Abstract: This paper presents descriptive accounts from the work conducted at a Swedish fire and rescue service's command centre during a high risk protest. Sensemaking has been applied as a theoretical lens on the empirical data. Two specific episodes has been selected to demonstrate how the process of sensemaking is manifested in the context of the command centre. The paper also includes implications for design suggesting how information systems for this context should be designed to better support sensemaking.

Abstract: Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a more strict separation of responsibilities between forecasters and decision maker can be made. A recent study identified some issues related to the effective use of probability forecasts. These add a dimension to an already multi-dimensional problem, making it increasingly difficult for decision makers to extract relevant information from a forecast. Secondly, while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be fully known, including estimates of flood damage and costs and effect of damage reducing measures. Here, we present suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development is outlined.

Abstract: Forecast and plan response to incidents are fundamental to create a Command and Control Center (C2 Center). However, some incidents are considered chaotic and are completely understood only after happening. These unforeseen incidents pose challenges to plans of such centers and if not properly managed, may result in failures. This article describes how the Integrated C2 Center of Rio de Janeiro City (CICC-RJ) responds to violent, unexpected and improbable events, especially related to protests that took place during the 2013 FIFA Confederations Cup. It aims to describe from the resilience engineering point of view how the CICC-RJ function to cope with incidents, where the structure has proved to be resilient, where it holds brittleness, and to suggest possible actions to help the center to become more resilient to upcoming events.

Abstract: This paper describes the basic work performance analysis from three research projects with a goal to investigate the impact of a decision support system that presents global positioning system (GPS) information to the decision makers in crisis management organizations. The goal was to compare the performance between teams that had access to GPS information in the command post with teams that had access only to paper maps. The method used was controlled experiments with the C3Fire micro-world. A total of 304 participants, forming 48 teams, participated in the three studies. The participants came from three different groups, university students, municipal crisis management organizations and rescue service personnel. The result shows that the performance and communication change depending on if the teams used GPS support or paper maps. The result also shows that the participants' background and perceived complexity of the task have an impact on the results.

Abstract: In this contribution, we introduce a prototype of a decision support tool for cost-optimal response in security management. The threat situation of a closed infrastructure, exposed to multiple threats, and the corresponding response actions are modeled by a continuous-time Markov decision process (CMDP). Since the CMDP cannot be solved exactly for large infrastructures, the response actions are determined from a heuristic, based on an index rule. The decision support tool's user interface displays the infrastructure's current threat state and proposes the heuristic response actions to the decision maker. In this way, global situation awareness can be enhanced and the decision maker is able to initiate an almost cost-optimal response action in short time.

Abstract: Accurate predictions of severe weather events are extremely important for society, economy, and environment. Due to the fact that weather forecasts are inherently uncertain, it is required to give information about forecast uncertainty to all users providing weather forecasts in probabilistic terms utilizing ensemble forecasts. Since ensemble forecasts tend to be under dispersive and biased, they need to be calibrated with statistical methods. This paper presents a method for the calibration of temperature forecasts using Gaussian regression, and the calibration of wind gust forecasts with a box-cox t-distribution method. Statistical calibration was made for the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (ENS) forecasts for lead times from 3 to 360 hours. The verification results showed that calibration improved both temperature and wind gust ensemble forecasts. The probabilistic temperature forecasts were better after calibration over whole lead time scale, but the probabilistic wind gust forecasts up to 240 hours.

Abstract: The use, or barriers to use, of crowdsourced data by emergency managers has been a significant topic of scholarly discussion during the past several years. The single strongest barrier to use has been identified as one of data quality (Tapia, et. al, 2011). We argue that within this environment the Emergency Manager (EM) acts as a decision-maker and evaluator of crowdsourced data. The final judgement on whether to incorporate crowdsourced data into a Crisis response lies with the EM. In this paper we make a brief argument for the role of EM as trustworthy data analyst and then propose a model for capturing the trust-analytical behavior through game theory (Griffin, et. al, 2012). Lastly, we propose a simple computer game, which uses our model through which we will capture EM trust-analytical behavior though a future empirical data collection effort.

Abstract: Over the last two decades a significant number of projects tried to convey the concept of business process man-agement to the domain of emergency management. Most of these approaches sought a partial automation for the execution of standard operating procedures in the sense of workflow support, while others strived for the sup-port and integration of information management and data streams in command centres during a crisis. This pa-per focuses on the planning of disaster response for reasons of better preparedness. It discusses whether emer-gency management organisations can capitalize on off-the-shelf business process modelling tools to prepare for disasters more effectively, and whether the concepts of process modelling can be applied to standard operating procedures and vice versa. Moreover, it investigates whether such tools can efficiently support a collaborative preparation of police, fire departments, and rescue organizations. This paper will demonstrate why conventional business process means are inapplicable as planning tool in this domain. And it will also give an outlook to so called smart checklists that might be better suited both for the planning and response phase of emergency epi-sodes. © 2012 ISCRAM.

Abstract: Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.