Penadés, M. ª.C., Núñez, A. G., Canós-Cerdá, J. H., & Coronel, J. (2023). DIMEPRO: a tool for diagnosis and improvement of the self-protection plans management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1071). Omaha, USA: University of Nebraska at Omaha.
Abstract: Ensuring people's protection is an increasingly demanded requirement, both at political and corporate levels. Within the Spanish legislative system, organizations are obliged by law to develop and maintain the so-called Self-Protection Plan (SPP). In this work, we introduce DIMEPRO, a tool that provides organizations with means to evaluate and improve the management of their SPPs (SPPM). DIMEPRO is based on the QuEP-framework, which evaluates the maturity an organization has reached regarding its SPPM. DIMEPRO aims to become the reference tool for organizations chosen to improve their self-protection; to the best of our knowledge, DIMEPRO represents a pioneer system in addressing this problem since no other tools have addressed the same problem. This tool allows the diagnosis of SPPM and provides a set of best practices that will allow a roadmap for its improvement. The results of the assessments are displayed in dashboards, as well as in reports of different natures.
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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.
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David Wodak, & Kenny Meesters. (2021). How To Improve HO/TO's: An Exploratory Study on The Alignment Between Information, Technology And Crisis teams. 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. 459–470). Blacksburg, VA (USA): Virginia Tech.
Abstract: In the last decade, the number of crises has increased, and have become more complex. Crisis response does not only focus on rescue operations, or separate stages but rather it is an integrated and continuous process. During this continuous process, several handovers take place. A handover is an important, critical but challenging moment during a crisis, due to the organizational factors that influence the handover and the technology used to transfer information. Since these are crucial elements of a Crisis handover, it would indicate that the alignment between these factors could lead to the improvement of Crisis handovers. However, certain barriers resulted in a lack of alignment. An important barrier originates from the organizational processes. These have a lack of focus on which crisis managers are involved in the handover and thus create a lack of alignment between the systems and information used by various crisis teams.
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Carolin Klonner, Melanie Eckle, Tomás Usón, & Bernhard Höfle. (2017). Quality Improvement of Remotely Volunteered Geographic Information via Country-Specific Mapping Instructions. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 939–947). Albi, France: Iscram.
Abstract: Volunteered geographic information can be seen as valuable data for various applications such as within disaster management. OpenStreetMap data, for example, are mainly contributed by remote mappers based on satellite imagery and have increasingly been implemented in response actions to various disasters. Yet, the quality often depends on the local and country-specific knowledge of the mappers, which is required for performing the mapping task. Hence, the question is raised whether there is a possibility to train remote mappers with country-specific mapping instructions in order to improve the quality of OpenStreetMap data. An experiment is conducted with Geography students to evaluate the effect of additional material that is provided in wiki format. Furthermore, a questionnaire is applied to collect participants' socio-demographic information, mapping experience and feedback about the material. This pre-study gives hints for future designs of country-specific mapping instructions as well as the experiment design itself.
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Lívia Castro Degrossi, João Porto de Albuquerque, Roberto dos Santos Rocha, & Alexander Zipf. (2017). A Framework of Quality Assessment Methods for Crowdsourced Geographic Information: a Systematic Literature Review. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 532–545). Albi, France: Iscram.
Abstract: Crowdsourced Geographic Information (CGI) has emerged as a potential source of geographic information in different application domains. Despite the advantages associated with it, this information lacks quality assurance, since it is provided by different people. Therefore, several authors have started investigating different methods to assess the quality of CGI. Some of the existing methods have been summarized in different classification scheme. However, there is not an overview of the methods employed to assess the quality of CGI in the absence of authoritative data. On the basis of a systematic literature review, we found 13 methods that can be employed to this end.
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