Information Sharing and Coordination in Collaborative Flood Warning and Response Systems

The introduction of new information and communication technologies enables communities to share information and self-organize in the response to disasters. Crowd-sourcing approaches enable professional authorities to capture information from the ground in real-time. However, there is a gap between the professional and community-driven response: locally emergent initiatives may lack the overview needed for efficient coordination, while decisions taken by professionals may not consider the actual situation on the ground. We study this information sharing and coordination gap through the lens of urban flood early warning and response systems. Based on a literature review combining academic articles as well as guidelines and reports from practice, we derive design principles for these systems. Considering the case study of Accra, specific requirements are individuated. The design principles are then used to address the requirements, resulting in a set of functionalities for a collaborative flood warning and response system. These functionalities provide the basis for further development and evaluation

Determining the accuracy of crowdsourced tweet verification for auroral research

The Aurorasaurus citizen science project harnesses volunteer crowdsourcing to identify sightings of an aurora (or the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter but with the caveat that there is a high level of accompanying non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by utilizing volunteers to sift through a pre-filtered list of geo-located tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geo-locating tweets, is described and its accuracy (which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings and care must therefore be taken when relying on crowdsourced identification

Identifying success factors in crowdsourced geographic information use in government

Crowdsourcing geographic information in government is focusing on projects that are engaging people who are not government officials and employees in collecting, editing and sharing information with governmental bodies. This type of projects emerged in the past decade, due to technological and societal changes - such as the increased use of smartphones, combined with growing levels of education and technical abilities to use them by citizens. They also flourished due to the need for updated data in relatively quick time when financial resources are low. They range from recording the experience of feeling an earthquake to recording the location of businesses during the summer time. 50 cases of projects in which crowdsourced geographic information was used by governmental bodies across the world are analysed. About 60% of the cases were examined in 2014 and in 2017, to allow for comparison and identification of success and failure. The analysis looked at different aspects and their relationship to success: the drivers to start a project; scope and aims; stakeholders and relationships; inputs into the project; technical and organisational aspect; and problems encountered. The main key factors of the case studies were analysed with the use of Qualitative Comparative Analysis (QCA) which is an analytical method that combines quantitative and qualitative tools in sociological research. From the analysis, we can conclude that there is no “magic bullet” or a perfect methodology for a successful crowdsourcing in government project. Unless the organisation has reached maturity in the area of crowdsourcing, identifying a champion and starting a project that will not address authoritative datasets directly is a good way to ensure early success and start the process of organisational learning on how to run such projects. Governmental support and trust is undisputed. If the choice is to use new technologies, this should be accompanied by an investment of appropriate resources within the organisation to ensure that the investment bear fruits. Alternatively, using an existing technology that was successful elsewhere and investing in training and capacity building is another path for success. We also identified the importance of intermediary Non-Governmental Organizations (NGOs) with the experience and knowledge in working with crowdsourcing within a partnership. These organizations have the knowledge and skills to implement projects at the boundary between government and the crowd, and therefore can offer the experience to ensure better implementation. Changes and improvement of public services, or a focus on environmental monitoring can be a good basis for a project. Capturing base mapping is a good point to start, too. The recommendation of the report address organisational issues, resources, and legal aspects

How to See the Future : Forecasting and Global Policy

To help bridge this gap and advance discussions on forecasting, Perry World House convened a two-day colloquium focused on "How to See the Future: Forecasting and Global Policy" on September 27–28, 2021. The colloquium was animated by a simple belief: Better forecasts can facilitate better policy. When governments can rank the probabilities of global threats, when they can understand the factors that increase the likelihood of a global pandemic or a terrorist attack,and when they can have more accurate information about their adversaries' likely actions, they can tailor policy more accurately to the world's most pressing problems.

Citizen Science: Reducing Risk and Building Resilience to Natural Hazards

Natural hazards are becoming increasingly frequent within the context of climate change—making reducing risk and building resilience against these hazards more crucial than ever. An emerging shift has been noted from broad-scale, top-down risk and resilience assessments toward more participatory, community-based, bottom-up approaches. Arguably, non-scientist local stakeholders have always played an important role in risk knowledge management and resilience building. Rapidly developing information and communication technologies such as the Internet, smartphones, and social media have already demonstrated their sizeable potential to make knowledge creation more multidirectional, decentralized, diverse, and inclusive (Paul et al., 2018). Combined with technologies for robust and low-cost sensor networks, various citizen science approaches have emerged recently (e.g., Haklay, 2012; Paul et al., 2018) as a promising direction in the provision of extensive, real-time information for risk management (as well as improving data provision in data-scarce regions). It can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. This Research Topic compiles 13 contributions that interrogate the manifold ways in which citizen science has been interpreted to reduce risk against hazards that are (i) water-related (i.e., floods, hurricanes, drought, landslides); (ii) deep-earth-related (i.e., earthquakes and volcanoes); and (iii) responding to global environmental change such as sea-level rise. We have sought to analyse the particular failures and successes of natural hazards-related citizen science projects: the objective is to obtain a clearer understanding of “best practice” in a citizen science context

Global Mapping of Citizen Science Projects for Disaster Risk Reduction

Citizen science for disaster risk reduction (DRR) holds huge promise and has demonstrated success in advancing scientific knowledge, providing early warning of hazards, and contributed to the assessment and management of impacts. While many existing studies focus on the performance of specific citizen science examples, this paper goes beyond this approach to present a systematic global mapping of citizen science used for DRR in order to draw out broader insights across diverse methods, initiatives, hazards and country contexts. The systematic mapping analyzed a total of 106 cases of citizen science applied to DRR across all continents. Unlike many existing reviews of citizen science initiatives, relevance to the disaster risk context led us to ‘open up’ our mapping to a broader definition of what might constitute citizen science, including participatory research and narrative-based approaches. By taking a wider view of citizen science and opening up to other disciplinary practices as valid ways of knowing risks and hazards, we also capture these alternative examples and discuss their relevance for aiding effective decision-making around risk reduction. Based on this analysis we draw out lessons for future research and practice of citizen science for DRR including the need to: build interconnections between disparate citizen science methods and practitioners; address multi-dimensionality within and across hazard cycles; and develop principles and frameworks for evaluating citizen science initiatives that not only ensure scientific competence but also attend to questions of equity, responsibility and the empowerment of those most vulnerable to disaster risk

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

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