Increasing resilience to natural hazards through crowd-sourcing in St. Vincent and the Grenadines

In this project we aim to demonstrate how volcanic environments exposed to multiple hazards tend to be characterised by a lack of relevant data available both in real time and over the longer term (e.g. months to years). This can be at least partially addressed by actively involving citizens, communities, scientists and other key stakeholders in the collection, analysis and sharing of observations, samples and measurements of changes in the environment. Such community monitoring and co-production of knowledge over time can also build trusting relationships and resilience (Stone et al. 2014). There are more than 100 institutions worldwide that monitor volcanoes and other natural hazards, contribute to early warning systems and are embedded in communities. They have a key role in building resilience alongside civil protection/emergency management agencies. In this report, we propose that such institutions are involved in big data initiatives and related research projects. In particular, we suggest that tools for crowd-sourcing may be of particular value. Citizen science, community monitoring and analysis of social media can build resilience by supporting: a) coordination and collaboration between scientists, authorities and citizens, b) decision-making by institutions and individuals, c) anticipation of natural hazards by monitoring institutions, authorities and citizens, d) capacity building of institutions and communities, and e) knowledge co-production. We propose a mobile phone app with a supporting website as an appropriate crowd-sourcing tool for St Vincent and the Grenadines. The monitoring institution is the key contact for users and leads on the required specifications based on local knowledge and experience. Remote support is provided from the UK on technical issues, research integration, data management, validation and evaluation. It is intended that the app facilitates building of long-term relationships between scientists, communities and authorities. Real-time contributions and analysis of social media support early warning, real-time awareness and real-time feedback enhancing the response of scientists and authorities. The app has potential to facilitate, for example, discussions on new or revised hazards maps, multiple hazard analysis and could contribute to real-time risk monitoring. Such an approach can be scaled up to facilitate regional use – and is transferable to other countries. Challenges of such an approach include data validation and quality assurance, redundancy in the system, motivating volunteers, managing expectations and ensuring safety. A combination of recruiting a core group of known and reliable users, training workshops, a code of conduct for users, identifying information influx thresholds beyond which external support might be needed, and continuing evaluation of both the data and the process will help to address these issues. The app is duplicated on the website in case mobile phone networks are down. Development of such approaches would fit well within research programmes on building resilience. Ideally such research should be interdisciplinary in acknowledgement of the diversity and complexity of topics that this embraces. There may be funding inequality between national monitoring institutions and international research institutions but these and other in-country institutions can help drive innovation and research if they are fully involved in problem-definition and research design. New innovations arising from increasing resolution (temporal and spatial) of EO products should lead to useful near-real time products from research and operational services. The app and website can ensure such diverse products from multiple sources are accessible to communities, scientists and authorities (as appropriate). Other innovations such as machine learning and data mining of time-series data collected by monitoring institutions may lead to new insights into physical processes which can support timely decision-making by scientists in particular (e.g. increasing alert levels)

Reasoning with Mixed Qualitative-Quantitative Representations of Spatial Knowledge

Drastic transformations in human settlements are caused by extreme events. As a consequence, descriptions of an environment struck by an extreme event, based on spatial data collected before the event, become suddenly unreliable. On the other hand, time critical actions taken for responding to extreme events require up-to-date spatial information. Traditional methods for spatial data collection are not able to provide updated information rapidly enough, calling for the development of new data collection methods. Reports provided by actors involved in the response operations can be considered as an alternative source of spatial information. Indeed, reports often convey spatial descriptions of the environment. The extraction of spatial descriptions from such reports can serve a fundamental role to update existing information which is usually maintained within, and by means of, Geographic Information Systems. However, spatial information conveyed by human reports has qualitative characteristics, that strongly differ from the quantitative nature of spatial information stored in Geographic Information Systems. Methodologies for integrating qualitative and quantitative spatial information are required in order to exploit human reports for updating existing descriptions of spatial knowledge. Although a significant amount of research has been carried on how to represent and reason on qualitative data and qualitative information, relatively little work exists on developing techniques to combine the different methodologies. The work presented in this thesis extends previous works by introducing a hybrid reasoning system--able to deal with mixed qualitative-quantitative representations of spatial knowledge--combining techniques developed separately for qualitative spatial reasoning and quantitative data analysis. The system produces descriptions of the spatial extent of those entities that have been modified by the event (such as collapsed buildings), or that were not existing before the event (such as fire or ash clouds). Furthermore, qualitative descriptions are produced for all entities in the environment. The former descriptions allow for overlaying on a map the information interpreted from human reports, while the latter triggers warning messages to people involved in decision making operations. Three main system functionalities are investigated in this work: The first allows for translating qualitative information into quantitative descriptions. The second aims at translating quantitative information into qualitative relations. Finally, the third allows for performing inference operations with information given partly qualitatively and partly quantitatively for boosting the spatial knowledge the system is able to produce

Probabilistic approach to decision-making under uncertainty during volcanic crises: retrospective application to the El Hierro (Spain) 2011 volcanic crisis

© 2014, The Author(s). Understanding the potential evolution of a volcanic crisis is crucial for designing effective mitigation strategies. This is especially the case for volcanoes close to densely populated regions, where inappropriate decisions may trigger widespread loss of life, economic disruption, and public distress. An outstanding goal for improving the management of volcanic crises, therefore, is to develop objective, real-time methodologies for evaluating how an emergency will develop and how scientists communicate with decision-makers. Here, we present a new model Bayesian Decision Model (BADEMO) that applies a general and flexible, probabilistic approach to managing volcanic crises. The model combines the hazard and risk factors that decision-makers need for a holistic analysis of a volcanic crisis. These factors include eruption scenarios and their probabilities of occurrence, the vulnerability of populations and their activities, and the costs of false alarms and failed forecasts. The model can be implemented before an emergency, to identify actions for reducing the vulnerability of a district; during an emergency, to identify the optimum mitigating actions and how these may change as new information is obtained; and after an emergency, to assess the effectiveness of a mitigating response and, from the results, to improve strategies before another crisis occurs. As illustrated by a retrospective analysis of the 2011 eruption of El Hierro, in the Canary Islands, BADEMO provides the basis for quantifying the uncertainty associated with each recommended action as an emergency evolves and serves as a mechanism for improving communications between scientists and decision-makers.This research has been funded by the European Commission (FP7 Theme: ENV.2011.1.3.3-1; Grant 282759: VUELCO).Peer Reviewe

Reasoning with Mixed Qualitative-Quantitative Representations of Spatial Knowledge

Drastic transformations in human settlements are caused by extreme events. As a consequence, descriptions of an environment struck by an extreme event, based on spatial data collected before the event, become suddenly unreliable. On the other hand, time critical actions taken for responding to extreme events require up-to-date spatial information. Traditional methods for spatial data collection are not able to provide updated information rapidly enough, calling for the development of new data collection methods. Reports provided by actors involved in the response operations can be considered as an alternative source of spatial information. Indeed, reports often convey spatial descriptions of the environment. The extraction of spatial descriptions from such reports can serve a fundamental role to update existing information which is usually maintained within, and by means of, Geographic Information Systems. However, spatial information conveyed by human reports has qualitative characteristics, that strongly differ from the quantitative nature of spatial information stored in Geographic Information Systems. Methodologies for integrating qualitative and quantitative spatial information are required in order to exploit human reports for updating existing descriptions of spatial knowledge. Although a significant amount of research has been carried on how to represent and reason on qualitative data and qualitative information, relatively little work exists on developing techniques to combine the different methodologies. The work presented in this thesis extends previous works by introducing a hybrid reasoning system--able to deal with mixed qualitative-quantitative representations of spatial knowledge--combining techniques developed separately for qualitative spatial reasoning and quantitative data analysis. The system produces descriptions of the spatial extent of those entities that have been modified by the event (such as collapsed buildings), or that were not existing before the event (such as fire or ash clouds). Furthermore, qualitative descriptions are produced for all entities in the environment. The former descriptions allow for overlaying on a map the information interpreted from human reports, while the latter triggers warning messages to people involved in decision making operations. Three main system functionalities are investigated in this work: The first allows for translating qualitative information into quantitative descriptions. The second aims at translating quantitative information into qualitative relations. Finally, the third allows for performing inference operations with information given partly qualitatively and partly quantitatively for boosting the spatial knowledge the system is able to produce