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A systematic literature review on the use of big data analytics in humanitarian and disaster operations

Author

Listed:
  • Abhilash Kondraganti

    (University of Liverpool Management School)

  • Gopalakrishnan Narayanamurthy

    (University of Liverpool Management School)

  • Hossein Sharifi

    (University of Liverpool Management School)

Abstract

At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed.

Suggested Citation

  • Abhilash Kondraganti & Gopalakrishnan Narayanamurthy & Hossein Sharifi, 2024. "A systematic literature review on the use of big data analytics in humanitarian and disaster operations," Annals of Operations Research, Springer, vol. 335(3), pages 1015-1052, April.
  • Handle: RePEc:spr:annopr:v:335:y:2024:i:3:d:10.1007_s10479-022-04904-z
    DOI: 10.1007/s10479-022-04904-z
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    1. Lu (Lucy) Yan & Alfonso J. Pedraza‐Martinez, 2019. "Social Media for Disaster Management: Operational Value of the Social Conversation," Production and Operations Management, Production and Operations Management Society, vol. 28(10), pages 2514-2532, October.
    2. Fan, Chao & Zhang, Cheng & Yahja, Alex & Mostafavi, Ali, 2021. "Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management," International Journal of Information Management, Elsevier, vol. 56(C).
    3. Martijn Warnier & Vincent Alkema & Tina Comes & Bartel Walle, 2020. "Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 815-834, September.
    4. Bell, David & Lycett, Mark & Marshan, Alaa & Monaghan, Asmat, 2021. "Exploring future challenges for big data in the humanitarian domain," Journal of Business Research, Elsevier, vol. 131(C), pages 453-468.
    5. Róisín Read & Bertrand Taithe & Roger Mac Ginty, 2016. "Data hubris? Humanitarian information systems and the mirage of technology," Third World Quarterly, Taylor & Francis Journals, vol. 37(8), pages 1314-1331, August.
    6. Snyder, Hannah, 2019. "Literature review as a research methodology: An overview and guidelines," Journal of Business Research, Elsevier, vol. 104(C), pages 333-339.
    7. Laura Mann, 2018. "Left to Other Peoples’ Devices? A Political Economy Perspective on the Big Data Revolution in Development," Development and Change, International Institute of Social Studies, vol. 49(1), pages 3-36, January.
    8. Li, Lifang & Zhang, Qingpeng & Tian, Jun & Wang, Haolin, 2018. "Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake," International Journal of Information Management, Elsevier, vol. 38(1), pages 34-41.
    9. Jayashankar M. Swaminathan, 2018. "Big Data Analytics for Rapid, Impactful, Sustained, and Efficient (RISE) Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1696-1700, September.
    10. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    11. Mann, Laura, 2018. "Left to other peoples’ devices? A political economy perspective on the big data revolution in development," LSE Research Online Documents on Economics 85057, London School of Economics and Political Science, LSE Library.
    12. Abhishek Behl & Meena Chavan & Kokil Jain & Isha Sharma & Vijay Edward Pereira & Justin Zuopeng Zhang, 2021. "The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(2), pages 569-586, July.
    13. Aghaei Chadegani, Arezoo & Salehi, Hadi & Md Yunus, Melor & Farhadi, Hadi & Fooladi, Masood & Farhadi, Maryam & Ale Ebrahim, Nader, 2013. "A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases," MPRA Paper 46898, University Library of Munich, Germany, revised 18 Mar 2013.
    14. Marco Avvenuti & Stefano Cresci & Fabio Del Vigna & Tiziano Fagni & Maurizio Tesconi, 2018. "CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing," Information Systems Frontiers, Springer, vol. 20(5), pages 993-1011, October.
    15. Galindo, Gina & Batta, Rajan, 2013. "Review of recent developments in OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 230(2), pages 201-211.
    16. Angelika Wirtz & Wolfgang Kron & Petra Löw & Markus Steuer, 2014. "The need for data: natural disasters and the challenges of database management," 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. 70(1), pages 135-157, January.
    17. Xianhua Wu & Yaru Cao & Yang Xiao & Ji Guo, 2020. "Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics," Annals of Operations Research, Springer, vol. 290(1), pages 865-896, July.
    18. Behl, Abhishek & Dutta, Pankaj, 2020. "Engaging donors on crowdfunding platform in Disaster Relief Operations (DRO) using gamification: A Civic Voluntary Model (CVM) approach," International Journal of Information Management, Elsevier, vol. 54(C).
    19. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    20. Xi Zhang & Lixin Yi & Dong Zhao, 2013. "Community-based disaster management: a review of progress in China," 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. 65(3), pages 2215-2239, February.
    21. Sameer Prasad & Rimi Zakaria & Nezih Altay, 2018. "Big data in humanitarian supply chain networks: a resource dependence perspective," Annals of Operations Research, Springer, vol. 270(1), pages 383-413, November.
    22. Shivam Gupta & Nezih Altay & Zongwei Luo, 2019. "Big data in humanitarian supply chain management: a review and further research directions," Annals of Operations Research, Springer, vol. 283(1), pages 1153-1173, December.
    23. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    24. Richard Oloruntoba & Gazi Farid Hossain & Beverly Wagner, 2019. "Theory in humanitarian operations research," Annals of Operations Research, Springer, vol. 283(1), pages 543-560, December.
    25. Ragini, J. Rexiline & Anand, P.M. Rubesh & Bhaskar, Vidhyacharan, 2018. "Big data analytics for disaster response and recovery through sentiment analysis," International Journal of Information Management, Elsevier, vol. 42(C), pages 13-24.
    26. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    27. Daniel A. Griffith & Bradley Boehmke & Randy V. Bradley & Benjamin T. Hazen & Alan W. Johnson, 2019. "Embedded analytics: improving decision support for humanitarian logistics operations," Annals of Operations Research, Springer, vol. 283(1), pages 247-265, December.
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