An Approach to Discovering Spatial–Temporal Patterns in Geographical Processes

  • Siyue Chai
  • Fenzhen Su
  • Weiling Ma
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Spatial data mining focuses on searching rules of the geographical statement, the structures of distribution and the spatial patterns of phenomena. However, many methods ignore the temporal information, thus, limited results describing the statement of spatial phenomena. This paper focuses on developing a mining method which directly detects spatial temporal association rules hidden in the geographical process. Through such approach, geographical process can be extracted as a particle which exists in spatial temporal-attribute dimensions. By setting customized fixed-window, geographical process in one time interval is organized as a record with attribute value and spatial orientation change. Spatial temporal association rules can be found in geographic process mining table.
$$ \left[ {{\hbox{TimeInterva}}{{\hbox{l}}_i},{\hbox{MovingDirectio}}{{\hbox{n}}_m},{\hbox{P}}} \right]{ } \Rightarrow \left[ {{\hbox{TimeInterva}}{{\hbox{l}}_j},{\hbox{MovingDirectio}}{{\hbox{n}}_n},{\hbox{Q}}} \right] $$

To verify this mining approach, it is applied on AVHRR MCSST thermal data for extracting Indo-Pacific warm pools frequent movement patterns. The raw data provided by PO.DAAC, whose time spans of 20years from 1981 to 2000 with 7days time particle, has been used to mining spatial temporal association rules. In the experiment, we extract warm pool within 30N 30S, 100E 140W and use 28C as temperature threshold. After which Warm Pool s geographical process table is established so as to describe the variation of warm pool in spatial temporal-attribute dimension. In the mining process, 18 spatialtemporal process frequent models can be found by setting minimal support threshold at 10% and confidence threshold at 60%. The result shows such a methodology can mine complicated spatial temporal rules in realistic data. At the same time, the mining result of warm pool s frequent movement patterns may provide reference for oceanographers.


Association Rule Warm Pool Area Shrinking Temporal Rule Traditional Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering (ICDE’95), IEEE Computer Society, Taipei, pp 3–14Google Scholar
  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Rec 22(2):207–216CrossRefGoogle Scholar
  3. Appice A, Buono P (2005) Analyzing multi-level spatial association rules through a graph-based visualization. In: Ali M, Esposito F (eds) Innovations in applied arepsicial intelligence, vol 3533. Springer, Heidelberg, pp 448–458Google Scholar
  4. Bembenik R, Rybiński H (2009) FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams. J Intell Info Syst 33(1):41–64CrossRefGoogle Scholar
  5. Brijker JM, Jung SJA et al (2007) ENSO related decadal scale climate variability from the Indo-Pacific warm pool. Earth Planet Sci Lett 253(1–2):67–82CrossRefGoogle Scholar
  6. Cheng X, Qi Y et al (2008) Trends of sea level variations in the Indo-Pacific warm pool. Global Planet Change 63(1):57–66CrossRefGoogle Scholar
  7. Coster M, Chermant JL (1985) Précis d’analyse d’ images. Editions du CNRS, ParisGoogle Scholar
  8. Enfield DB, Lee S-K et al (2005) How are large western hemisphere warm pools formed? Prog Oceanography 70(2–4):346–365Google Scholar
  9. Fen-zhen SU, Cheng-hu ZHOU (2006) A framework for Process Geographical Information System. Geogr Res 25(3):477–483Google Scholar
  10. Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: Dayal U, Gray PMD, Nishio S (eds) Proceedings of 21st international conference on very large data bases (VLDB’95), Morgan Kaufmann, Zurich, pp 420–431Google Scholar
  11. Huang Y-P, Kao L-J et al (2008) Efficient mining of salinity and temperature association rules from ARGO data. Expert Syst Appl 35(1–2):59–68CrossRefGoogle Scholar
  12. Hwang S-Y, Wei C-P et al (2004) Discovery of temporal patterns from process instances. Comput Ind 53(3):345–364CrossRefGoogle Scholar
  13. Inokuchi A, Washio T et al. (2000) An apriori algorithm for mining frequent substructures from graph data. Principles of Data Mining and Knowledge Discovery 73(2):13–28Google Scholar
  14. Kong X, Wei Q et al. (2010) An approach to discovering multi-temporal patterns and its application to financial databases. Information Sciences 180(6):873–885Google Scholar
  15. Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th international symposium on advances in spatial databases (SSD’95), Springer, Berlin, pp 47–66Google Scholar
  16. Kuramochi M, Karypis G (2001) Frequent subgraph discovery. In: Proceedings of the IEEE international conference on data mining (ICDM’01), San Jose, pp 313–320Google Scholar
  17. Lee AJT, Chen Y-A et al (2009) Mining frequent trajectory patterns in spatial–temporal databases. Info Sci 179(13):2218–2231CrossRefGoogle Scholar
  18. McPhaden MJ, Picaut J (1990) El Niñ-Southern Oscillation displacements of the western equatorial Pacific warm pool. Science 250:1385–1388CrossRefGoogle Scholar
  19. Pei J, Han J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC (2001) PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 2001 international conference on extending database technology: advances in database technology, Heidelberg, pp 215–224Google Scholar
  20. Qilong Z, Xuechuan W (1997) Analysis of some oceangraphic characteristics of the tropical western pacific warm pool. Studia Marina Sin 38(01):31–38. El Niño-Southern Oscillation, Google Scholar
  21. Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Jensen CS, Schneider M, Seeger VJ, Tsotras B (eds) Proceedings of the 7th international symposium on the advances in spatial and temporal databases. LNCS, vol 2121. Springer, Berlin/Heidelberg/New York, pp 236–256Google Scholar
  22. Trenberth KE, Jones PD et al (2007) Observations: surface and atmospheric climate change. In: Qin SD, Manning M et al (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp 235–336Google Scholar
  23. Verhein F, Chawla S (2008) Mining spatio-temporal patterns in object mobility databases. Data Min Knowl Discov 16(1):5–38CrossRefGoogle Scholar
  24. Wanying LKZCS (1998) Basic features of the warm pool in the western pacific and its impact on climate. Acta Geogr Sin 53(6):511–519Google Scholar
  25. Winarko E, Roddick JF (2007) ARMADA – an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 63(1):76–90CrossRefGoogle Scholar
  26. Zaki MJ (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn 42(1):31–60CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Geographic Sciences and Natural Resources ResearchBeijingChina
  2. 2.Chinese Academy of ScienceBeijingChina

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