Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China
AbstractRapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigat...
| Main Authors: | , , , , |
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| פורמט: | Article |
| שפה: | English |
| יצא לאור: |
Taylor & Francis Group
2023-12-01
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| סדרה: | Geomatics, Natural Hazards & Risk |
| נושאים: | |
| גישה מקוונת: | https://www.tandfonline.com/doi/10.1080/19475705.2023.2221772 |
| _version_ | 1827582533535006720 |
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| author | Xinxin Guo Benyong Wei Guiwu Su Wenhua Qi Tengfei Zhang |
| author_facet | Xinxin Guo Benyong Wei Guiwu Su Wenhua Qi Tengfei Zhang |
| author_sort | Xinxin Guo |
| collection | DOAJ |
| description | AbstractRapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigated the 2022 Mw 6.6 Luding earthquake in Sichuan, China; the relationship between mobile signaling data and the seismically most-affected area was explored using comparative, regression, and spatial interpolation analysis. The results revealed that mobile signaling data changed abnormally following the earthquake and differed across high- and low-intensity zones. There was a significant correlation between mobile signaling data and intensity. Additionally, the most-affected areas estimated using mobile signaling data were comparable to the actual seismic damage. Therefore, mobile signaling data can provide a reference for determining the most-affected areas and assist in emergency relief efforts after an earthquake. |
| first_indexed | 2024-03-08T22:53:07Z |
| format | Article |
| id | doaj.art-fc77f066bc2442f389dedcbb28e6e2c1 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-03-08T22:53:07Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-fc77f066bc2442f389dedcbb28e6e2c12023-12-16T08:49:46ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132023-12-0114110.1080/19475705.2023.2221772Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, ChinaXinxin Guo0Benyong Wei1Guiwu Su2Wenhua Qi3Tengfei Zhang4China Earthquake Administration, Institute of Geology, Beijing, ChinaChina Earthquake Administration, Institute of Geology, Beijing, ChinaChina Earthquake Administration, Institute of Geology, Beijing, ChinaChina Earthquake Administration, Institute of Geology, Beijing, ChinaChina Earthquake Administration, Institute of Geology, Beijing, ChinaAbstractRapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigated the 2022 Mw 6.6 Luding earthquake in Sichuan, China; the relationship between mobile signaling data and the seismically most-affected area was explored using comparative, regression, and spatial interpolation analysis. The results revealed that mobile signaling data changed abnormally following the earthquake and differed across high- and low-intensity zones. There was a significant correlation between mobile signaling data and intensity. Additionally, the most-affected areas estimated using mobile signaling data were comparable to the actual seismic damage. Therefore, mobile signaling data can provide a reference for determining the most-affected areas and assist in emergency relief efforts after an earthquake.https://www.tandfonline.com/doi/10.1080/19475705.2023.2221772Mobile signalingmost-affected arearapid assessmentearthquakeLuding |
| spellingShingle | Xinxin Guo Benyong Wei Guiwu Su Wenhua Qi Tengfei Zhang Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China Geomatics, Natural Hazards & Risk Mobile signaling most-affected area rapid assessment earthquake Luding |
| title | Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China |
| title_full | Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China |
| title_fullStr | Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China |
| title_full_unstemmed | Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China |
| title_short | Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China |
| title_sort | determination of most affected areas by earthquakes based on mobile signaling data a case study of the 2022 mw 6 6 luding earthquake china |
| topic | Mobile signaling most-affected area rapid assessment earthquake Luding |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2023.2221772 |
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