Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors

Kropivnitskaya, Yelena; Tiampo, K. F.; Qin, Jun; Bauer, Michael

doi:10.1007/s00024-016-1417-6

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…[25] SAR [10], [39] InSAR [40], [41], [42], [43 [52], [53], [54] Closed-circuit television videos (CCTV) [55], [56] Missions deployed, or missions that have had their results published during the period covered by this literature review are Albania [46,57], Puerto Rico [44,45], Mayotte [51], Mexico [27][28][29], Palu -North Sulawesi (Indonesia) [52,55,58], Hualien (China) [50],…”

Section: Lidarmentioning

confidence: 99%

“…Central Italy [31,32,34,59], Muisne (Ecuador) [34], Nepal [15,39,48], Napa -California (USA) [53,54], Haiti [10] and L'Aquila (Italy) [12,36]. The location of major earthquakes epicentres from 2014 -2019 included as case studies in this literature review is depicted in Figure 3.…”

Section: Lidarmentioning

confidence: 99%

“…Incoming entries from multiple web servers are processed and aggregated over postal ZIP codes (in the US), and 1-km and 10 km aggregated boxes for each earthquake to make interactive maps and plots served via the USGS Earthquake Program web pages [44]. The DYFI is a program for community internet intensity maps (CIIM) developed based on the questionnaires filled in that assigns average intensity level to each ZIP code [54]. Two relevant questionnaire indices are combined with other users within their community to produce an intensity calculation [44].…”

Section: Did You Feel It (Dyfi)mentioning

confidence: 99%

“…Published papers, maps about the seismotectonic context in Indonesia, teleseismic data, local seismic waveforms, high-resolution optical satellite images, SAR, tide gauge records, and field observations from both science groups and local residents were shared on Twiter, getting rapid and varied feedback from fellow researchers [52]. The correlation between activity on Twitter and Mercalli intensity was demonstrated in Napa, California [54], Japan, and Chile [53]. The activity on Twitter related to recent earthquakes is plotted in Figure 12.…”

Section: Twittermentioning

confidence: 99%

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Earthquakes Reconnaissance Data Sources, a Literature Review

Mojica¹,

Wilkinson²,

James³

2021

Preprint

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Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

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Section: Lidarmentioning

confidence: 99%

Section: Lidarmentioning

confidence: 99%

Section: Did You Feel It (Dyfi)mentioning

confidence: 99%

Section: Twittermentioning

confidence: 99%

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Earthquakes Reconnaissance Data Sources, a Literature Review

Mojica¹,

Wilkinson²,

James³

2021

Preprint

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“…Previous studies have shown the quantitative relationships between location-aware big data and natural disasters themselves and the damages they caused. For example, disaster-related social media data are significantly correlated with the actual rainfall of a rainstorm, the shaking intensity of an earthquake, and the atmospheric pressure of a hurricane [20][21][22][23][24]. Strong positive relationships have also been found between the proximity to the hurricane's path and hurricane-related twitter activity, and between per-capita twitter activity and per-capita economic damage inflicted by the hurricane [18,25].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

Liu

et al. 2019

Remote Sensing

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The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.

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Concept of “People as Sensors”

Shibuya

2019

Social Media Communication Data for Recovery

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Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors

Cited by 21 publications

References 12 publications

Earthquakes Reconnaissance Data Sources, a Literature Review

Earthquakes Reconnaissance Data Sources, a Literature Review

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

Concept of “People as Sensors”

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