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Data Mining Techniques for the Characterization of Dynamic Regions in Spatiotemporal Data

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Encyclopedia of GIS

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Synonyms

Spatiotemporal Change Detection; Spatiotemporal Data Mining; Spatiotemporal Dynamics;

Definition

Spatiotemporal sensor data is prevalent in many domains and, at its most basic level, consists of a sensor location defined by coordinates in two-dimensional or three-dimensional space and an attribute or set of attributes being measured at that location. In the context of this entry, sensors are typically represented in the form of a point location where the objective is to measure a spatial process that is moving over time. For example, a precipitation gage or a pixel in a satellite image could be modeled as a stationary sensor. Moving sensors such as depth sensors on boats or a drifting temperature probe in the ocean also attempt to measure a moving phenomenon except the sensors are also moving in space. The resulting dataset includes a set of spatial coordinates representing either a sensor or the center of a grid cell, a time stamp, and the attributes being measured at that...

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References

  • Agarwal D, McGregor A, Phillips J, Venkatasubramanian S, Zhu Z (2006) Spatial scan statistics: approximations and performance study. In: Conference on knowledge discovery in data: proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia, Pennsylvania, Citeseer, vol 20, pp 24–33

    Google Scholar 

  • Birant D, Kut A (2006) Spatio-temporal outlier detection in large databases. J Comput Inf Technol 14(4):291

    Article  Google Scholar 

  • Birant D, Kut A (2007) St-dbscan: an algorithm for clustering spatial–temporal data. Data Knowl Eng 60(1):208–221

    Article  Google Scholar 

  • Breunig MM, Kriegel HP, Ng RT, Sander J (1999) Optics-of: identifying local outliers. In: Principles of data mining and knowledge discovery, Prague, Czech Republic, pp 262–270

    Chapter  Google Scholar 

  • Chan J, Bailey J, Leckie C (2008) Discovering correlated spatio-temporal changes in evolving graphs. Knowl Inf Syst 16(1):53–96

    Article  Google Scholar 

  • Changtien L, Dechang C, Yufeng K (2003) Algorithms for spatial outlier detection. In: Proceedings of 3rd IEEE international conference on data mining, Los Alamitos. IEEE Computer Society Press, pp 597–600

    Google Scholar 

  • Cheng T, Li Z (2006) A multiscale approach for spatio-temporal outlier detection. Trans GIS 10(2):253–263

    Article  Google Scholar 

  • Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, Hoboken, New Jersey

    MATH  Google Scholar 

  • Das M, Parthasarathy S (2009) Anomaly detection and spatio-temporal analysis of global climate system. In: SensorKDD ’09: proceedings of the third international workshop on knowledge discovery from sensor data, New York, New York pp 142–150

    Google Scholar 

  • Günnemann S, Kremer H, Laufkötter C, Seidl T (2012) Tracing evolving subspace clusters in temporal climate data. Data Min Knowl Discov 24(2):387–410

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Janeja V, Atluri V (2008) Random walks to identify anomalous free-form spatial scan windows. IEEE Trans Knowl Data Eng 20(10):1378–1392

    Article  Google Scholar 

  • Knorr EM, Ng RT, Tucakov V (2000) Distance-based outliers: algorithms and applications. VLDB J Int J Very Large Data Bases 8(3–4):237–253

    Article  Google Scholar 

  • Kou Y, Lu C, Santos R (2007) Spatial outlier detection: a graph-based approach. In: 2007 ICTAI 2007 19th IEEE international conference on tools with artificial intelligence, Patras, Greece, vol 1

    Google Scholar 

  • Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496

    Article  MathSciNet  MATH  Google Scholar 

  • Lin Y, Mitchell KE (2005) The NCEP stage II/IV hourly precipitation analyses: development and applications. In: 19th conference on hydrology. American Meteorological Society, San Diego, 9–13 Jan 2005

    Google Scholar 

  • Lin F, Xie K, Song G, Wu T (2009) A novel spatio-temporal clustering approach by process similarity. In: 2009 FSKD ’09 sixth international conference on Fuzzy systems and knowledge discovery, Tainjin, China, vol 5, pp 150–154

    Google Scholar 

  • McGuire M, Janeja V, Gangopadhyay A (2011) Characterizing sensor datasets with multi-granular spatio-temporal intervals. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems (GIS ’11). ACM, New York

    Google Scholar 

  • McGuire MP, Janeja VP, Gangopadhyay A (2014) Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets. Data Min Knowl Discov 28(4): 961–1003

    Article  MathSciNet  Google Scholar 

  • NOAA (2000) Tropical atmosphere ocean project. http://www.pmel.noaa.gov/tao/jsdisplay/

  • Oliveira M, Gama J (2010) Bipartite graphs for monitoring clusters transitions. In: Advances in intelligent data analysis IX, Tuscon, Arizona, pp 114–124

    Chapter  Google Scholar 

  • Reljin I, Reljin DB, Jovanović G (2003) Clustering and mapping spatial-temporal datasets using som neural networks. J Autom Control 13(1):55–60

    Article  MATH  Google Scholar 

  • Rosswog J, Ghose K (2008) Detecting and tracking spatio-temporal clusters with adaptive history filtering. In: 2008 ICDMW ’08 IEEE international conference on data mining workshops, Pisa, Italy, pp 448–457

    Chapter  Google Scholar 

  • Sap MNM, Awan A (2005) Finding spatio-temporal patterns in climate data using clustering. In: 2005 international conference on cyberworlds, pp 8–164. doi:10.1109/CW.2005.45

    Google Scholar 

  • Sedaghat L, Hersey J, McGuire MP (2013) Detecting spatio-temporal outliers in crowdsourced bathymetry data. In: Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information. ACM, New York, pp 55–62

    Google Scholar 

  • Shekhar S, Lu C, Zhang P (2003) A unified approach to detecting spatial outliers. GeoInformatica 7(2): 139–166

    Article  Google Scholar 

  • Spiliopoulou M, Ntoutsi I, Theodoridis Y, Schult R (2006) Monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 706–711

    Chapter  Google Scholar 

  • Steinhaeuser K, Ganguly A, Chawla N (2012) Multivariate and multiscale dependence in the global climate system revealed through complex networks. Clim Dyn 39(3-4): 889–895

    Article  Google Scholar 

  • Sun P, Chawla S (2004) On local spatial outliers. In: 2004 ICDM ’04 Fourth IEEE international conference on data mining, pp 209–216. doi:10.1109/ICDM.2004.10097

    Google Scholar 

  • Von Storch H, Zwiers F (2002) Statistical analysis in climate research. Cambridge University Press, Cambridge

    Google Scholar 

  • Worboys M, Duckham M (2006) Monitoring qualitative spatiotemporal change for geosensor networks. Int J Geogr Inf Sci 20(10):1087–1108

    Article  Google Scholar 

  • Wu E, Liu W, Chawla S (2010) Spatio-temporal outlier detection in precipitation data. In: Knowledge discovery from sensor data. CRC Press, Boca Raton, Florida, pp 115–133

    Chapter  Google Scholar 

  • Yang H, Parthasarathy S, Mehta S (2005) A generalized framework for mining spatio-temporal patterns in scientific data. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, New York, pp 716–721

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Towson University, 21252, Towson, MD, USA

    Michael P. McGuire

Authors
  1. Michael P. McGuire
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Corresponding author

Correspondence to Michael P. McGuire .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Shashi Shekhar

  2. Management Science and Information Systems Department, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA

    Hui Xiong

  3. Department of Management Sciences, Tippie College of Business, University of Iowa, Iowa City, IA, USA

    Xun Zhou

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McGuire, M.P. (2017). Data Mining Techniques for the Characterization of Dynamic Regions in Spatiotemporal Data. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1543

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