Abstract
As a global concern, disaster resilience is considering as the priority sector of almost all countries in the world. This study intends to explore the potential of big data for disaster management through increasing resilience against socio-ecological vulnerability. A qualitative approach focusing desk review and secondary data have been used to substantiate the arguments. This study argues that disaster resilience is an integrated function of the adaptive, absorptive and transformative capacity of an individual or society to withstand and cope with the adverse effects of the disaster. Big data technologies create an opportunity to supply huge information to enhance these capacities so that a social system can face natural disasters properly. This study also emphasizes the major principles of big data for effective use for disaster management like open source tools, strong infrastructure, developing local skills, context-specific data sources, data sharing with ethics, awareness about the right of data and learning from experience. This study also argues that big data is a potential tool for policymakers, administrators, and related stakeholders to take necessary actions during and after disasters like an early warning system, weather forecasting, emergency evacuation, immediate responses, relief distribution, training needs assessment and increasing trained individuals.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adger, W.N., Hughes, T.P., et al.: Social-ecological resilience to coastal disasters. Science 309(5737), 1036–1039 (2005)
Agrawal, N.: Natural Disasters and Risk Management in Canada. Springer (2018)
Alam, G.M., Alam, K., et al.: Hazards, food insecurity and human displacement in rural riverine Bangladesh: implications for policy. Int. J. Disaster Risk Reduct. 43, 101364 (2019)
Carley, K.M., Malik, M., et al.: Crowd sourcing disaster management: the complex nature of Twitter usage in padang Indonesia. Saf. Sci. 90, 48–61 (2016)
Clark, N., Guiffault, F.: Seeing through the clouds: processes and challenges for sharing geospatial data for disaster management in Haiti. Int. J. Disaster Risk Reduct. 28, 258–270 (2018)
Contreras, D., Forino, G., Blaschke, T.: Measuring the progress of a recovery process after an earthquake: the case of l’aquila, Italy. Int. J. Disaster Risk Reduct. 28, 450–464 (2018)
Di Felice, M., Trotta, A., et al.: Self-organizing aerial mesh networks for emergency communication. In: 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), pp. 1631–1636. IEEE (2014)
Enenkel, M., Saenz, S.M., et al.: Social media data analysis and feedback for advanced disaster risk management arXiv preprint arXiv:180202631 (2018)
Folke, C.: Resilience: the emergence of a perspective for social-ecological systems analyses. Glob. Environ. Change 16(3), 253–267 (2006)
Goldenberg, S.B., Gopalakrishnan, S.G., et al.: The 2012 triply nested, high-resolution operational version of the hurricane weather research and forecasting model (HWRF): track and intensity forecast verifications. Weather Forecast. 30(3), 710–729 (2015)
Horita, F.E., de Albuquerque, J.P., et al.: Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in Brazil. Decis. Support Syst. 97, 12–22 (2017)
Kumar, S.A., Bao, S., et al.: Flooding disaster resilience information framework for smart and connected communities. J. Reliable Intell. Environ. 5(1), 3–15 (2019)
Lu, Z., Cao, G., La Porta, T.: Teamphone: networking smartphones for disaster recovery. IEEE Trans. Mob. Comput. 16(12), 3554–3567 (2017)
Lv, Z., Li, X., Choo, K.K.R.: E-government multimedia big data platform for disaster management. Multimedia Tools Appl. 77(8), 10077–10089 (2018)
Mali, V., Rao, M., Mantha, S.: AHP driven GIS based emergency routing in disaster management. In: International Conference on Advances in Computing, Communication and Control, pp. 237–248. Springer (2013)
Masood, T., So, E., McFarlane, D.: Disaster management operations-big data analytics to resilient supply networks. In: Proceedings of the 24th EurOMA Conference (2017)
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151(4), 264–269 (2009)
Musaev, A., Wang, D., Pu, C.: LITMUS: a multi-service composition system for landslide detection. IEEE Trans. Serv. Comput. 8(5), 715–726 (2014)
Ogie, R.I., Clarke, R.J., et al.: Crowdsourced social media data for disaster management: lessons from the Petajakarta. org project. Comput. Environ. Urban Syst. 73, 108–117 (2019)
Ragini, J.R., Anand, P.R., Bhaskar, V.: Big data analytics for disaster response and recovery through sentiment analysis. Int. J. Inf. Manag. 42, 13–24 (2018)
Sarker, M.N.I., Wu, M., et al.: Livelihood vulnerability of riverine-Island dwellers in the face of natural disasters in Bangladesh. Sustainability 11(6), 1623 (2019)
Tomaszewski, B., Judex, M., et al.: Geographic information systems for disaster response: a review. J. Homel. Secur. Emerg. Manag. 12(3), 571–602 (2015)
Vandenbroucke, J.P., Von Elm, E., et al.: Strengthening the reporting of observational studies in epidemiology (strobe): explanation and elaboration. Ann. Intern. Med. 147(8), W–163 (2007)
Yang, C., Su, G., Chen, J.: Using big data to enhance crisis response and disaster resilience for a smart city. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp 504–507. IEEE (2017)
Yu, M., Yang, C., Li, Y.: Big data in natural disaster management: a review. Geosciences 8(5), 165 (2018)
Acknowledgements
This article is funded by Sichuan University Innovation Spark Project (No.2018hhs-21), Sichuan University Central University Basic Scientific Research Project (No.skqx201501).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sarker, M.N.I., Wu, M., Chanthamith, B., Ma, C. (2020). Resilience Through Big Data: Natural Disaster Vulnerability Context. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-49829-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49828-3
Online ISBN: 978-3-030-49829-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)