Abstract
The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-k spatial outliers over several time periods. The top-k spatial outliers are found using the Exact-Grid Top- k and Approx-Grid Top- k algorithms, which are an extension of algorithms developed by Agarwal et al. [1]. Since they use the Kulldorff spatial scan statistic, they are capable of discovering all outliers, unaffected by neighbouring regions that may contain missing values. After generating the outlier sequences, we show one way they can be interpreted, by comparing them to the phases of the El Niño Southern Oscilliation (ENSO) weather phenomenon to provide a meaningful analysis of the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agarwal, D., Phillips, J.M., Venkatasubramanian, S.: The Hunting of the Bump: On Maximizing Statistical Discrepancy. In: Proceedings 17th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1137–1146. ACM Press, New York (2006)
Han, J., Altman, R.B., Kumar, V., Mannila, H., Pregibon, D.: Emerging Scientific Applications in Data Mining. Communications of the ACM 45(8), 54–58 (2002)
Miller, H.J.: Geographic Data Mining and Knowledge Discovery. In: Wilson, Fotheringham, A.S. (eds.) Handbook of Geographic Information Science (2007)
Miller, H.J., Han, J.: Geographic Data Mining and Knowledge Discovery: An Overview In Geographic Data Mining and Knowledge Discovery. Taylor & Francis, New York (2001)
Openshaw, S.: Geographical Data Mining: Key Design Issues. In: Proceedings of GeoComputation 1999 (1999)
Liebmann, B., Allured, D.: Daily precipitation grids for South America. Bulletin of the American Meteorological Society 86, 1567–1570 (2005)
DAleo, J.S., Grube, P.G.: The Oryx Resource Guide to El Niño and La Niña. Oryx Press, CT (2002)
National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center: Monthly Atmospheric & SST Indices, http://www.cpc.noaa.gov/data/indices (Accessed February 2, 2008)
Wu, E., Chawla, S.: Spatio-Temporal Analysis of the relationship between South American Precipitation Extremes and the El Niño Southern Oscillation. In: Proceedings of the 2007 International Workshop on Spatial and Spatio-temporal Data Mining. IEEE Computer Society, Washington (2007)
Agarwal, D., McGregor, A., Phillips, J.M., Venkatasubramanian, S., Zhu, Z.: Spatial Scan Statistics: Approximations and Performance Study. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 24–33. ACM Press, New York (2006)
Cheng, T., Li, Z.: A Multiscale Approach for Spatio-Temporal Outlier Detection. Transactions in GIS 10(2), 253–263 (2006)
Birant, D., Kut, A.: Spatio-temporal outlier detection in large databases. In: 28th International Conference on Information Technology Interfaces, pp. 179–184 (2006)
Ng, R.: Detecting Outliers from Large Datasets In Geographic Data Mining and Knowledge Discovery, pp. 218–235. Taylor & Francis, New York (2001)
Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the Generation of Spatiotemporal Datasets. In: Proceedings of the 6th International Symposium on Advances in Spatial Databases, pp. 147–164. Springer, London (1999)
Kulldorff, M.: A Spatial Scan Statistic. Communications in Statistics - Theory and Methods 26, 1481–1496 (1997)
Iyengar, V.S.: On Detecting Space-Time Clusters. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge Discovery and Data mining, pp. 587–592. ACM, New York (2004)
Chawla, S., Shekhar, S., Wu, W., Ozesmi, U.: Modelling Spatial Dependencies for Mining Geospatial Data: An Introduction. In: Geographic Data Mining and Knowledge Discovery, pp. 131–159. Taylor & Francis, New York (2001)
Tobler, W.R.: A computer model simulation of urban growth in the Detroit region. Economic Geography 46(2), 234–240 (1970)
Redmond, K.: Classification of El Niño and La Niña Winters, http://www.wrcc.dri.edu/enso/ensodef.html (Accessed October 24, 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, E., Liu, W., Chawla, S. (2010). Spatio-temporal Outlier Detection in Precipitation Data. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-12519-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12518-8
Online ISBN: 978-3-642-12519-5
eBook Packages: Computer ScienceComputer Science (R0)