Spatial distribution patterns of global natural disasters based on biclustering

Abstract

Understanding the spatial distribution patterns (SDPs) of natural disasters plays an essential role in reducing and minimizing natural disaster risks. An integrated discussion on the SDPs of multiple global disasters is still lacking. In addition, due to their high quantity and complexity, natural disasters constitute high-dimensional data that represent a challenge for an analysis of SDPs. This paper analyzed the SDPs of global disasters from 1980 to 2016 through biclustering. The results indicate that the SDPs of fatality rates are more uneven than those of occurrence rates. Based on the occurrence rates, the selected countries were clustered into four classes. (1) The major disasters along the northern Pacific and in the Caribbean Sea and Madagascar are storms, followed by floods. (2) Most of Africa is mainly affected by floods, epidemics, and droughts. (3) The primary disaster types in the Alpine-Himalayan belt and the western Andes are floods and earthquakes. (4) Europe, America, Oceania, and South and Southeast Asia are predominantly influenced by floods. In addition, according to the fatality rates, the selected countries were clustered into eight classes. (1) Extreme high temperatures mostly result in high fatality rates (HFRs) in developed countries. (2) Epidemics lead to HFRs in parts of Africa. (3) Droughts produce HFRs in East Africa. (4) Earthquakes result in HFRs along the eastern Pacific coastline and the Alpine-Himalayan belt. (5) Tsunamis mainly cause HFRs in Thailand, Indonesia, and Japan. (6) Storms result in scattered but distinct HFRs along the coastal regions of the Pacific and Indian Oceans. (7) Floods cause concentrated HFRs in South Asia and northeastern South America. (8) Finally, volcanoes cause HFRs in Colombia, while extreme low temperatures cause HFRs in Ukraine and Poland.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Aoyama T, Shiratori N, Hagimoto K et al (2014) Lessons of the Great East Japan earthquake. IEEE Commun Mag 52:21–22

    Article  Google Scholar 

  2. Bathrellos GD, Skilodimou HD, Chousianitis K et al (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134

    Article  Google Scholar 

  3. Borden KA, Cutter SL (2008) Spatial patterns of natural hazards mortality in the United States. Int J Health Geogr 7:64

    Article  Google Scholar 

  4. Cheng Y, Church GM (2000) Biclustering of expression data. Intell Syst Mol Biol 8:93

    Google Scholar 

  5. Council NR (2012) Disaster resilience: a national imperative. The National Academies Press, Washington, DC

    Google Scholar 

  6. Cvetkovic V (2013) Spatial and temporal distribution of geophysical disasters. J Geogr Inst Jovan Cvijic SASA 63:345–360

    Article  Google Scholar 

  7. Dilley M (2006) Setting priorities: global patterns of disaster risk. Philos Trans R Soc A 364:2217–2229

    Article  Google Scholar 

  8. Ferraty F (2010) High-dimensional data: a fascinating statistical challenge. J Multivar Anal 101:305–306

    Article  Google Scholar 

  9. Guha-Sapir D EM-DAT: the CRED/OFDA international disaster database. http://www.emdat.be/

  10. Han W, Liang C, Jiang B et al (2016) Major natural disasters in China, 1985–2014: occurrence and damages. Int J Environ Res Public Health 13:1118

    Article  Google Scholar 

  11. Izenman AJ, Harris PW, Mennis J et al (2011) Local spatial biclustering and prediction of urban juvenile delinquency and recidivism. Stat Anal Data Min 4:259–275

    Article  Google Scholar 

  12. Knapp KR, Kruk MC, Levinson DH et al (2010) The international best track archive for climate stewardship (IBTrACS). Bull Am Meteorol Soc 91:363–376

    Article  Google Scholar 

  13. Li C, Chai Y, Yang L, Li H (2016) Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa. Nat Hazards 82:721–731

    Article  Google Scholar 

  14. Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 1:24–45

    Article  Google Scholar 

  15. Masih I, Maskey S, Mussá FEF, Trambauer P (2014) A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol Earth Syst Sci 18:3635–3649

    Article  Google Scholar 

  16. McCall C (2017) Remembering the Indian Ocean tsunami. Lancet 384:2095–2098

    Article  Google Scholar 

  17. Oh CH, Oetzel J (2011) Multinationals’ response to major disasters: how does subsidiary investment vary in response to the type of disaster and the quality of country governance? Strateg Manag J 32:658–681

    Article  Google Scholar 

  18. Padilha VA, Campello RJGB (2017) A systematic comparative evaluation of biclustering techniques. BMC Bioinform 18:55

    Article  Google Scholar 

  19. Peng Y, Song J, Cui T, Cheng X (2017) Temporal-spatial variability of atmospheric and hydrological natural disasters during recent 500 years in Inner Mongolia, China. Nat Hazards 89:441–456

    Article  Google Scholar 

  20. Pontes B, Giráldez R, Aguilar-Ruiz JS (2015) Biclustering on expression data: a review. J Biomed Inform 57:163–180

    Article  Google Scholar 

  21. Sasa M, Cvetkovic VM (2014) Victimization of people by natural disasters: spatial and temporal distribution of consequences. Temida 17:19–42

    Article  Google Scholar 

  22. Shen S, Cheng C, Su K et al (2016) Quantitative visualization about differences between scientists concerned nature disasters and historic events. In: 2016 International conference on behavioral, economic and socio-cultural computing (BESC), pp 1–6

  23. Shi P (1996) Theory and practice of disaster study. J Nat Disasters 11:6–17

    Google Scholar 

  24. Shi P (2002) Theory on disaster science and disaster dynamics. J Nat Disasters 11:1–9

    Google Scholar 

  25. Shi P, Li N, Ye Q et al (2010) Research on integrated disaster risk governance in the context of global environmental change. Int J Disaster Risk Sci 1:17–23

    Google Scholar 

  26. Stone R, Kerr RA (2005) Girding for the next killer wave. Science 310:1602–1605

    Article  Google Scholar 

  27. Tappin DR, Watts P, Grilli ST (2008) The Papua New Guinea tsunami of 17 July 1998: anatomy of a catastrophic event. Nat Hazards Earth Syst Sci 8:243–266

    Article  Google Scholar 

  28. Tweed F, Walker G (2011) Some lessons for resilience from the 2011 multi-disaster in Japan. Local Environ 16:937–942

    Article  Google Scholar 

  29. Yang T, Wang X, Zhao C et al (2011) Changes of climate extremes in a typical arid zone: observations and multimodel ensemble projections. J Geophys Res Atmos. https://doi.org/10.1029/2010JD015192

    Google Scholar 

  30. Zhao Y, Karypis G (2003) Clustering in life sciences. In: Brownstein MJ, Khodursky AB (eds) Functional genomics: methods and protocols. Humana Press, Totowa, pp 183–218

    Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments on the manuscript. This work was supported by National Natural Science Foundation of China [Grant Number 41771537], National Key Research and Development Plan of China [Grant Number 2017YFB0504102], and the Fundamental Research Funds for the Central Universities.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Changxiu Cheng.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shen, S., Cheng, C., Song, C. et al. Spatial distribution patterns of global natural disasters based on biclustering. Nat Hazards 92, 1809–1820 (2018). https://doi.org/10.1007/s11069-018-3279-y

Download citation

Keywords

  • Natural disasters
  • Biclustering
  • Spatial distribution
  • Multiple disasters