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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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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, 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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