Vihan C.N. Weeraratne, Raymond C.Z. Cohen, Mahesh Prakash, Lalitha Ramachandran, Nikhil Garg, & Valentijn Pauwels. (2023). Assessing Climate Vulnerability Under Future Changes to Climate, Demographics and Infrastructure: A Case Study for the Chapel Street Precinct, Melbourne. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 35–44). Palmerston North, New Zealand: Massey Unversity.
Abstract: The Chapel Street Precinct is a busy commercial and residential corridor in the City of Stonnington Local Government Area (LGA) located in metropolitan Melbourne, Australia. Authorities and planners in the LGA are interested in understanding how the changing climate affects the socioeconomic environment of the region. By considering existing climate hazards (such as extreme heat, flood and water availability), infrastructure, and demographic information in the region together with future projections of climate change and demographic changes, a Socioeconomic Vulnerability Index (SVI) was created at a Mesh Block scale to better identify relatively high-risk Mesh Blocks in the region. The climate projections under medium and high future emission scenarios (i.e., representative concentration pathways (RCP)) as per IPCC (Intergovernmental Panel on Climate Change) fifth assessment report (AR5), RCP4.5 and RCP8.5 respectively for 30-year epochs around 2030, 2050 and 2070 were used in the SVI development. The current-day scenario is considered under Baseline conditions for demographic and asset information representing present-day conditions, whereas the baseline climate dataset considers the climate for the 30 year period 1991-2020 to best represent the present-day climate. The multi-model mean of the future climate projections from 6 different climate models were obtained from the Victoria’s Future Climate tool (https://vicfutureclimatetool.indraweb.io), developed by CSIRO (Commonwealth Scientific and Industrial Research Organisation) Data61 together with the Department of Environment, Land, Water and Planning (DELWP) under Data61’s INDRA framework (https://research.csiro.au/indra/). A version of INDRA is currently under development to allow map-based interactivity, experimentation and scrutiny of the vulnerability indices and their subcomponents across the study region. The SVI was created using a weighted indicator approach utilising a range of indicators belonging to 3 categories, exposure, susceptibility, and baseline adaptive capacity. The indicators were first normalised and the final SVI was given a score between 0-1 for each Mesh Block. The worst levels of vulnerability were observed to be for the RCP8.5 2070 scenario. In general, the RCP8.5 scenarios indicated a worse outcome compared to the RCP4.5 scenario. The area along Chapel Street within the precinct which is a densely built-up area high in population was found to be the most vulnerable area in the study region. It is foreseen that decision makers will be able to use the holistic data-driven outcomes of this study to make better informed decisions whilst adapting to climate change.
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Jens Kersten, Jan Bongard, & Friederike Klan. (2022). Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 664–673). Tarbes, France.
Abstract: Overload reduction is essential to exploit Twitter text data for crisis management. Often used pre-trained machine learning models require training data for both, crisis-related and off-topic content. However, this task can also be formulated as a one-class classification problem in which labeled off-topic samples are not required. Gaussian processes (GPs) have great potential in both, binary and one-class settings and are therefore investigated in this work. Deep kernel learning combines the representative power of text embeddings with the Bayesian formalism of GPs. Motivated by this, we investigate the potential of deep kernel models for the task of classifying crisis-related tweet texts with special emphasis on cross-event applications. Compared to standard binary neural networks, first experiments with one-class GP models reveal a great potential for realistic scenarios, offering a fast and flexible approach for interactive model training without requiring off-topic training samples and comprehensive expert knowledge (only two model parameters involved).
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Erik Borglund, & Jonas Hansson. (2022). Tactical Police Interventions: Design Challenges for Situational Awareness. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1037–1047). Tarbes, France.
Abstract: Police officers’ situational awareness during tactical intervention can be crucial for how they act and whether they use the correct level of force in extreme situations. This paper presents preliminary findings in ongoing research focusing on police tactical interventions and situational awareness. Twenty-one police officers were interviewed, and a video sequence of a shorter car chase was used to set the scene in the interviews. The interviewed police officers described their tactical decisions applying the standardized tactical approach applied in the Swedish police. In the analysis, a focus on how situational awareness is gained and how situational awareness is affected by tactical decisions is presented. The study indicates that the situational awareness process begins before the actual intervention (pre-intervention phase). During the actual intervention, situational awareness is very complex. Technology supporting police officers’ cognition, as well as management and control of one or many risk areas, is identified.
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Cody Buntain, Richard Mccreadie, & Ian Soboroff. (2022). Incident Streams 2021 Off the Deep End: Deeper Annotations and Evaluations in Twitter. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 584–604). Tarbes, France.
Abstract: This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.
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Clara Le Duff, Jean-Philippe Gitto, Julien Jeany, Raphaël Falco, Matthieu Lauras, & Frederick Benaben. (2022). A Physics-based Approach to Evaluate Crisis Impacts on Project Management. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 134–143). Tarbes, France.
Abstract: Project management has become a standard in business. Unfortunately, the projects as well as companies are increasingly subject to major disruptions. In this context, it is of prime importance to have the ability to manage the risks inherent to these projects to best achieve their objectives. The existing approaches of crisis management in the literature no longer seem to be adapted to this new normality. The future of research lies in a more systematic crisis assessment and a better conceptualization of the uncertainty associated with risks. It is necessary to rely on the collection of heterogeneous data in order to maximize the understanding of the project environment and to find a way that best describes and visualizes the influence of crises on the project management processes. This article uses the POD approach and applies it in the context of project management to address these issues.
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