Using Non-authoritative Sources During Emergencies in Urban Areas

Schnebele, E.; Oxendine, Christopher; Cervone, Guido; Ferreira, Celso M.; Waters, Nigel

doi:10.1007/978-3-319-11469-9_14

Cited by 16 publications

(6 citation statements)

References 28 publications

(18 reference statements)

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“…Therefore, the social and spatiotemporal inequality in the usage of social media data should be fully considered before such data can be leveraged to predict damage, investigate impacted populations and prioritize activities during the course of disaster management. Instead of using social media as a standalone information source, previous studies [25,30] suggested that authoritative data (e.g., remote sensing data) should be combined to enhance the identification of relevant messages from social media.…”

Section: Discussionmentioning

confidence: 99%

“…However, despite the quantity of big data available, gaps are often present due to the specific limitations of the instruments or their carrier platforms. Several attempts [23][24][25], therefore, have made to illustrate how volunteered geographical data (VGI) can be used to augment traditional remote sensing data and methods to estimate flood extent and identify affected roads during a flood disaster. In those work, a variety of non-authoritative, multi-sourced data, such as tweets, geolocated photos from Google search engine, traffic data from cameras, OpenStreetMap, videos from Youtube, and news, are collected to assess the damage of transportation infrastructure and to construct an estimate of the advancement and recession of the flood event.…”

Section: Damage Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Huang

Xiao

2015

IJGI

223

119

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Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery. OPEN ACCESS

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Section: Discussionmentioning

confidence: 99%

Section: Damage Assessmentmentioning

confidence: 99%

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Huang

Xiao

2015

IJGI

223

119

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“…Meanwhile, tweets posted by volunteers are also considered as highly reliable information sources. Schnebele and Cervone (2013) and their series of studies (Schnebele et al 2014;Schnebele et al 2015) developed a method of crisis mapping by combining remote sensing with local information sent by volunteers. They concluded that volunteered information can be a trustworthy source.…”

Section: Discussionmentioning

confidence: 99%

Detecting flood inundation information through Twitter: The 2015 Kinu River flood disaster in Japan

Shi

Sayama

Takara

et al. 2019

Journal of Natural Disaster Science

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For effective responses to flood disasters, it is essential to identify affected areas in real time. Recently, social media (e.g. Twitter) have emerged as new sources of disaster-related information in real time. However, concerns still remain regarding the trustworthiness and the amount of information, especially that issued from a site of crisis. This study investigated a total of 109 tweets sampled based on certain criteria during a flood disaster in the Kinu River Basin, Japan in September 2015. We classified them into five categories depending on the main contents: 1) flood inundation, 2) rescue, 3) emotion, 4) river condition, and 5) damage situation. The analysis suggests that the highest proportion (37%) of tweets were related to flood inundation followed by damage (19%) and 32% of them were posted in near real time with photos. We further compared well-positioned tweets with other inundation extent information based on our field investigations and aerial photos. The results showed good agreement between the inundation information from the posted tweets and the expected locations. Some tweets suggested additional inundated areas, not originally identified by the aerial photos. Overall, the study shows the potential use of social media to collect local details about floods.

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“…It was proven that nonauthoritative data could be used to fill the gaps in remote sensing data. A similar study for the assessment of transportation infrastructure was also presented in [80]. The study in [81] first analyzed the spatial relationship between the brightness change of the satellite nightlight images and the density of power-related tweets.…”

Section: ) Disaster Monitoringmentioning

confidence: 99%

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges,

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Using Non-authoritative Sources During Emergencies in Urban Areas

Cited by 16 publications

References 28 publications

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Detecting flood inundation information through Twitter: The 2015 Kinu River flood disaster in Japan

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

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