functions to find optimal solutions to complex problems. As an integral part of this approach, geographic data sharing model for Turkey were developed as a new approach that enables using the data corporately and effectively. General features of this model are object-oriented model, based on ISO/TC211 standards and INSPIRE Data Specifications, describing nationwide unique object identifiers, and defining a mechanism to manage object changes through time. The model is fully described with Unified Modeling Language (UML) class diagram. This can be a starting point for geographic data providers in Turkey to create sector models like Emergency Management that has importance because of the increasing number of natural and man-made disasters. In emergency management, this sector model can provide the most appropriate data to many "Actors" that behave as emergency response organizations such as fire and medical departments. Actors work in "Sectors" such as fire department and urban security. Each sector is responsible for "Activities" such as traffic control, fighting dire, emission, and so on. "Tasks" such as registering incident, fire response, and evacuating area are performed by actors and part of activity. These tasks produce information for emergency response and require information based on the base data model. By this way, geographic data models of emergency response are designed and discussed with "Actor-Sector-Activity-Task" classes as an extension of the base model with some cases from Turkey

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy

empathi: An ontology for Emergency Managing and Planning about Hazard Crisis

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/

Building Information Modeling as Tool for Enhancing Disaster Resilience of the Construction Industry

As frequencies of the disasters are increasing, new technologies can be used to enhance disaster resilience performance of the construction industry. This paper investigates the usage of BIM (Building Information Modeling) in enhancing disaster resilience of the construction industry and in the establishment of the resilient built environment. In-depth literature review findings reveal BIM’s contribution to the disaster resilience in the pre-disaster and post-disaster phases especially through influencing the performance of the supply chain, construction process, and rescue operations. This paper emphasises the need for BIM’s integration to the education and training curriculums of the built environment professionals. Policy makers, construction professionals, professional bodies, academics can benefit from this research

Terrorism threat in Belgium : the resilience of Belgian citizens and the protection of governmental reputation by means of communication

In November 2015, the terrorism threat in Belgium confronted both citizens and the government with a situation characterized by high uncertainty. In this context, a national survey was conducted among 805 respondents, with three purposes. First, this case study aimed to explore how Belgians deal with the threat by examining if they change their behavior in public places and seek information about the threat. Second, we investigated why people seek and process information about the terrorism threat based on three determinants,namely their level of involvement with the threat, the expert efficacy of the government, and attitudes towards mass media communication. Finally, this study elaborated on perceived governmental efficacy, researching how governmental reputation is affected through institutional trust and governmental responsibility. The results show that the terrorism threat leads citizens to be more alert in public places and participate less in mass events. Moreover, one fifth stopped traveling by public transport. It was found that Belgian citizens also searched for information several times a day, mostly via traditional media such as television and radio. Furthermore, based on structural equation modelling, we found that information seeking and processing behavior is determined by the cognitive assessment of the risk. This cognitive risk assessment is in turn positively influenced by risk involvement and perceived governmental expert efficacy. However, if the mass media are seen to focus too much on drama and sensationalism then the perception of risk decreases, and this in turn reduces information seeking behavior. In addition, results show that a perception of governmental expert efficacy is able to increase trust and decrease the level of governmental responsibility, which is in turn beneficial for governmental reputation. The implications of these findings are discussed