Abstract: In this paper ongoing developments regarding the conceptual setup and subsequent implementation logic of a seamless spatio-temporal population dynamics model are presented. The DynaPop model aims at serving as basic input for social impact evaluation in crisis management. In addition to providing the starting point for assessing population exposure dynamics, i.e. the location and number of affected people at different stages during an event, knowledge of spatio-temporal population distribution patterns is also considered crucial for a set of other related aspects in disaster risk and crisis management including evacuation planning and casualty assessment. DynaPop is implemented via a gridded spatial disaggregation approach and integrates previous efforts on spatio-temporal modeling that account for various aspects of population dynamics such as human mobility and activity patterns that are particularly relevant in picturing the highly dynamic daytime situation.

Abstract: Disaster response requires communications among all affected parties including emergency responders and the affected populace. Wireless telecommunications, if available through a fixed structure cellular mobile network, satellites, portable station mobile networks and ad hoc mobile networks, can provide this means for such communications. While the deployment of temporary mobile networks and other wireless equipment following disasters has been successfully accomplished by governmental agencies and mobile network providers following previous disasters, there appears to be little optimization effort involved with respect to maximizing key performance measures of the deployment or minimizing overall 'cost' (including time aspects) to deploy. This work-in-progress does not focus on the question of what entity will operate the portable base during a disaster, but on optimizing the placement of mobile base stations or similar network nodes for planning and real time management purposes. An optimization model is proposed for the staging and placement of portable base stations to support disaster relief efforts.

Abstract: Modeling the interaction between interdependent systems in dynamic environments represents a promising approach to enabling communities to assess and manage the recurring risk to which they are exposed. We frame the problem as a complex, adaptive system, examining the interaction between transportation and emergency response as a socio-technical system. Using methods of spatial and statistical analysis, we overlaid the engineered transportation system on the organizational emergency response system to identify the thresholds of fragility in each. We present a research design and preliminary results from a small-scale study conducted in the Pittsburgh Metropolitan Region that examined the interaction between the transportation and emergency response systems. These results informed the design of a Situational Assessment Module for emergency managers, currently under development at the University of Pittsburgh.

Abstract: In the past a crisis event was notified by local witnesses that use to make phone calls to the special services. They reported by speech according to their observation on the crisis site. The recent improvements in the area of human computer interfaces make possible the development of context-aware systems for crisis management that support people in escaping a crisis even before external help is available at site. Apart from collecting the people's reports on the crisis, these systems are assumed to automatically extract useful clues during typical human computer interaction sessions. The novelty of the current research resides in the attempt to involve computer vision techniques for performing an automatic evaluation of facial expressions during human-computer interaction sessions with a crisis management system. The current paper details an approach for an automatic facial expression recognition module that may be included in crisis-oriented applications. The algorithm uses Active Appearance Model for facial shape extraction and SVM classifier for Action Units detection and facial expression recognition.

Abstract: Over the past few years, several standardized GIS data models have been developed to document “best practices” database designs for various application domains including the domain of emergency management. The majority of such models, however, have been developed in the context of developed countries as in the case of the Homeland Security geodatabase data model by ESRI®. These data models fail to be successful when transferred and used in the context of Least Developing Countries (LDCs) due to significant contextual differences in the domains of information systems and emergency practices. Therefore, developing GIS data models that are specifically designed for emergency response activities in LDCs are needed to improve existing emergency response practices in these countries. This paper reviews the state of development in GIS data models and the potential benefits and applications of building models that are specifically designed to support emergency response activities in LDCs. We first discusses why it is important to differentiate emergency response activities in LDCs from other contexts. We then present some theoretical considerations in developing GIS data models that can overcome contextual difficulties in LDCs in general and in the domain of information systenms. We finally attempt to provide key guidelines that may help designing a GIS Data Model, while is specifically referenced to the LDCs context.

Abstract: One of the challenges of humanitarian organizations is that there exist limited decision technologies that fit their needs. It has also been pointed out that those organizations experience coordination difficulties with volunteers willing to help. The purpose of this paper is to help address those challenges through the development of a decision model to assist in the management of volunteers. While employee workforce management models have been the topic of extensive research over the past decades, no work has focused on the problem of managing humanitarian relief volunteers. In this paper, we discuss a series of principles from the field of volunteer management and develop a multi criteria optimization model to assist in the assignment of volunteers to tasks. We present an illustrative example and analyze a solution methodology where the decision maker exercises his/her preferences by trading-off conflicting objectives. Conclusions, limitations, and directions for future research are also discussed.

Abstract: Maintaining the critical infrastructures, such as Drinking Water Treatment Plants (DWTP), transportation, power generation and communications systems, in a safe state is a complex problem. The effective collaboration, as well as the collection aggregation and dissemination of early warning information among the stakeholders of the Safety Management System (SMS) responsible for the safety of these critical infrastructures are some of the challenges that need to be addressed. This paper argues that the Software as a Service (SaaS) deployment model can offer new ways of enhancing the fusion of early warning information during the operation phase of critical infrastructures. It presents the requirements, the architecture and a number of features of a working prototype SaaS-based early warning information fusion system for DWTP safety issues in the Republic of Ireland. It is the first time that a SaaSbased working prototype system is reported of providing early warning information fusion services in the literature.

Abstract: After a risk has manifested itself and has led to an accident, valuable lessons can be learned that can be taken into account to reduce the risk of a similar accident occurring again. This calls for accident analysis methods. In the past 20 years a large number of accident analysis methods have been proposed and it is difficult to find the right method to apply in a specific circumstance. We conducted a review of the state of the art of accident analysis methods and models across domains. We classify the models using the well-known categorization into sequential, epidemiological and systemic methods. We find that these classes have their own characteristics in terms of speed of application versus pay-off. For optimum risk reduction, methods that take organizational issues into account can add valuable information to the risk management process in an organization.

Abstract: This paper presents a unique view of modeling the diffusion of warnings in social networks where the network structure may change over time. Since the characteristics and actions of people in a community have significant influence on the flow of information through a network, we present an axiomatic framework for modeling the diffusion process through the concept of trust. This ongoing work provides a micro level view of the behavior of individuals and groups in a community. Preliminary experiments were made to explore how model parameters such as trust and the social network structure affect warning message belief and evacuation.

Abstract: It is of practical significance and great value to design and develop a novel Early Warning System (EWS), which will be used by the personnel of institutions involved in the drinking water delivery governance model of Ireland. In order to help the users of our EWS in representing and codifying their knowledge on the complex coincidences that may drive Water Treatment Plants (WTP) to failures or to hazardous states we propose in this paper a novel approach of using Domain Specific Modeling (DSM) in the domain of EWS for Water Treatment Plants. The novelty of our DSM approach also lies in providing a standalone open source software application rendering profiling of the water utilities, early warning signals, monitoring mechanisms of signals along with capability of assessing the “tendency” of a WTP towards failure, given a set of observed early warning signals.

Abstract: Outbreak of a destructive pandemic influenza threatens to disrupt societies worldwide. International agencies and national governments have prepared plans and recommendations, but it is often decision-makers with the local authorities that are responsible for implementing the response. A central issue for these decision makers is what interventions are available and effective for the specific local community. The paper presents a simulator architecture and its relation to a workflow for decision support in influenza preparedness and response. The simulator can simulate pandemic scenarios, using localized community models, in the presence of various interventions to support an evaluation of potential response strategies. The architecture includes a customized modeling tool, separated from the simulation engine, which facilitates swift scenario modification and recalculation. This flexibility is essential both to explore alternative solutions in planning, and to adapt to changing requirements, information, and resources in outbreak response. An example simulation, based on actual population data from a reference city, illustrates the approach.

Abstract: Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes.