Emma Hudson-Doyle, Douglas Paton, & David Johnston. (2018). Reflections on the communication of uncertainty: developing decision-relevant information. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 166–189). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Successful emergency management decision-making during natural hazard events is fundamentally dependent upon individual and team situation awareness (i.e., how selection, interpretation, and understanding of available information defines the problem and identifies solutions) while operating under high time and risk pressures. The development and evolution of SA, and response effectiveness during a crisis, depends upon information and advice from external experts. This advice is characterised by stochastic (system variability) and epistemic (lack of knowledge) uncertainty, constraining decision-making and blocking or delaying action. How this uncertainty is communicated, and managed, varies throughout the phases of emergency management. Through this 'Insight' paper, we review how people cope with uncertainty, individual and team factors that affect uncertainty communication, and inter-agency methods to enhance communication. We propose communicators move from a one-way dissemination of advice, towards two-way and participatory approaches that identify decision-relevant uncertainty information needs pre-event, for communication efforts to focus on in-event.
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Iva Seto, David Johnstone, & Jennifer Campbell-Meier. (2018). Experts' sensemaking during the 2003 SARS crisis. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 44–55). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: This paper depicts the real-time sensemaking of experts as they worked to combat the first emerging disease of the 21st century: Severe Acute Respiratory Syndrome (SARS). Newspaper data was analysed from the 2003 SARS crisis, with a Canadian perspective, to follow the process of solving the puzzle of this emerging disease. Retrospective sensemaking is a process that is triggered by the unexpected, which leads to actors gathering information (taking action) in order to consider possible interpretations for the unexpected event. Disease outbreaks serve as sensemaking triggers, and actors engage in retrospective sensemaking to find out the factors involved in how the outbreak happened. Prospective sensemaking (future-oriented) is employed when actors work together to plan how to combat the disease. The newspaper data demonstrate that retrospective and prospective sensemaking are tethered: to make plans to combat a disease, actors first require a collectively agreed upon understanding from which they can generate possibilities for a crisis response. This paper contributes to the field by providing concepts for long-duration crisis sensemaking, as the bulk of organisational research focuses on acute crises such as wildfires, or earthquakes.
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Marion Lara Tan, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, Graham Leonard, & David Johnston. (2019). Enhancing the usability of a disaster app: exploring the perspective of the public as users. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Limited research has studied how citizens? perspectives as end-users can contribute to improving the usability of disaster apps. This study addresses this gap by exploring end-user insights with the use of a conceptual disaster app in the New Zealand (NZ) context. NZ has multiple public alerting authorities that have various technological options in delivering information to the population?s mobile devices; including social media platforms, apps, as well as the Emergency Mobile Alert system. However, during critical events, the multiplicity of information may become overwhelming. A disaster app, conceptualised in the NZ context, aims to aggregate, organise, and deliver information from official sources to the public. After the initial conceptual design, a usability inquiry was administered by interviewing members of the public. Partial results of the inquiry show that the public?s perspective has value; in the process of understanding the new user?s viewpoint, usability highlights and issues are identified.
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Marion Lara Tan, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, Graham Leonard, & David Johnston. (2018). Usability Factors Affecting the Continuance Intention of Disaster Apps. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 326–338). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Multiple disaster mobile applications (apps) already exist for public use; however, availability does not automatically translate to continued usage. Limited research has explored whether disaster apps are usable and whether the apps' usability affects users' intent for continued use. The paper presents a work-in-progress study that aims to test a usability-continuance intention model for the specific context of disaster apps. The study theorises seven usability factors that influence continued intention to use. An online usability survey was used to gather user experience data on disaster apps. Initial findings, through structural equational modelling, showed that five of the seven usability factors have a significant relationship to continuance intention. Although the relationships have different weights and directions, key influencers to users' intent to continue usage are app utility, app dependability, interface output, interface input, and interface graphics. The next step of the study will investigate the mediating effects of the factors and the moderating effects of users' experience and technological comfort.
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Miles Crawford, Wendy Saunders, Emma Hudson-Doyle, & David Johnston. (2018). End-user perceptions of natural hazard risk modeling across policy-making, land-use planning, and emergency management within New Zealand local government. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 550–560). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: While the development of risk modelling has focussed on improving model accuracy and modeller expertise, less consideration has been given to understanding how risk models are perceived and used by the end-user. In this think-piece, we explore how risk modelling is perceived and used by three different end-user functions for natural hazard risk management in New Zealand local government: policy-making, land-use planning, and emergency management. We find that risk modelling is: valued and used by policy-makers; less valued within land-use planning and not as widely used; and valued within emergency planning but not as widely used. We offer our thoughts as to why this is the case with reference to focus groups and qualitative interviews held with local government natural hazard risk end-users across the Wellington, Hawke's Bay and Gisborne regions of New Zealand. We conclude with recommendations for how risk modelling can be further developed to increase community resilience.
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Nilani Algiriyage, Raj Prasanna, Emma E H Doyle, Kristin Stock, & David Johnston. (2020). Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 100–109). Blacksburg, VA (USA): Virginia Tech.
Abstract: Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps.
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Nilani Algiriyage, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, David Johnston, Minura Punchihewa, et al. (2021). Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 40–48). Blacksburg, VA (USA): Virginia Tech.
Abstract: Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions.
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Nilani Algiriyage, Rangana Sampath, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, & David Johnston. (2021). Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 731–743). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research.
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Thomas Huggins, Stephen Hill, Robin Peace, & David Johnston. (2018). Extending Ecological Rationality: Catching the High Balls of Disaster Management. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 295–309). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: The contemporary world is characterized by several large-scale hazards to human societies and the environments we live in, including the impacts of climate change. This paper outlines theories concerning cognitive psychology and complexity dynamics that help explain the challenges of responding to these hazards and the complex systems which create them. These theories are illustrated with a baseball metaphor, to highlight the need for decision-making strategies which do not rely on comprehensive information where comprehensive information is not available. The importance of tools which can support more efficient uses of limited information is also outlined, as is the way that these tools help combine the computational resources and acquired experience of several minds. Existing research has been used to investigate many of the concepts outlined. However, further research is required to coalesce cognitive theories with complexity theories and the analysis of group-level interactions, towards improving important disaster management decisions.
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Yasir Javed, Tony Norris, & David Johnston. (2012). Evaluating SAVER: Measuring shared and team situation awareness of emergency decision makers. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Large scale emergencies are usually responded to by a team of emergency managers or a number of sub teams for safety and efficiency. Team coordination has attracted considerable research interest, especially from the cognitive, human factors, and ergonomic aspects because shared situation awareness (SSA) and team situation awareness (TSA) of team members are critical for optimal decision making. This paper describes the development of an information system (SAVER) based on SSA and TSA oriented systems design. Validation and evaluation of the implemented design show that decision performance is improved by the SAVER system. © 2012 ISCRAM.
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Yasir Javed, Tony Norris, & David Johnston. (2011). Ontology-based inference to enhance team situation awareness in emergency management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper, we propose the use of an ontology-based and semantic technologies approach to improving shared situation awareness amongst teams dealing with emergency situations. We have also identified that shared and team situation awareness tends to be viewed only in terms of cooperative task completion and so we have tried to describe their important relationship with team decision making. The applicability of our approaches is demonstrated by a case study of mass evacuation in the case of a tsunami event. We show how ontology can be used to represent context-based situations and how the axioms and rules can improve team situation awareness.
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Yasir Javed, Tony Norris, & David Johnston. (2010). Design approach to an emergency decision support system for mass evacuation. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper is directed primarily to investigating the information needs of emergency managers following recognition of a risk of volcanic eruption. These needs include type of information required during the collection, integration, synthesis, presentation, and sharing of information. This will identify and model the processes underpinning the design of an emergency decision support system (EDSS). Exploration of the information needs, flows, and processes involved in emergency decision making can improve the design of EDSS both in terms of their content and the all-important human-system interfaces that determine their usability.The information attributes and flows then lead to the development of a prototype system that can be evaluated to test and refine the concepts.
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