Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China
Abstract
:1. Introduction
2. Methods and Data
2.1. Study Area and 2017 Jiuzhaigou Earthquake
2.2. Methods
2.2.1. Analysis of Mobile Signaling Changes
2.2.2. Analysis of the Relationship between Mobile Signaling Data Changes and Seismic Intensities
2.2.3. Kriging Interpolation Methods
2.3. Data
2.3.1. Data Acquisition
2.3.2. Mobile Signaling
2.3.3. Data Overview
3. Results
3.1. Analysis of the Overall Change in Regional Mobile Signaling
3.1.1. Daily Variation in the Number of Devices on the Day before the Earthquake
3.1.2. Signaling Changes on the Day of the Earthquake
3.1.3. Signaling Changes between the Day of and the Day before the Earthquake
3.1.4. Analysis of the Decommission Rate for Mobile Signaling
3.2. Change in Mobile Signaling in Different Intensity Areas
3.2.1. Signaling Changes in Various Intensity Areas
3.2.2. Analysis of the Minute-by-Minute Change in the Number of Devices in Different Intensity Areas
3.2.3. Thirty-Minute Moving Average Change in the Decommission Rate for Different Intensity Areas
3.3. The Relationship between the Change in Mobile Signaling and Intensity
3.3.1. Correlation Analysis
3.3.2. Regression Analysis
3.4. Spatial Interpolation Analysis of Mobile Signaling
4. Discussion
4.1. Response of Mobile Signaling to Earthquake Effects
4.2. Mobile Signaling Changes in Response to the Degree of Disruption
4.3. Determination of the Seismic Influence Field by Mobile Phone Signaling
4.4. Delineating the Seismic Influence Field by Mobile Signaling
4.5. Limitations and Challenges
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ma, Y.H.; Xie, L.L. A discussion on the factors of human casualties in earthquakes. J. Nat. Disasters 2000, 84–90. [Google Scholar] [CrossRef]
- Wei, B.Y.; Nie, G.Z.; Su, G.W.; Qi, W.H. Advances on the assessment methods of buried personnel distribution in earthquake disaster. J. Catastrophol. 2017, 32, 155–159. [Google Scholar]
- Nie, G.Z.; An, J.W.; Deng, Y. Advances in earthquake emergency disaster services. Seismol. Geol. 2012, 34, 782–791. [Google Scholar]
- Zhang, H.Y.; Li, H.Y. Overview of the January 17, 1994 Northridge, California, USA earthquake. Prog. Earthq. Sci. 1994, 5, 8–14. [Google Scholar]
- Wald, D.J.; Quitoriano, V.; Heaton, T.H.; Kanamori, H. Relationships Between Peak Ground Acceleration, Peak Ground Velocity, and Modified Mercalli Intensity in California. Earthq. Spectra 1999, 15, 557–564. [Google Scholar] [CrossRef]
- Li, S.Y.; Jin, X.; Chen, X. A study of earthquake intensity and seismic intensity velocities. Earthq. Eng. Eng. Vibr. 2002, 6, 1–7. [Google Scholar]
- Kaka, S.I.; Atkinson, G.M. Empirical ground-motion relations for ShakeMap applications in southeastern Canada and the north-eastern United States. Seism. Res. Lett. 2005, 76, 274–282. [Google Scholar] [CrossRef]
- Wald, D.; Lin, K.W.; Porter, K.L. Turner, ShakeCast: Automating and Improving the Use of ShakeMap for Post-Earthquake Decision-Making and Response. Earthq. Spectra 2008, 24, 533–553. [Google Scholar] [CrossRef]
- Bragato, P.L. Assessing regional and site-dependent variabilityof ground motions for ShakeMap implementation in Ital. Bull. Seism. Soc. Am. 2009, 99, 2950–2960. [Google Scholar] [CrossRef]
- Chen, K.; Yu, Y.X.; Gao, M.T. ShakeMap system considering site effects. Earthq. Res. China 2010, 26, 92–102. [Google Scholar]
- Jin, X.; Zhang, H.C.; Li, J. Preliminary study on seismic instrument intensity criteria. Prog. Geophys. 2013, 28, 2336–2351. [Google Scholar]
- Wang, X.Q.; Dou, A.X.; Wang, L. Remote sensing assessment of the 2013 Sichuan Lushan 7.0 magnitude earthquake intensity. Chin. J. Geophys. 2015, 58, 163–171. [Google Scholar]
- Yates, D.; Paquette, S. Emergency knowledge management and social media technologies: A case study of the 2010 haitian earthquake. Int. J. Inf. Manag. 2011, 31, 6–13. [Google Scholar] [CrossRef]
- Al-Saggaf, Y.; Simmons, P. Social media in Saudi Arabia: Exploring its use during two natural disasters, Technol. Forecast. Soc. Chang. 2015, 95, 3–15. [Google Scholar] [CrossRef]
- Tim, Y.; Pan, S.L.; Ractham, P.; Kaewkitipong, L. Digitally enabled disaster response: The emergence of social media as boundary objects in a flooding disaster. Inform. Syst. 2017, 27, 197–232. [Google Scholar] [CrossRef]
- Kim, J.; Bae, J.; Hastak, M. Emergency information diffusion on online social media during storm Cindy in U.S. Int. J. Inf. Manag. 2018, 40, 153–165. [Google Scholar] [CrossRef]
- Zhang, C.; Fan, C.; Yao, W.L.; Hu, X.; Mostafavi, A. Social media for intelligent public information and waning in disasters: An interdisciplinary review. Int. J. Inf. Manag. 2019, 49, 190–207. [Google Scholar] [CrossRef]
- Kavota, J.K.; Jean Robert, K.K.; Samuel, F.W. Social media and disaster management: Case of the north and south Kivu regions in the Democratic Republic of the Congo. Int. J. Inf. Manag. 2020, 52, 102068. [Google Scholar] [CrossRef]
- Shuai, X.H.; Hu, S.P.; Zheng, X.X. Rapid estimation of the felt range of Wenchuan earthquake based on Internet information. Seismol. Geol. 2014, 36, 1094–1105. [Google Scholar]
- Arapostathis, S.; Lekkas, E.; Kalabokidis, K.; Xanthopoulos, G.; Drakatos, G.; Spirou, N.; Kalogeras, I. Developing seismic intensity maps from twitter data; The case study of Lesvos Greece 2017 earthquake: Assessments, improvements and enrichments on the methodology. In Proceedings of the ISPRS—The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, 18–21 March 2018; Volume XLII-3/W4, pp. 59–66. [Google Scholar] [CrossRef]
- Xing, Z.Y.; Su, X.; Liu, J.; Su, W.; Zhang, X. Spatiotemporal change analysis of earthquake emergency information based on Mi-croblog Data: A case study of the “8.8” Jiuzhaigou earthquake. ISPRS Int. J. Geo-Inf. 2019, 8, 359. [Google Scholar] [CrossRef]
- Kühn, P.J. Location-Based Services in Mobile Communication Infrastructures. AEU—AEU Int. J. Electron. Commun. 2004, 58, 159–164. [Google Scholar] [CrossRef]
- Shi, W.H. Location-Based Services in Mobile Communication Infrastructures. Master’s Thesis, Shanghai Normal University, Shanghai, China, 2006. [Google Scholar]
- Bengtsson, L.; Lu, X.; Thorson, A.; Garfield, R.; von Schreeb, J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti. PLoS Med. 2011, 8, e1001083. [Google Scholar] [CrossRef] [PubMed]
- Huang, Z.; Ling, X.; Wang, P.; Zhang, F.; Mao, Y.; Lin, T.; Wang, F.Y. Modeling real-time human, mobility based on mobile phone and transportation data fusion. Transport. Res. C Emerg. Technol. 2018, 96, 251–269. [Google Scholar] [CrossRef]
- Balistrocchi, M.; Metulini, R.; Carpita, M.; Ranzi, R. Dynamic maps of human exposure to floods based on mobile phone data. Nat. Hazards Earth Syst. Sci. 2020, 20, 3485–3500. [Google Scholar] [CrossRef]
- Dai, K.X.; Cheng, C.X.; Shen, S.; Su, K.; Zheng, X.M.; Zhang, T. Postearthquake situational awareness based on mobile phone signaling data: An example from the 2017 Jiuzhaigou earthquake. Int. J. Disaster Risk Reduct. 2022, 69, 102736. [Google Scholar] [CrossRef]
- Pei, T.; Sobolevsky, S.; Ratti, C.; Shaw, S.-L.; Li, T.; Zhou, C. A new insight into land use classification based on aggregated mobile phone data. Int. J. Geogr. Inf. Sci. 2014, 28, 1988–2007. [Google Scholar] [CrossRef]
- Secchi, P.; Vantini, S.; Vitelli, V. Analysis of spatio-temporal mobile phone data: A case study in the metropolitan area of Milan. Stat. Methods Appl. 2015, 24, 279–300. [Google Scholar] [CrossRef]
- Wesolowski, A.; Metcalf, C.J.; Eagle, N.; Kombich, J.; Grenfell, B.; Bjornstad, O.; Lessler, J.; Tatem, A.; Buckee, C. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Proc. Natl. Acad. Sci. USA 2015, 112, 11114–11119. [Google Scholar] [CrossRef]
- Claudio, G.; Armando, P.; Andrea, B. A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic. Atmos. Environ. 2016, 131, 289–300. [Google Scholar] [CrossRef]
- Fang, J.; Wang, D.; Xie, D.C.; Wang, C.; Wang, H.Y.; Wu, M.W.; Ye, X.Y.; Duan, Z.Y. Research on dynamic change and early warning of large tourist flow based on mobile signal data analysis: A case study of Gucun park sakura festival in Shanghai. City Plan. Rev. 2016, 40, 43–51. [Google Scholar]
- Tu, W.; Cao, J.; Yue, Y.; Shaw, S.-L.; Zhou, M.; Wang, Z.; Chang, X.; Xu, Y.; Li, Q. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. Int. J. Geogr. Inf. Sci. 2017, 31, 2331–2358. [Google Scholar] [CrossRef]
- Fan, Z.; Pei, T.; Ma, T.; Du, Y.; Song, C.; Liu, Z.; Zhou, C. Estimation of urban crowd flux based on mobile phone location data: A case study of Beijing, China. Comput. Environ. Urban Syst. 2018, 69, 114–123. [Google Scholar] [CrossRef]
- Lu, X.; Bengtsson, L.; Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. Proc. Natl. Acad. Sci. USA 2012, 109, 11576–11581. [Google Scholar] [CrossRef]
- Wilson, R.; Zu Erbach-Schoenberg, E.; Albert, M.; Power, D.; Tudge, S.; Gonzalez, M.; Guthrie, S.; Chamberlain, H.; Brooks, C.; Hughes, C.; et al. Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLoS Curr 2016, 8. [Google Scholar] [CrossRef]
- Moumni, B.; Frias-Martinez, V.; Frias-Martinez, E. Characterizing social response tourban earthquakes using cell-phone network data: The 2012 oaxaca earthquake. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp ’13 Adjunct, Zurich, Switzerland, 8–12 September 2013; Association for Computing Machinery: New York, NY, USA, 2013; pp. 1199–1208. [Google Scholar] [CrossRef]
- Li, D.P.; Huang, L.H.; Chen, P. Crowd flow analysis based on cell phone location data for the 7.0 magnitude earthquake in Jiuzhaigou, Sichuan. Earthq. Res. China 2017, 33, 602–612. [Google Scholar]
- Pang, X.K.; Nie, G.Z.; Zhang, X. Selection of earthquake disaster indicators based on cell phone location data. Earthq. Res. China 2019, 35, 144–157. [Google Scholar]
- Xia, C.X.; Nie, G.Z.; Pang, X.K.; Fan, X.W.; Zhou, J.X.; Li, H.Y. Research on the Application of Phone Location Data in the Rapid Delimitation of the Meizoseismal Area. Bull. Seismol. Soc. Am. 2019, 109, 2470–2490. [Google Scholar] [CrossRef]
- Xing, Z.Y.; Zhang, X.D.; Zan, X.L.; Xiao, C.; Li, B.; Han, K.K.; Liu, Z.; Liu, J.M. Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake. Int. J. Disaster Risk Reduct. 2021, 58, 102200. [Google Scholar] [CrossRef]
- Wei, B.Y.; Nie, G.Z.; Su, G.W.; Sun, L.; Bai, X.F.; Qi, W.H. Risk Assessment of People Trapped in Earthquake Based on Km Grid: A Case Study of the 2014 Ludian Earthquake, China, Geomatics. Geomat. Nat. Hazards Risk 2017, 8, 1289–1305. [Google Scholar] [CrossRef] [Green Version]
- Jiuzhaigou County People’s Government. Jiuzhaigou County Overview [EB/OL]. Available online: http://www.jzg.gov.cn/jzgrmzf/c100125/201904/5a4014ab619f4216bd98cfad0f988180.shtml (accessed on 15 March 2022).
- Oliver, M.A.; Webster, R. Kriging: A method of interpolation for geographical information systems. Int. J. Geogr. Inf. Sci. 1990, 4, 313–332. [Google Scholar] [CrossRef]
- National Natural Resources and Geospatial Basic Information Database [EB/OL]. Available online: http://sgic.net.cn/web/geo/zhyjygtb/info/2017/682.html (accessed on 5 July 2022).
Indicators | Gid | Station | WiFi | Loginmac |
---|---|---|---|---|
Day before the earthquake | 93,482 | 65,056 | 597,533 | 106,275 |
Day of the earthquake | 67,292 | 48,405 | 321,410 | 46,299 |
Decommission rate | 28% | 25% | 56% | 46% |
Indicators | IX | VIII | VII | VI |
---|---|---|---|---|
Gid | 4 | 13 | 36 | 59 |
Station | 4 | 13 | 31 | 33 |
WiFi | 4 | 10 | 35 | 53 |
Loginmac | 4 | 10 | 26 | 51 |
Signaling | Abrupt Change Point | Change Trends | Duration of Sudden Change |
---|---|---|---|
Gid | 21:20 | sudden increase | 4 min |
21:21 | sudden reduction | ||
Station | 21:20 | sudden increase | 4 min |
21:21 | sudden reduction | ||
Loginmac | 21:20 | sudden reduction | 5 min |
WiFi | 21:21 | sudden reduction | 3 min |
Signaling Data | IX-Intensity Zone | VIII-Intensity Zone | VII-Intensity Zone | VI-Intensity Zone | |
---|---|---|---|---|---|
Gid | Day before the earthquake | 19,242 | 22,346 | 7688 | 44,206 |
Day of the earthquake | 9389 | 13,177 | 5504 | 39,222 | |
Decommission rate | 51.20% | 41.03% | 28.40% | 11.27% | |
Station | Day before the earthquake | 11,923 | 14,290 | 5509 | 33,334 |
Day of the earthquake | 6128 | 8799 | 4301 | 29,177 | |
Decommission rate | 48.60% | 38.42% | 21.92% | 12.47% | |
Loginmac | Day before the earthquake | 16,204 | 17,564 | 7953 | 64,554 |
Day of the earthquake | 2445 | 4151 | 1704 | 37,999 | |
Decommission rate | 84.91% | 76.36% | 78.57% | 41.13% | |
WiFi | Day before the earthquake | 139,896 | 198,243 | 23,431 | 235,963 |
Day of the earthquake | 44,032 | 77,016 | 9335 | 191,027 | |
Decommission rate | 68.52% | 61.15% | 60.15% | 19.04% |
Variables | Dgi | Rgi |
---|---|---|
Ig1 | −0.900 ** | 0.244 * |
Ig2 | −0.886 ** | 0.278 * |
Ig3 | −0.894 ** | 0.474 ** |
Ig4 | −0.880 ** | 0.385 ** |
Models | Adjusted R2 | Sig. | Accuracy |
---|---|---|---|
(1) I1 = 8.662 − 0.062 × D1 + 0.432 × R1 | 0.822 | 0.000 | 84% |
(2) I2 = 8.306 − 0.054 × D2 + 0.45 × R2 | 0.786 | 0.000 | 82% |
(3) I3 = 8.229 − 0.052 × D3 + 0.623 × R3 | 0.771 | 0.000 | 86% |
(4) I4 = 8.233 – 0.050 × D4 + 0.357 × R4 | 0.749 | 0.000 | 82% |
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Guo, X.; Wei, B.; Nie, G.; Su, G. Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China. Int. J. Environ. Res. Public Health 2022, 19, 10697. https://doi.org/10.3390/ijerph191710697
Guo X, Wei B, Nie G, Su G. Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China. International Journal of Environmental Research and Public Health. 2022; 19(17):10697. https://doi.org/10.3390/ijerph191710697
Chicago/Turabian StyleGuo, Xinxin, Benyong Wei, Gaozhong Nie, and Guiwu Su. 2022. "Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China" International Journal of Environmental Research and Public Health 19, no. 17: 10697. https://doi.org/10.3390/ijerph191710697