Dunn, M. M. (2023). Aphorme: An Intralingual Translation Tool for Emergency Management and Disaster Response. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1033–1041). Omaha, USA: University of Nebraska at Omaha.
Abstract: While multilingual translation needs (from one or more language(s) to one or more others) in disaster events are a “perennial issue” among responders in crisis-affected communities (Crowley & Chan, 2011) and calls are being made to consider the access to (and translation of) information during crisis a human right (Greenwood et al., 2017), the literature that deals with intralingual translation in disaster is limited in places where it should thrive, such as crisis communication, translation studies, and rhetoric. Intralingual translation is of increasing relevance in disaster not only because of potential variability in literacy levels among those affected (O’Brien, 2020) but because responding to/planning for disaster requires an understanding of the ‘operational’ terms used (but not always shared) by other responding agencies in the field. This paper calls for increased attention to intralingual translation needs in disaster and introduces a translation technology (“Aphorme”) designed to mitigate those needs.
|
|
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.
|
|
Savannah Thais, Shaine Leibowitz, Allie Saizan, & Ashay Singh. (2022). Understanding Historical, Socio-Economic, and Policy Contributions to COVID-19 Health Inequities. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 481–494). Tarbes, France.
Abstract: The COVID-19 pandemic has generated unprecedented, devastating impacts across the United States. However, some communities have disproportionately endured adverse health outcomes and socioeconomic injuries. Ascertaining the factors driving these inequities is crucial to determining how policy could mitigate the impacts of future public health crises. We have established research-driven metrics, aggregated as the Community Vulnerability Index (CVI), that quantify vulnerability to public health and economic impacts of COVID-19. We performed two analyses to better understand similarities between communities in terms of the vulnerabilities represented by the metrics. We performed an unsupervised k-means clustering analysis to understand whether communities can be grouped together based on their levels of negative social and health indicators. Our goal for this analysis is to determine whether attributes of the constructed clusters reveal areas of opportunity for potential policy impacts and future disaster response efforts. We also analyzed similarities between communities across time using time-sensitive clustering analysis to discover whether historical community vulnerabilities were persistent in the years preceding the pandemic and to better understand the historical factors associated with disparate COVID-19 impacts. In particular, we highlight where communities should invest based on their historical health and socioeconomic patterns and related COVID impacts. Through extensive interpretation of our findings, we uncover how health policy can advance equity and improve community resilience.
|
|
Ylenia Casali, Nazli Yonca Aydin, & Tina Comes. (2021). Zooming into Socio-economic Inequalities: Using Urban Analytics to Track Vulnerabilities – A Case Study of Helsinki. 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. 1028–1041). Blacksburg, VA (USA): Virginia Tech.
Abstract: The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators.
|
|
Gah-Kai Leung. (2021). Reducing Flood Risks for Young People in the UK Housing Market. 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. 481–487). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is one of the most serious natural hazards faced in the UK. The Environment Agency estimates that in England alone, about 5.2 million properties are at risk of flooding, or roughly one in six (2009: 3). Flooding imposes significant financial, psychological and social burdens on households and these may be especially acute for young people in the property market, such as renters and first-time buyers. This paper examines how housing-related policy can help alleviate the burdens of flooding on young people in the housing market. First, it canvasses the kinds of damage inflicted when flooding affects properties. Second, it discusses the financial burdens imposed by such damage. Third, it enumerates the financial burdens and benefits of measures to protect against flooding. Fourth, it considers the non-monetary burdens of flooding, in the form of psychological and social burdens. Finally, the paper offers some policy recommendations in light of the preceding discussion.
|
|