Skip to main content
SpringerLink
Log in

A Gentle Introduction to Spatiotemporal Data Mining

  • Chapter
  • First Online:
Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

Spatiotemporal data mining refers to the extraction of knowledge, regularly repeating relationships, and interesting patterns from data with spatial and temporal aspects. In recent years, many spatiotemporal frequent pattern mining algorithms were developed for spatiotemporal event instances represented by a series of region objects that evolves over time. These algorithms focus on the discovery of spatiotemporal co-occurrence patterns and event sequences by inspecting the spatiotemporal overlap and follow relationships. Before moving onto these relationships, we will demonstrate different types of spatiotemporal knowledge to place the relationships and methods in the greater context. This chapter provides a bird-eye view on the output of spatiotemporal data mining techniques in the literature, gives rationale for mining spatiotemporal patterns from evolving regions, and explains the challenges of mining patterns from evolving region data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abraham, T., Roddick, J.F.: Opportunities for knowledge discovery in spatio-temporal information systems. Australasian J. of Inf. Systems 5(2) (1998)

    Google Scholar 

  2. Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007). https://doi.org/10.1016/j.datak.2006.01.013

    Article  Google Scholar 

  3. Boubrahimi, S.F., Aydin, B., Kempton, D., Angryk, R.A.: Spatio-temporal interpolation methods for solar events metadata. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5–8, 2016, pp. 3149–3157 (2016). https://doi.org/10.1109/BigData.2016.7840970

  4. Buchin, M., Dodge, S., Speckmann, B.: Context-aware similarity of trajectories. In: International Conference on Geographic Information Science, pp. 43–56. Springer (2012)

    Google Scholar 

  5. Chen, L., Lv, M., Chen, G.: A system for destination and future route prediction based on trajectory mining. Pervasive and Mobile Computing 6(6), 657–676 (2010). https://doi.org/10.1016/j.pmcj.2010.08.004

    Article  Google Scholar 

  6. Conaghan, J., Chen, A.A., Willman, S.P., Ivani, K., Chenette, P.E., Boostanfar, R., Baker, V.L., Adamson, G.D., Abusief, M.E., Gvakharia, M., et al.: Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial. Fertility and sterility 100(2), 412–419 (2013)

    Article  Google Scholar 

  7. Cressie, N.A.C.: Statistics for spatial data. Wiley series in probability and mathematical statistics. J. Wiley & Sons (1993)

    MATH  Google Scholar 

  8. Dodge, S., Weibel, R., Lautenschütz, A.: Towards a taxonomy of movement patterns. Information Visualization 7(3-4), 240–252 (2008)

    Article  Google Scholar 

  9. Elsberry, R.L.: Predicting hurricane landfall precipitation: Optimistic and pessimistic views from the symposium on precipitation extremes. Bulletin of the American Meteorological Society 83(9), 1333–1339 (2002)

    Article  Google Scholar 

  10. Erwig, M.: Toward spatio-temporal patterns. In: R. de Caluwe, G. de Tr, G. Bordogna (eds.) Spatio-Temporal Databases, pp. 29–53. Springer Berlin Heidelberg (2004). https://doi.org/10.1007/978-3-662-09968-1\_3

    Chapter  Google Scholar 

  11. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA, pp. 226–231 (1996)

    Google Scholar 

  12. Evans, M.R., Oliver, D., Shekhar, S., Harvey, F.: Summarizing trajectories into k-primary corridors: a summary of results. In: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL’12, Redondo Beach, CA, USA, November 7–9, 2012, pp. 454–457 (2012). https://doi.org/10.1145/2424321.2424388

  13. Gauthreaux, S.A., and, C.G.B.: Bird movements on Doppler weather surveillance radar. Birding 35(6), 616–628 (2003)

    Google Scholar 

  14. Ge, Y., Xiong, H., Liu, C., Zhou, Z.: A taxi driving fraud detection system. In: 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11–14, 2011, pp. 181–190 (2011). https://doi.org/10.1109/ICDM.2011.18

  15. Herrero, J., Meseguer, M.: Selection of high potential embryos using time-lapse imaging: the era of morphokinetics. Fertility and sterility 99(4), 1030–1034 (2013)

    Article  Google Scholar 

  16. Hoerling, M., Kumar, A.: Atmospheric response patterns associated with tropical forcing. Journal of Climate 15(16), 2184–2203 (2002)

    Article  Google Scholar 

  17. Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)

    Article  Google Scholar 

  18. Hurlburt, N., Cheung, M., Schrijver, C., Chang, L., Freeland, S., Green, S., Heck, C., Jaffey, A., Kobashi, A., Schiff, D., et al.: Heliophysics event knowledgebase for the solar dynamics observatory (sdo) and beyond. In: The Solar Dynamics Observatory, pp. 67–78. Springer (2012)

    Google Scholar 

  19. Im, J., Jensen, J.R., Tullis, J.A.: Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledge-based systems. In: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2005, July 25–29, 2005, Seoul, Korea, Proceedings, pp. 2129–2132 (2005). https://doi.org/10.1109/IGARSS.2005.1526438

  20. Kang, J.M., Shekhar, S., Henjum, M., Novak, P.J., Arnold, W.A.: Discovering teleconnected flow anomalies: A relationship analysis of dynamic neighborhoods (RAD) approach. In: Advances in Spatial and Temporal Databases, 11th International Symposium, SSTD 2009, Aalborg, Denmark, July 8–10, 2009, Proceedings, pp. 44–61 (2009). https://doi.org/10.1007/978-3-642-02982-0\_6

    Chapter  Google Scholar 

  21. Kempton, D., Pillai, K.G., Angryk, R.A.: Iterative refinement of multiple targets tracking of solar events. In: 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27–30, 2014, pp. 36–44 (2014). https://doi.org/10.1109/BigData.2014.7004402

  22. Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering. In: Data Mining and Knowledge Discovery Handbook, 2nd ed., pp. 855–874. Springer (2010)

    Google Scholar 

  23. Kuhn, K., Campbell-Lendrum, D., Haines, A., Cox, J.: Using climate to predict infectious disease epidemics. World Health Organization, Geneva (2005)

    Google Scholar 

  24. Kulldorff, M.: Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society: Series A (Statistics in Society) 164(1), 61–72 (2001)

    Article  MathSciNet  Google Scholar 

  25. Langhoff, S.R., Straume, T.: Highlights of the Space Weather Risks and Society workshop. Space Weather 10(6) (2012)

    Article  Google Scholar 

  26. LMSAL: Heliophysics Event Registry (2014). URL http://lmsal.com/isolsearch. URL: http://lmsal.com/isolsearch, [Online; accessed 26-June-2014]

  27. Lowe, R., Bailey, T.C., Stephenson, D.B., Graham, R.J., Coelho, C.A., Carvalho, M.S., Barcellos, C.: Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in brazil. Computers & Geosciences 37(3), 371–381 (2011)

    Article  Google Scholar 

  28. Ma, H., Tsai, T., Liu, C.: Real-time monitoring of water quality using temporal trajectory of live fish. Expert Syst. Appl. 37(7), 5158–5171 (2010). https://doi.org/10.1016/j.eswa.2009.12.076

    Article  Google Scholar 

  29. Martens, P., Attrill, G., Davey, A., Engell, A., Farid, S., Grigis, P., Kasper, J., Korreck, K., Saar, S., Savcheva, A., et al.: Computer vision for the solar dynamics observatory (SDO). In: The Solar Dynamics Observatory, pp. 79–113. Springer (2012)

    Google Scholar 

  30. Mellor, P., Leake, C.: Climatic and geographic influences on arboviral infections and vectors. Revue scientifique et technique (International Office of Epizootics) 19(1), 41–54 (2000)

    Article  Google Scholar 

  31. Meseguer, M., Herrero, J., Tejera, A., Hilligsøe, K.M., Ramsing, N.B., Remohí, J.: The use of morphokinetics as a predictor of embryo implantation. Human reproduction 26(10), 2658–2671 (2011)

    Article  Google Scholar 

  32. de Mouzon, J., Goossens, V., Bhattacharya, S., Castilla, J., Ferraretti, A., Korsak, V., Kupka, M., Nygren, K.G., Andersen, A.N., et al.: Assisted reproductive technology in europe, 2006: results generated from european registers by eshre. Human Reproduction p. deq124 (2010)

    Google Scholar 

  33. Ntoutsi, I., Mitsou, N., Marketos, G.: Traffic mining in a road-network: How does the traffic flow? IJBIDM 3(1), 82–98 (2008). https://doi.org/10.1504/IJBIDM.2008.017977

    Article  Google Scholar 

  34. Paaijmans, K.P., Blanford, S., Bell, A.S., Blanford, J.I., Read, A.F., Thomas, M.B.: Influence of climate on malaria transmission depends on daily temperature variation. Proceedings of the National Academy of Sciences 107(34), 15,135–15,139 (2010). https://doi.org/10.1073/pnas.1006422107

    Article  Google Scholar 

  35. Payne, D., Flaherty, S.P., Barry, M.F., Matthews, C.D.: Preliminary observations on polar body extrusion and pronuclear formation in human oocytes using time-lapse video cinematography. Human Reproduction 12(3), 532–541 (1997)

    Article  Google Scholar 

  36. Pesnell, W.D., Thompson, B., Chamberlin, P.: The solar dynamics observatory (sdo). In: The Solar Dynamics Observatory, pp. 3–15. Springer (2011)

    Google Scholar 

  37. Pillai, K.G., Angryk, R.A., Aydin, B.: A filter-and-refine approach to mine spatiotemporal co-occurrences. In: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2013, Orlando, FL, USA, November 5–8, 2013, pp. 104–113 (2013)

    Google Scholar 

  38. Quercia, D., Lathia, N., Calabrese, F., Lorenzo, G.D., Crowcroft, J.: Recommending social events from mobile phone location data. In: ICDM, pp. 971–976 (2010)

    Google Scholar 

  39. Roddick, J.F., Lees, B.G.: Spatiotemporal data mining paradigms and methodologies. In: H. Miller, J. Han (eds.) Geographic Data Mining and Knowledge. CRC Press (2001)

    Google Scholar 

  40. Rogers, D.J., Randolph, S.E., Snow, R.W., Hay, S.I.: Satellite imagery in the study and forecast of malaria. Nature 415(6872), 710–715 (2002)

    Article  Google Scholar 

  41. Romanowicz, B.: Spatiotemporal patterns in the energy release of great earthquakes. Science 260(5116), 1923–1926 (1993)

    Article  Google Scholar 

  42. Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., Nickless, D.: Terra and aqua modis products available from nasa ges daac. Advances in Space Research 34(4), 710–714 (2004)

    Article  Google Scholar 

  43. Schuh, M.A., Angryk, R.A., Pillai, K.G., Banda, J.M., Martens, P.C.: A large-scale solar image dataset with labeled event regions. In: IEEE International Conference on Image Processing, ICIP 2013, Melbourne, Australia, September 15–18, 2013, pp. 4349–4353 (2013)

    Google Scholar 

  44. Shekhar, S., Jiang, Z., Ali, R.Y., Eftelioglu, E., Tang, X., Gunturi, V., Zhou, X.: Spatiotemporal data mining: A computational perspective. ISPRS International Journal of Geo-Information 4(4), 2306–2338 (2015)

    Article  Google Scholar 

  45. Shekhar, S., Xiong, H. (eds.): Encyclopedia of GIS. Springer (2008)

    Google Scholar 

  46. Spaccapietra, S., Parent, C., Damiani, M.L., de Macêdo, J.A.F., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008). https://doi.org/10.1016/j.datak.2007.10.008

    Article  Google Scholar 

  47. Tango, T., Takahashi, K., Kohriyama, K.: A space–time scan statistic for detecting emerging outbreaks. Biometrics 67(1), 106–115 (2011)

    Article  MathSciNet  Google Scholar 

  48. Tiakas, E., Papadopoulos, A., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Searching for similar trajectories in spatial networks. Journal of Systems and Software 82(5), 772–788 (2009). https://doi.org/10.1016/j.jss.2008.11.832

    Article  Google Scholar 

  49. Tonnang, H.E.Z., Kangalawe, R.Y.M., Yanda, P.Z.: Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in africa. Malaria Journal 9, 111–120 (2010)

    Article  Google Scholar 

  50. Verhein, F., Chawla, S.: Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: Database Systems for Advanced Applications, 11th International Conference, DASFAA 2006, Singapore, April 12–15, 2006, Proceedings, pp. 187–201 (2006)

    Google Scholar 

  51. Walgraef, D.: Spatio-Temporal Pattern Formation: With Examples from Physics, Chemistry, and Materials Science. Springer Verlag (1997)

    Google Scholar 

  52. Walker, M.C., Murphy, K.E., Pan, S., Yang, Q., Wen, S.W.: Adverse maternal outcomes in multifetal pregnancies. BJOG: An International Journal of Obstetrics & Gynaecology 111(11), 1294–1296 (2004). https://doi.org/10.1111/j.1471-0528.2004.00345.x

    Article  Google Scholar 

  53. Wang, F., Minor, W.: Where the jobs are: Employment access and crime patterns in cleveland. Annals of the Association of American Geographers 92(3), 435–450 (2002)

    Article  Google Scholar 

  54. Wang, J., Young, K., Hock, T., Lauritsen, D., Behringer, D., Black, M., Black, P.G., Franklin, J., Halverson, J., Molinari, J., et al.: A long-term, high-quality, high-vertical-resolution gps dropsonde dataset for hurricane and other studies. Bulletin of the American Meteorological Society 96(6), 961–973 (2015)

    Article  Google Scholar 

  55. Wiegand, T., Moloney, K., Milton, S.: Population dynamics, disturbance, and pattern evolution: Identifying the fundamental scales of organization in a model ecosystem. The American Naturalist 152(3), 321–337 (1998)

    Article  Google Scholar 

  56. Wong, C.C., Loewke, K.E., Bossert, N.L., Behr, B., De Jonge, C.J., Baer, T.M., Pera, R.A.R.: Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nature Biotechnology 28(10), 1115–1121 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Berkay Aydin & Rafal A. Angryk

Authors
  1. Berkay Aydin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafal A. Angryk
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Aydin, B., Angryk, R.A. (2018). A Gentle Introduction to Spatiotemporal Data Mining. In: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-99873-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99873-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99872-5

  • Online ISBN: 978-3-319-99873-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation