Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Wang, Yandong; Ruan, Shisi; Wang, Teng; Qiao, Mengling

doi:10.1080/17538947.2018.1497100

Cited by 33 publications

(14 citation statements)

References 38 publications

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“…Wang et al [22] introduced a method for detecting the actual effect region rapidly through social media after a major earthquake. They suggested a theoretical model recognized as the spatial logistic growth model (SLGM), relying on logistics to define the linear development procedures of citizens' information after an earthquake.…”

Section: Literature Reviewmentioning

confidence: 99%

“…ShakeMap uses the modified Mercalli intensity (MMI) scale to measure the impact of earthquakes to provide a representation of the shaking severity after severe earthquakes. The MMI scale consists of 12 intensity levels on a scale from I (not felt) to XII (complete collapse) and is related to measurable damage from the earthquake [22].…”

Section: Validationmentioning

confidence: 99%

“…The worldwide spread of the internet and citizen crowdsourcing has enabled social networks to become a major hub for low-cost earthquake detection. Fast and effective assessment of the affected region in which an earthquake causes injury and property losses is crucial for urgent response procedures and assessment of damages [22]. Rapid assessment of damage data can assist in asset allocation and guide decision-makers in an emergency.…”

Section: Introductionmentioning

confidence: 99%

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Earthquake Damage Assessment Based on User Generated Data in Social Networks

Ahadzadeh

Malek

2021

Sustainability

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Natural disasters have always been one of the threats to human societies. As a result of such crises, many people will be affected, injured, and many financial losses will incur. Large earthquakes often occur suddenly; consequently, crisis management is difficult. Quick identification of affected areas after critical events can help relief workers to provide emergency services more quickly. This paper uses social media text messages to create a damage map. A support vector machine (SVM) machine-learning method was used to identify mentions of damage among social media text messages. The damage map was created based on damage-related tweets. The results showed the SVM classifier accurately identified damage-related messages where the F-score attained 58%, precision attained 56.8%, recall attained 59.25%, and accuracy attained 71.03%. In addition, the temporal pattern of damage and non-damage tweets was investigated on each day and per hour. The results of the temporal analysis showed that most damage-related messages were sent on the day of the earthquake. The results of our research were evaluated by comparing the created damage map with official intensity maps. The findings showed that the damage of the earthquake can be estimated efficiently by our strategy at multispatial units with an overall accuracy of 69.89 at spatial grid unit and Spearman’s rho and Pearson correlation of 0.429 and 0.503, respectively, at the spatial county unit. We used two spatial units in this research to examine the impact of the spatial unit on the accuracy of damage assessment. The damage map created in this research can determine the priority of the relief workers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Earthquake Damage Assessment Based on User Generated Data in Social Networks

Ahadzadeh

Malek

2021

Sustainability

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“…In 2018, Sina Weibo's monthly active users reached 462 million (2018 Sina Weibo User Development Report). Weibo data have been used in many areas, including public health [25], environmental issues [26], natural disasters [27], and urban land use [28]. Based on the timestamp and location record of each Weibo user's posts, the user's movement trajectory can be constructed.…”

Section: Chinese Spring Festival Travelmentioning

confidence: 99%

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

et al. 2020

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Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.

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“…Spatial information in social media data can be used in disaster mapping. Therefore, help disaster responders gain situational awareness in crisis identification and loss assessment [18]- [20]. The real-time characteristics of social media streams can help detect the outbreak of disasters [21], [22].…”

Section: Related Studies a Using Social Media For Situational Awmentioning

confidence: 99%

Community Evolutional Network for Situation Awareness Using Social Media

Wang

et al. 2020

IEEE Access

Self Cite

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Social media is important for situational awareness during a disaster. During a disaster, the situation of emergence often changes over time and hence the topics of social media messages generated by social media users also change accordingly. Few studies quantitatively describe the topic evolution of social media during a disaster and the corresponding relationship between topic evolution and disaster process. We address this problem using co-word network analysis and present a new method based on the community evolution of the co-word network to analyze topic evolution over time in social media. The method uses communities of the co-word network in social media to represent topics. Based on the theory of community evolution, a community evolutional network is proposed to support and quantify the evolution of the topics. We implemented the proposed method in a case study, ''July 2012 Beijing flood'' using the Sina Weibo dataset. Results show that our method can well quantify the evolution process of topics and validate the effectiveness of our method in real-world applications. The method can facilitate the understanding of public expression dynamics during a disaster and be used to reveal the process and stages of a disaster.

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Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Cited by 33 publications

References 38 publications

Earthquake Damage Assessment Based on User Generated Data in Social Networks

Earthquake Damage Assessment Based on User Generated Data in Social Networks

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

Community Evolutional Network for Situation Awareness Using Social Media

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