JDR Vol.17 No.6 pp. 1090-1100
doi: 10.20965/jdr.2022.p1090


Application of Open Data in Disaster Risk Research: A Preliminary Review Using Bibliometric Analysis

Jingyi Gao*,†, Wei Chen**, and Osamu Murao***

*Department of Architecture and Building Science, Graduate School of Engineering, Tohoku University
6-6 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

Corresponding author

**School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China

***International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, Japan

December 3, 2021
June 15, 2022
October 1, 2022
disaster risk, open data, bibliometric method, Web of Science Core Collection, CiteSpace

Open data is a practical source for identifying disaster risks. However, few studies have examined open data usage. This study employed CiteSpace to conduct a bibliometric analysis to determine the evolution of open data in the field of disaster risk based on the literature. The findings were as follows: first, the existing disaster-related research can be classified into four categories: introduction to risk management and its concepts, multi-hazard response, studies on the specific background or context, and analysis of the technology or methods used in disaster risk reduction. Second, the relevant literature first emerged in 1997 and has rapidly expanded in recent years. Top keywords were identified, such as “natural disaster,” “risk,” and “climate change.” Third, the most productive country in terms of publications has been the People’s Republic of China; however, the low centrality indicates a lack of international collaborations. Fourth, several bursts were found in the collected literature. The term “data analysis” appears to be one of the most pressing concerns. Finally, we identified the research frontiers. The topic “accessible global dataset” has been of primary interest to researchers recently. The results of this study can provide directional references for future research in the field.

Cite this article as:
J. Gao, W. Chen, and O. Murao, “Application of Open Data in Disaster Risk Research: A Preliminary Review Using Bibliometric Analysis,” J. Disaster Res., Vol.17 No.6, pp. 1090-1100, 2022.
Data files:
  1. [1] World Economic Forum, “The global risks report 2021,” 16th Edition, 2021, [accessed May 20, 2021]
  2. [2] K. Satake, “Advances in earthquake and tsunami sciences and disaster risk reduction since the 2004 Indian ocean tsunami,” Geosci. Lett., Vol.1, No.1, Article No.15, 2014.
  3. [3] X. Li et al., “Measuring county resilience after the 2008 Wenchuan earthquake,” Int. J. Disaster Risk Sci., Vol.7, No.4, pp. 393-412, 2016.
  4. [4] Y. Zhang, W. Hua, and S. Yuan, “Mapping the scientific research on open data: A Bibliometric review,” Learn. Publ., Vol.31, No.2, pp. 95-106, 2018.
  5. [5] G. Shen and S. N. Hwang, “Spatial–temporal snapshots of global natural disaster impacts revealed from EM-DAT for 1900–2015,” Geomat. Nat. Hazards Risk, Vol.10, No.1 pp. 912-934, doi: 10.1080/19475705.2018.1552630, 2019.
  6. [6] A. O. Talisuna et al., “Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016–2018,” Global. Health, Vol.16, No.1, Article No.9, 2020.
  7. [7] C. S. Witham, “Volcanic disasters and incidents: A new database,” J. Volcanol. Geotherm. Res., Vol.148, Nos.3-4, pp. 191-233, 2005.
  8. [8] C. Huggel et al., “How useful and reliable are disaster databases in the context of climate and global change? A comparative case study analysis in Peru,” Nat. Hazards Earth Syst. Sci., Vol.15, No.3, pp. 475-485, 2015.
  9. [9] G. Li et al., “Gap analysis on open data interconnectivity for disaster risk research,” Geo-Spat. Inf. Sci., Vol.22, No.1, pp. 45-58, 2019.
  10. [10] S. Kanbara and R. Shaw, “Disaster risk reduction regime in Japan: An analysis in the perspective of open data, open governance,” Sustainability, Vol.14, No.1, Article No.19, doi: 10.3390/su14010019, 2022.
  11. [11] M. K. McBurney and P. L. Novak, “What is bibliometrics and why should you care?,” Proc. of IEEE Int. Professional Communication Conf. (IPCC 2002), pp. 108-114, 2002.
  12. [12] I. Zupic and T. Čater, “Bibliometric methods in management and organization,” Organ. Res. Methods, Vol.18, No.3, pp. 429-472, 2015.
  13. [13] N. Oh and J. Lee, “Changing landscape of emergency management research: A systematic review with bibliometric analysis,” Int. J. Disaster Risk Reduct., Vol.49, Article No.101658, 2020.
  14. [14] X. Zhang et al., “People-centered early warning systems in China: A bibliometric analysis of policy documents,” Int. J. Disaster Risk Reduct., Vol.51, Article No.101877, 2020.
  15. [15] M. T. Amin, F. Khan, and P. Amyotte, “A bibliometric review of process safety and risk analysis,” Process Saf. Environ. Prot., Vol.126, pp. 366-381, 2019.
  16. [16] M. Gall, K. H. Nguyen, and S. L. Cutter, “Integrated research on disaster risk: Is it really integrated?,” Int. J. Disaster Risk Reduct., Vol.12, pp. 255-267, 2015.
  17. [17] C. Giupponi and C. Biscaro, “Vulnerabilities – Bibliometric analysis and literature review of evolving concepts,” Environ. Res. Lett., Vol.10, No.12, Article No.123002, 2015.
  18. [18] B. J. Kim, S. Jeong, and J.-B. Chung, “Research trends in vulnerability studies from 2000 to 2019: Findings from a bibliometric analysis,” Int J. Disaster Risk Reduct., Vol.56, Article No.102141, 2021.
  19. [19] W. M. Sweileh, “A bibliometric analysis of health-related literature on natural disasters from 1900 to 2017,” Health Res. Policy Syst., Vol.17, No.1, Article No.18, 2019.
  20. [20] B. Barnes, S. Dunn, and S. Wilkinson, “Natural hazards, disaster management and simulation: A bibliometric analysis of keyword searches,” Nat. Hazards, Vol.97, No.2, pp. 813-840, 2019.
  21. [21] C. Curt, “Multirisk: What trends in recent works? – A bibliometric analysis,” Sci. Total Environ., Vol.763, Article No.142951, 2021.
  22. [22] W.-T. Chiu and Y.-S. Ho, “Bibliometric analysis of tsunami research,” Scientometrics, Vol.73, No.1, pp. 3-17, 2007.
  23. [23] N. Jain, D. Virmani, and A. Abraham, “Tsunami in the last 15 years: A bibliometric analysis with a detailed overview and future directions,” Nat. Hazards, Vol.106, No.1, pp. 139-172, 2021.
  24. [24] L. B. L. da Silva, M. H. Alencar, and A. T. de Almeida, “Multidimensional flood risk management under climate changes: Bibliometric analysis, trends and strategic guidelines for decision-making in urban dynamics,” Int. J. Disaster Risk Reduct., Vol.50, Article No.101865, 2020.
  25. [25] C. O. Lima and J. Bonetti, “Bibliometric analysis of the scientific production on coastal communities’ social vulnerability to climate change and to the impact of extreme events,” Nat. Hazards, Vol.102, No.3, pp. 1589-1610, 2020.
  26. [26] I. A. Rana, “Disaster and climate change resilience: A bibliometric analysis,” Int. J. Disaster Risk Reduct., Vol.50, Article No.101839, 2020.
  27. [27] G. Di Matteo et al., “Bibliometric analysis of Climate Change Vulnerability Assessment research,” Environ. Syst. Decis., Vol.38, No.4, pp. 508-516, 2018.
  28. [28] J.-L. Fan et al., “Scientific and technological power and international cooperation in the field of natural hazards: A bibliometric analysis,” Nat. Hazards, Vol.102, No.3, pp. 807-827, 2020.
  29. [29] Sahil and S. K. Sood, “Bibliometric monitoring of research performance in ICT-based disaster management literature,” Qual. Quant., Vol.55, No.1, pp. 103-132, 2021.
  30. [30] Y. Feng, Q. Zhu, and K.-H. Lai, “Corporate social responsibility for supply chain management: A literature review and bibliometric analysis,” J. Clean. Prod., Vol.158, pp. 296-307, 2017.
  31. [31] O. Ellegaard and J. A. Wallin, “The bibliometric analysis of scholarly production: How great is the impact?,” Scientometrics, Vol.105, No.3, pp. 1809-1831, 2015.
  32. [32] C. Chen, “CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature,” J. Am. Soc. Inf. Sci. Technol., Vol.57, No.3, pp. 359-377, 2006.
  33. [33] C. Chen, “A glimpse of the first eight months of the COVID-19 literature on Microsoft Academic Graph: Themes, citation contexts, and uncertainties,” Front. Res. Metr. Anal., Vol.5, Article No.607286, 2020.
  34. [34] J. W. Buzydlowski, “Co-occurrence analysis as a framework for data mining,” J. Technol. Res., Vol.6. 19pp., 2015.
  35. [35] X. Jing et al., “Mapping global research related to international students: A scientometric review,” High. Educ., Vol.80, No.3, pp. 415-433, 2020.
  36. [36] R. K. Blashfield and M. S. Aldenderfer, “The literature on cluster analysis,” Multivar. Behav. Res., Vol.13, No.3, pp. 271-295, 1978.
  37. [37] J. Kleinberg, “Bursty and hierarchical structure in streams,” Data Min. Knowl. Discov., Vol.7, No.4, pp. 373-397, 2003.
  38. [38] R. R. Braam, H. F. Moed, and A. F. J. van Raan, “Mapping of science by combined co-citation and word analysis. II: Dynamical aspects,” J. Am. Soc. Inf. Sci., Vol.42, No.4, pp. 252-266, 1991.
  39. [39] L. C. Freeman, “Centrality in social networks conceptual clarification,” Soc. Netw., Vol.1, No.3, pp. 215-239, 1978.
  40. [40] U. Brandes, “A faster algorithm for betweenness centrality,” J. Math. Sociol., Vol.25, No.2, pp. 163-177, 2001.
  41. [41] E. E. Koks et al., “Combining hazard, exposure and social vulnerability to provide lessons for flood risk management,” Environ. Sci. Policy, Vol.47, pp. 42-52, 2015.
  42. [42] M. Ding et al., “Regional vulnerability assessment for debris flows in China – A CWS approach,” Landslides, Vol.13, No.3, pp. 537-550, 2015.
  43. [43] S. Lindersson et al., “A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies,” WIREs Water, Vol.7, No.3, Article No.e1424, 2020.
  44. [44] M. R. Ferdous et al., “The interplay between structural flood protection, population density, and flood mortality along the Jamuna River, Bangladesh,” Reg. Environ. Change, Vol.20, No.1, Article No.5, 2020.
  45. [45] M. N. I. Sarker et al., “Disaster resilience through big data: Way to environmental sustainability,” Int. J. Disaster Risk Reduct., Vol.51, Article No.101769, 2020.
  46. [46] A. G. Rumson, A. P. Garcia, and S. H. Hallett, “The role of data within coastal resilience assessments: An East Anglia, UK, case study,” Ocean Coast. Manag., Vol.185, Article No.105004, 2020.
  47. [47] F. Pagliacci and M. Russo, “Be (and have) good neighbours! Factors of vulnerability in the case of multiple hazards,” Ecol. Indic., Vol.111, Article No.105969, 2020.
  48. [48] S. Eberenz et al., “Asset exposure data for global physical risk assessment,” Earth Syst. Sci. Data, Vol.12, No.2, pp. 817-833, 2020.
  49. [49] U. A. Bukar et al., “Crisis informatics in the context of social media crisis communication: Theoretical models, taxonomy, and open issues,” IEEE Access, Vol.8, pp. 185842-185869, 2020.
  50. [50] A. Fekete, “Critical infrastructure cascading effects. Disaster resilience assessment for floods affecting city of Cologne and Rhein-Erft-Kreis,” J. Flood Risk Manag., Vol.13, No.2, Article No.e312600, 2020.

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