Advertisement

Analysis of Natural and Technogenic Safety of the Krasnoyarsk Region Based on Data Mining Techniques

  • Tatiana PenkovaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9975)

Abstract

This paper presents a comprehensive analysis of natural and technogenic safety indicators of the Krasnoyarsk region in order to explore geographical variations and patterns in occurrence of emergencies by applying the multidimensional analysis techniques – principal component analysis and cluster analysis – to data of the Territory Safety Passports. For data modelling, two principal components are selected and interpreted taking account of the contribution of the data attributes to the principal components. Data distribution on the principal components is analysed at different levels of the territory detail: municipal areas and settlements. Two- and three- cluster structures are constructed in multidimensional data space; the main clusters features are analyzed. The results of this analysis have allowed to identify the high-risk municipal areas and rank the territories according to danger degree of occurrence of the natural and technogenic emergencies. It gives the basis for decision making and makes it possible for authorities to allocate the forces and means for territory protection more efficiently and develop a system of measures to prevent and mitigate the consequences of emergencies in the large region.

Keywords

Comprehensive data analysis Data mining Principal component analysis Cluster analysis Prevention of emergencies Territorial management Decision making support 

References

  1. 1.
    Report of the State of Natural and Anthropogenic Emergencies Protection of Territory and Population in the Krasnoyarsk Region: Annual Report of Ministry of Emergency, Krasnoyarsk, p. 254 (2014) (in Russian)Google Scholar
  2. 2.
    Regional Organizational System of Emergency Monitoring and Prediction: The Regulation of the Krasnoyarsk Region, p. 80 (2011) (in Russian)Google Scholar
  3. 3.
    Penkova, T.G., Korobko, A.V., Nicheporchuk, V.V., Nozhenkova, L.F.: On-line modelling and assessment of the state of technosphere and environment objects based on monitoring data. Procedia Comput. Sci. 35, 156–165 (2014)CrossRefGoogle Scholar
  4. 4.
    Yronen, Y.P., Yronen, E.A., Ivanov, V.V., Kovalev, I.V., Zelenkov, P.V.: The concept of creation of information system for environmental monitoring based on modern gis-technologies and earth remote sensing data. In: IOP Conference Series: Materials Science and Engineering, vol. 94, 012023 (2015). doi: 10.1088/1757-899X/94/1/012023
  5. 5.
    Shaparev, N.Y.: Environmental monitoring of the krasnoyarsk region in terms of sustainable environmental management. Inf. Anal. Bull. (Scientific and Technical Journal) 18(12), 110–113 (2009). (in Russian)Google Scholar
  6. 6.
    Bryukhanova, E.A., Kobalinskiy, M.V., Shishatskiy, N.G., Sibgatulin, V.G.: Improvement of environmental monitoring information maintenance as an instrument for sustainable social and economic development (on the example of the Krasnoyarsk Region). Inf. Commun. 1, 43–47 (2014) (in Russian)Google Scholar
  7. 7.
    The Standard Territory Passport of Regions and Municipal Areas: The Regulation of Ministry of Emergency, no. 484 (2004) (in Russian)Google Scholar
  8. 8.
    Giudici, P.: Applied Data Mining: Statistical Methods for Business and Industry, p. 376. Wiley, Chichester (2005)Google Scholar
  9. 9.
    Williams, G.J., Simoff, S.J.: Data Mining: Theory, Methodology, Techniques, and Applications. LNAI, vol. 3755, p. 331. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Gorban A., Pitenko A., Zinovyev A.: ViDaExpert: User-friendly Tool for Nonlinear Visualization and Analysis of Multidimensional Vectorial Data. Cornell University Library. http://arxiv.org/abs/1406.5550
  11. 11.
    Using ArcViewGIS: The Geographic Information System of Everyone. ESRI Press, p. 350 (1996)Google Scholar
  12. 12.
    Abdi, H., Williams, L.: Principal components analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 439–459 (2010)CrossRefGoogle Scholar
  13. 13.
    Jain, A., Dubes, R.: Algorithms for Clustering Data, p. 320. Michigan State University, Prentice Hall, East Lansing, Englewood Cliffs (1988)zbMATHGoogle Scholar
  14. 14.
    Peres-Neto, P., Jackson, D., Somers, K.: How many principal components? stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data Anal. 49(4), 974–997 (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Computational Modelling SB RASKrasnoyarskRussia

Personalised recommendations