Abstract: Emergency situations call for the timely collaboration and error free communication of first responder (FR) teams from their Command Posts (CP) and between themselves. First responder teams must form and adapt their plans and actions as a real-time critical situation unfolds. This paper presents an advanced Command Post application that manages a diversity of FR teams during an emergency. Data from biometric, fire and/or gas sensors in addition to received annotated videos from first responders on site, carrying personal digital assistants (PDAs), are simultaneously managed. The presented system provides properly configured access to and alert-dependent visualization of real time location, biometric, gas, fire and annotated video data from FRs in the field to allow for effective reaction and decision support from CP personnel. Additionally, the system forms an information management system for all necessary information to be quickly handy during emergency handling, such as FR information, critical infrastructure information, historical information, etc. This system has been validated through qualitative analysis in a field trial at the M30 tunnel in Madrid by participating end users.

Abstract: Although quantitative analytical information systems are an important resource for supporting decision-making in disaster operations management, not all aspects of a disaster situation can be easily quantified. For example, although the concept of the disaster resilience of a community has a technical dimension within which one can measure the resistance of the infrastructure against, and the speed of its recovery from, a disaster event, it also has social, organizational, and economic dimensions within which these characteristics may be more difficult to measure. This work-in-progress paper introduces a quantitative framework within which the multi-dimensional nature of such disaster resilience can be represented in a concise manner. This can help to improve understanding of the complexities associated with the concept, and thus directly support decision-making in disaster operations planning and management.

Abstract: The need to gather and use decentralized information and resources in responding to disasters demands an integrated interface that can support large-scale collaboration. This paper describes the development of a collaboration tool interface. The tool will surpass existing groupware and social networking applications, providing easy entry, categorization, and visualization of masses of critical data; the ability to form ad-hoc teams with collaboration protocols for negotiated action; and agent-augmented mixed-initiative tracking and coordination of these activities. The paper reports user testing results concerning the data entry interface, emergent leadership, and the directed negotiation process. The paper also discusses planned enhancements, including formalized collaboration engineering and the use of a disaster simulation test bed.

Abstract: Maps are essential visual advocacy devices for humanitarian relief projects. Maps provide advocacy by presenting, disseminating, and analyzing humanitarian relief project information in formats that are easier to understand and reason with. In this paper, we present our preliminary work on geographically visualizing Consolidated Appeals Process (CAP) information. The practical intent of our work is to (a) provide advocacy for CAP projects by geographically representing project information such as funding status so that (b) the nature of a project is better understood, thus potentially leading to increased project donations and improved project funding decision making. We provide examples of a prototype mapping application built to utilize Google Earth[TM] for representing CAP project information in map-based formats.

Abstract: Federal, State, and local decision makers and public health officials must prepare and exercise complex plans to contend with a variety of possible mass casualty events, such as pandemic influenza. Through the provision of quick look tools (QLTs) focused on mass casualty events, such planning can be done with higher accuracy and more realism through the combination of interactive simulation and visualization in these tools. If an event happens, the QLTs can then be employed to rapidly assess and execute alternative mitigation strategies, and thereby minimize casualties. This can be achieved by conducting numerous “what-if” assessments prior to any event in order to assess potential health impacts (e.g., number of sick individuals), required community resources (e.g., vaccinations and hospital beds), and optimal mitigative decision strategies (e.g., school closures) during the course of a pandemic. In this presentation, we overview and demonstrate a pandemic influenza QLT, discuss some of the modeling methods and construct and visual analytic components and interface, and outline additional development concepts. These include the incorporation of a user selectable infectious disease palette, simultaneous visualization of decision alternatives, additional resource elements associated with emergency response (e.g., first responders and medical professionals), and provisions for other potential disaster events.

Abstract: In time-sensitive environments such as disaster management, decision-making often requires rapidly gathering the information from diverse data sources and then visualizing the collected information to understand it. Thus, it is critical to reduce the overhead in data integration and visualization for efficient decision-making. Geospatial mashups can be an effective solution in such environments by providing an integrated approach to extract, integrate and view diverse information. Currently, mashup building tools exist for creating mashups, but none of them deal with the issue of data visualization. An improper visualization of the data could result in users wasting precious time to understand the data. In this paper, we introduce a programming-by-demonstration approach to data visualization in geospatial mashups that allows the users to customize the data visualization.

Abstract: To face the urgent need to train strategic and operational managers in dealing with complex crises, we are researching and developing an innovative decision support system to be used for crisis management and interactive crisis training. This paper provides an overview of current decision-support systems, simulation software and other technologies specifically designed to serve crisis managers. These findings inform the design of a new interactive simulation technology system, where a 3D Common Operational Picture (COP) is shared between tactile digital whiteboard in the command center and mobile devices in the field. © 2012 ISCRAM.

Abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.

Abstract: The development of sustainable tsunami early warning systems (TEWS) requires the adoption of proven standards for components on all system levels. This is crucial to ensure the successful operation of the overall system in the long term. Currently, components, data formats and models used to build an individual TEWS come from independent development efforts, using non-standardized proprietary interfaces. Integrating these components into a TEWS requires additional work effort due to the proprietary technologies and formats. This article discusses alternative cost-effective approaches. The successful integration of the TEWS system components depends critically on the adoption and application of industry standards and good practices. From this perspective, this article examines the role of tsunami simulation models, and the challenge to integrate the data products generated from independent tsunami models for a TEWS. The significance of tsunami simulation products, consisting of data and metadata, for the overall early warning workflow is described, including data exchange (among multiple TEWS) and information visualization in combination with additional spatial information. As an outcome, the use of standardized data formats for simulation products is recommended for future work. This approach is demonstrated on a simulation of the March 2011 Tohoku-Oki mega thrust earthquake.

Abstract: Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media.

Abstract: In this paper, we describe AsonMaps, a platform for collection, aggregation, visualization and analysis of near real-time, geolocated quantifiable information from a variety of heterogeneous social media outlets in order to provide emergency responders and other coordinating federal agencies not only with the means of listening to the affected population, but also to be able to incorporate this data into geophysical and probabilistic disaster forecast models that guide their response actions. Hurricane Sandy disaster is examined as a use-case scenario discussing the different types of quantifiable information that can be extracted from Instagram and Twitter.

Abstract: The role of common citizens within the emergency management (EM) process is crucial in order to support efficiently the operators' activities during the response phase. Moreover, their participation is strictly related to their profile and their experience in previous events. In a previous contribution we identified the different roles that the citizens can play for an effective cooperation with the EM workers. In this paper, we introduce an emergency tool based on a mobile application designed to support the activities of the citizens acting as Agent. The Agents have specific capabilities recognized by the EM Operation Center (OC) to execute actions under the remote supervision of the EM operators. The proposed tool allows the Agents to receive information from the OC and to visualize it through an advanced visualization modality. In particular, available information is previously collected by the center from the witnesses and the affected people that have alerted about it.

Abstract: Emergency situations are characterized by their complexity and the heterogeneity of the available information. Emergency managers are frequently confronted with redundant or irrelevant information, causing the problem of information overload. Evidence of this problem was identified in an exploratory survey conducted in the Center for Integrated Command and Control of Rio de Janeiro (CICC-RJ). In this paper, we present a conceptual architecture that allows a user to handle this influx of information. From a set of available data, a manager can select those of interest, which can then be transformed and mapped into one or more views, and organized in a dashboard. The whole process is interactive, allowing the manager to redefine his/her dashboard as needed. In addition, we provide collaborative mechanisms, given that, at times, it is not possible for a single user to handle such large datasets alone.