Advertisement

World Climate Search and Classification Using a Dynamic Time Warping Similarity Function

  • Pawel Netzel
  • Tomasz F. Stepinski
Conference paper
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

We present a data-mining approach to climate classification and analysis. Local climates are represented as time series of climatic variables. A similarity between two local climates is calculated using the dynamic time warping (DTW) function that allows for scaling and shifting of the time axis to model the similarity more appropriately than a Euclidean function. A global grid of climatic data is clustered into 5 and 13 climatic classes, and the resultant world-wide map of climate types is compared to the empirical Köppen–Geiger classification. We also present a concept of climate search—an interactive, Internet-based application that allows retrieval and mapping of world-wide locations having climates similar to a user-selected location query.

Keywords

Climate classification Dynamic time warping Climate search Clustering Similarity 

Notes

Acknowledgements

This work was supported by the University of Cincinnati Space Exploration Institute, and by the National Aeronautics and Space Administration through grant NNX15AJ47G.

References

  1. Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. KDD Workshop 10(16):359–370Google Scholar
  2. Cannon AJ (2012) Köppen versus the computer: comparing Köppen-Geiger and multivariate regression tree climate classifications in terms of climate homogeneity. Hydrol Earth Syst Sci 16(1):217–229CrossRefGoogle Scholar
  3. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227CrossRefGoogle Scholar
  4. Hijmans RJ, Cameron SE, Parra JL, Jones P, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978CrossRefGoogle Scholar
  5. Kaufman L, Rousseeuw P (1987) Clustering by means of medoids. In: Dodge Y (ed) Statistical data analysis based on the L1 norm and related methods. North-Holland, pp 405–416Google Scholar
  6. Köppen W (1936) Das geographische system der klimate. In: Köppen W, Geiger R (eds) Handbuch der klimatologie. Gebrueder Borntraeger, Berlin, pp 1–44Google Scholar
  7. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15(3):259–263CrossRefGoogle Scholar
  8. Metzger MJ, Bunce RGH, Jongman RHG, Sayre R, Trabucco A, Zomer R (2012) A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob Ecol Biogeogr 22(5):630–638CrossRefGoogle Scholar
  9. Rabiner L, Juand B (1993) Fundamentals of speech recognition. Prentice-Hall International IncGoogle Scholar
  10. Rosenberg A, Hirschberg J (2007) V-Measure: a conditional entropy-based external cluster evaluation measure. In: Joint conference on empirical methods in natural language processing and computational natural language learning, pp 410–420Google Scholar
  11. Santiago A (2015) The book of OpenLayers 3. Theory and Practice, Leanpub, Victoria, BCGoogle Scholar
  12. Stepinski T, Netzel P, Jasiewicz J (2014) Landex—a geoweb tool for query and retrieval of spatial patterns in land cover datasets. IEEE J Sel Top Appl Earth Obs Remote Sens 7(1):257–266Google Scholar
  13. Usery EL, Seong J (2001) All equal-area map projections are created equal, but some are more equal than others. Cartogr Geogr Inf Sci 28(3):183–193CrossRefGoogle Scholar
  14. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244CrossRefGoogle Scholar
  15. Wilkinson L, Friendly M (2009) The history of the cluster heat map. Am Stat 63(2):179–184CrossRefGoogle Scholar
  16. Zhang X, Yan X (2014) Spatiotemporal change in geographical distribution of global climate types in the context of climate warming. Clim Dyn 43(3–4):595–605CrossRefGoogle Scholar
  17. Zscheischler J, Mahecha MD, Harmeling S (2012) Climate classifications: the value of unsupervised clustering. Procedia Comput Sci 9:897–906CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Space Informatics LabUniversity of CincinnatiCincinnatiUSA

Personalised recommendations