Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study

Yuan, Faxi; Li, Rui

doi:10.1016/j.ijdrr.2018.02.003

Cited by 100 publications

(58 citation statements)

References 45 publications

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“…Data obtained from analyzing social media databases and image/video repositories can also be used to assess the impact of natural disasters. This can include determination of the spatial extent (Brouwer et al, ; Cervone et al, ; Jongman et al, ; Rosser et al, ) and impact/damage (de Albuquerque et al, ; Jongman et al, ; Kryvasheyeu et al, ; Vieweg et al, ) of floods, as well as the damage/injury arising from fires (Vieweg et al, ), hurricanes (Kryvasheyeu et al, ; Middleton et al, ; Yuan & Liu, ), tornadoes (Kryvasheyeu et al, ; Middleton et al, ), earthquakes (Kryvasheyeu et al, ), and mudslides (Kryvasheyeu et al, ).…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

View full text Add to dashboard Cite

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“…These characteristics fit well with the need for disaster governance to widely mobilize public participation and make effective use of collective intelligence. Crowdsourcing enables better communication and coordination between disaster victims, spontaneous volunteers, and official relief agencies [14,15], because it enables timely data for loss assessment [16] and identification of disaster problems [14,17] for enhanced rescue implementation [18]. For example, Sahana [19] is a crowdsourcing system that was built to help manage the 2004 Indian ocean tsunami.…”

Section: Introductionmentioning

confidence: 99%

Promoting Disaster Resilience: Operation Mechanisms and Self-Organizing Processes of Crowdsourcing

2020

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It is often difficult to realize effective governance and management within the inherent complexity and uncertainty of disasters. The application of crowdsourcing, through encouraging voluntary support from the general public, advances efficient disaster governance. Twelve international case studies of crowdsourcing and natural disaster governance were collected for in-depth analysis. Influenced by Complex Adaptive System theory, we explored the self-organizing operation mechanisms and self-organization processes of crowdsourcing within disaster governance. The self-organizing operation mechanisms of crowdsourcing are influenced by the multi-directional interaction between the crowdsourcing platform, the initiator (who commences the crowdsourcing process) and the contractor (who undertakes disaster reduction tasks). The benefits of crowdsourcing for governance structure and self-organization processes in natural disaster governance are reflected in three perspectives: strengthening communication and coordination, optimizing emergency decision-making, and improving the ability to learn and adapt. This paper discusses how crowdsourcing can promote disaster resilience from the perspective of the complex adaptive system to enrich the theoretical research on crowdsourcing and disaster resilience.Sustainability 2020, 12, 1862 2 of 14 enhancing community resilience. The research conducted by Baybay and Hindmarsh (2018) in coastal communities in the Philippines shows that community engagement can be increased by improving community activities, knowledge, skills, resources, and by adopting local culture and customs, which are crucial to promoting community resilience [4]. Traditional community engagement refers to offline action processes, while the Internet provides a new form of online community engagement. Crowdsourcing platforms, such as portals, social media, and online applications, have become effective tools for community engagement, by reducing the transaction costs associated with information search, analysis, and decision-making, as well as lowering resource costs (time, money, health, etc.) for civic engagement [5]. Crowdsourcing expands the boundaries of traditional community engagement, which is not limited by time or space, while people from all over the world can participate in specific actions online anytime and anywhere.The issue of how crowdsourcing promotes the self-organization process and builds disaster resilience is understudied. Existing research focuses on how crowdsourcing can be used to collect and analyze disaster data, as well as crowdsourcing's use in communication, loss assessment, and decision support. Research focuses on the functions of crowdsourcing at the technical level, but does not consider the interaction between different subjects in crowdsourcing from the perspective of disaster governance. Taking a tactical emphasis, many studies are based on a single case that focuses on how crowdsourcing can mitigate local problems caused by a specific disaster [6][7][8]. This paper analyses 1...

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“…For example, Wu [7] verified the spatio-temporal correlation between the number of disaster-related tweets, public emotion, and disaster loss. Faxi [12] et al compared disaster-related tweets and loss-related tweets to demonstrate that it was possible for microblog data to rapidly determine county-level units suffering from serious disasters. To evaluate typhoon disaster losses, Qing [13] et al applied Sina Weibo data to establish a disaster loss assessment index model using term frequency-inverse document frequency (TF-IDF), a classical term frequency weight method.…”

Section: Microblog Data For Disaster Damage Assessmentmentioning

confidence: 99%

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

Xing

Liu

et al. 2019

IJGI

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Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.

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Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study

Cited by 100 publications

References 45 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Promoting Disaster Resilience: Operation Mechanisms and Self-Organizing Processes of Crowdsourcing

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

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