Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Controlling patterns of geospatial phenomena

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Modeling spatially distributed phenomena in terms of its controlling factors is a recurring problem in geoscience. Most efforts concentrate on predicting the value of response variable in terms of controlling variables either through a physical model or a regression model. However, many geospatial systems comprises complex, nonlinear, and spatially non-uniform relationships, making it difficult to even formulate a viable model. This paper focuses on spatial partitioning of controlling variables that are attributed to a particular range of a response variable. Thus, the presented method surveys spatially distributed relationships between predictors and response. The method is based on association analysis technique of identifying emerging patterns, which are extended in order to be applied more effectively to geospatial data sets. The outcome of the method is a list of spatial footprints, each characterized by a unique “controlling pattern”—a list of specific values of predictors that locally correlate with a specified value of response variable. Mapping the controlling footprints reveals geographic regionalization of relationship between predictors and response. The data mining underpinnings of the method are given and its application to a real world problem is demonstrated using an expository example focusing on determining variety of environmental associations of high vegetation density across the continental United States.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Buneman P, Jajodia S (eds) Proceedings of the 1993 ACM SIGMOD international conference on management of data. Washington, D.C., pp 26–28, 207–216

  2. Boulesteix AL, Tutz G, Strimmer K (2003) A cart-based approach to discover emerging patterns in microarray data. Bioinformatics 19(18):2465–2472

    Article  Google Scholar 

  3. Burdick D, Calimlim M, Gehrke J (2001) Mafia: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th international conference on data engineering. Heidelberg, Germany

  4. Ceci M, Appice A, Malerba D (2007) Discovering emerging patterns in spatial databases: a multi-relational approach. In: Knowledge discovery in databases: PKDD 2007, series: lecture notes in artificial intelligence, vol 4702. Springer, Berlin, pp 390–397

    Chapter  Google Scholar 

  5. Cormode G, Muthukrishnan S (2004) What’s new: finding significant differences in network data streams. In: IEEE INFOCOM

  6. Cressie, NA (1993) Statistics for spatial data. Wiley, New York

    Google Scholar 

  7. Ding W, Stepinski TF, Parmar R, Jiang D, Eick CF (2009) Discovery of feature-based hot spots using supervised clustering. Comput Geosci 35:1508–1516

    Article  Google Scholar 

  8. Ding W, Stepinski TF, Salazar, J (2009) Discovery of geospatial discriminating patterns from remote sensing datasets. In: SIAM international conference on data mining (SDM), Nevada, April 2009

  9. Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: KDD ’99: proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. San Diego, California, United States

  10. Korkalainen T, Lauren A (2006) Using phytogeomorphology, cartography and GIS to explain forest site productivity expressed as tree height in southern and central Finland. Geomorphology 74:271–284

    Article  Google Scholar 

  11. Larsen DR, Speckman, PL (2004) Multivariate regression trees for analysis of abundance data. Biometrics, 60(2):543–549

    Article  Google Scholar 

  12. Li J, Wong L (2005) Structural geography of the space of emerging patterns. Intelligent Data Analysis 9(6):567–588

    Google Scholar 

  13. Li J, Yang Q (2007) Strong compound-risk factors: efficient discovery through emerging patterns and contrast sets. IEEE Trans Inf Technol Biomed 11:544–552

    Article  Google Scholar 

  14. Li J, Liu H, S-K Ng, Wong L (2003) Discovery of significant rules for classifying cancer diagnosis data. Bioinformatics 19:ii93–ii102

    Google Scholar 

  15. Liaghati T, Preda M, Cox M (2003) Heavy metal distribution and controlling factors within coastal plain sediments, Bells Creek catchment, southeast Queensland, Australia. Environ Int 29:935–948

    Article  Google Scholar 

  16. Lobell, J. I. Ortiz-Monasterio, Asner GP, Naylor RL, Falcon WP (2005) Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agron J 97:241–249

    Google Scholar 

  17. Munkres J (1999) Topology, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  18. Navas A, Machín J (2002) Spatial distribution of heavy metals and arsenic in soils of Aragón (northeast Spain): controlling factors and environmental implications. Appl Geochem 17:961–973

    Article  Google Scholar 

  19. ORNL (2009) Oak Ridge National Laboratory distributed active archive center data holdings.

  20. Podraza R, Tomaszewski K (2005) KTDA: emerging patterns based data analysis system. In: XXI fall meeting of polish information processing society, pp 213–221

  21. PRISM (2009) PRISM (parameter-elevation regressions on independent slopes model) climate mapping system products matrix. PRISM, Corvallis

  22. Remmel TK, Csillag, F (2006) Mutual information spectra for comparing categorical maps. Int J Remote Sens 27:1425–1452

    Article  Google Scholar 

  23. Rousseeuw J, Croux C (1993) Alternatives to the median absolute deviation. J Am Stat Assoc 88:1273–1283

    Article  Google Scholar 

  24. Rusjan S, Mikos, M (2008) Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique. Hydrol Earth Syst Sci 12:645–656

    Article  Google Scholar 

  25. Seamless (2009) National map seamless server. USGS, Denver

  26. Steegen A, Govers G, Takkena I, Nachtergaelea J, Poesena J, Merckxb R (2001) Factors controlling sediment and phosphorus export from two Belgian agricultural catchments. J Environ Qual 30:1249–1258

    Article  Google Scholar 

  27. Stepinski T, Ding W, Eick C (2008) Discovering controlling factors of geospatial variables. In: The 16th ACM SIGSPATIAL international conference on advances in geographic information systems (ACM GIS 2008). Irvine, CA, USA, pp 1–4

  28. Wang X, Qin Y (2005) Spatial distribution of metals in urban topsoils of Xuzhou (China): controlling factors and environmental implications. Environ Geol 49(6):905–914

    Article  Google Scholar 

  29. White D, Sifneos JC (2002) Regression tree cartography. J Comput Graph Stat 11(3):600–614

    Article  Google Scholar 

  30. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn (Morgan Kaufmann series in data management systems). Morgan Kaufmann, San Francisco

    Google Scholar 

Download references

Acknowledgements

The work is supported in part by the National Science Foundation under Grant IIS-0812271. A portion of this research was conducted at the Lunar and Planetary Institute, which is operated by the USRA under contract CAN-NCC5-679 with NASA. This is LPI Contribution No.1532.

Author information

Authors and Affiliations

  1. Lunar and Planetary Institute, Houston, TX, 77058, USA

    Tomasz F. Stepinski

  2. Department of Computer Science, University of Massachusetts Boston, Boston, MA, 02125-3393, USA

    Wei Ding

  3. Department of Computer Science, University of Houston, Houston, TX, 77204-3010, USA

    Christoph F. Eick

Authors
  1. Tomasz F. Stepinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph F. Eick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Ding.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stepinski, T.F., Ding, W. & Eick, C.F. Controlling patterns of geospatial phenomena. Geoinformatica 15, 399–416 (2011). https://doi.org/10.1007/s10707-010-0107-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-010-0107-2

Keywords

Search

Navigation