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Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime

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  • Francesco Finazzi

Abstract

When an earthquake affects an inhabited area, a need for information immediately arises among the population. In general, this need is not immediately fulfilled by official channels which usually release expert‐validated information with delays of many minutes. Seismology is among the research fields where citizen science projects succeeded in collecting useful scientific information. More recently, the ubiquity of smartphones is giving the opportunity to involve even more citizens. This paper focuses on seismic intensity reports collected through smartphone applications while an earthquake is occurring. The aim is to provide a framework for predicting and updating in near realtime earthquake parameters that are useful for assessing the effect of the earthquake. This is done by using a multivariate space–time model based on time‐varying coefficients and a spatial latent variable. As a case‐study, the model is applied to more than 200000 seismic reports globally collected over a period of around 4 years by the Earthquake Network citizen science project. It is shown how the time‐varying coefficients are needed to adapt the model to an information content that changes with time, and how the spatial latent variable can capture the local seismicity and the heterogeneity in the people's response across the globe.

Suggested Citation

  • Francesco Finazzi, 2020. "Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 857-882, June.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:3:p:857-882
    DOI: 10.1111/rssa.12577
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    1. Paola Sbarra & Patrizia Tosi & Valerio De Rubeis, 2010. "Web-based macroseismic survey in Italy: method validation and results," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(2), pages 563-581, August.
    2. Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
    3. Francesco Finazzi & E. Marian Scott & Alessandro Fassò, 2013. "A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 287-308, March.
    4. Gehl, Pierre & Cavalieri, Francesco & Franchin, Paolo, 2018. "Approximate Bayesian network formulation for the rapid loss assessment of real-world infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 80-93.
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    Cited by:

    1. Shasha Li & Xinyu Peng & Ruiqiu Pang & Li Li & Zixuan Song & Hongying Ye, 2021. "Information Preference and Information Supply Efficiency Evaluation before, during, and after an Earthquake: Evidence from Songyuan, China," IJERPH, MDPI, vol. 18(24), pages 1-28, December.
    2. Mario J. Valladares-Garrido & Luis E. Zapata-Castro & Christopher G. Valdiviezo-Morales & Abigaíl García-Vicente & Darwin A. León-Figueroa & Raúl Calle-Preciado & Virgilio E. Failoc-Rojas & César Joha, 2022. "Factors Associated with Knowledge of Evacuation Routes and Having an Emergency Backpack in Individuals Affected by a Major Earthquake in Piura, Peru," IJERPH, MDPI, vol. 19(22), pages 1-15, November.

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