Support Vector Machines for Spatiotemporal Analysis in Geosensor Networks

  • Jon Devine
  • Anthony Stefanidis
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Geosensor networks are a growing source of spatiotemporal data. However, the raw data generated by these networks, as simple collections of readings from point locations, allow little analysis to be conducted directly. As such, this research presents support vector machine based methods for the extraction of estimates for the spatial extent of areal events from geosensor data and demonstrates how these results can serve as a basis for spatiotemporal analysis. Support vector machines are a recently developed class of machine learning algorithms that have seen considerable application due to their attractive generalization properties and ability to efficiently handle large datasets. While traditionally applied to classification problems, this research demonstrates how these methods can be applied to geosensor applications where decision boundaries can be interpreted as representations of the boundaries of the spatial extent of events. Once derived, these estimates are shown as capable of serving as input for existing methods for spatiotemporal analysis and enabling description of the evolution of spatiotemporal phenomena in terms of movement and deformation. As coverage of geosensor networks increases, with sensors becoming smaller and cheaper, applications of the techniques described in this research are foreseen in environmental science, public health, and security informatics.


Geosensor networks spatiotemporal analysis spatiotemporal helix support vector machines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abe S (2005) Support Vector Machines for Pattern Classification, SpringerGoogle Scholar
  2. Agouris P, Stefanidis A (2003) Efficient Summarization of Spatiotemporal Events. Communications of the ACM 46: 65-66CrossRefGoogle Scholar
  3. Agouris P, Stefanidis A, Gyftakis S (2001) Differential Snakes for Change Detection in Road Segments. Photogrammetric Engineering & Remote Sensing 67: 1391-1399Google Scholar
  4. Aizermann M, Braverman E, Rozonoer L (1964) Theoretical Foundations of the Potential Function Method in Pattern Recognition. Automation and Remote Control 25: 821-837Google Scholar
  5. Boser BE, Guyon IM, Vapnik V (1992) A Training Algorithm for Optimal Margin Classifiers. In: Haussler, D. (Ed.) 5th Annual ACM Workshop on Computational Learning Theory. ACM PressGoogle Scholar
  6. Burges CJC (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2: 121-167CrossRefGoogle Scholar
  7. Chen Y, Chuah C, Zhao Q (2008) Network Configuration for Optimal Utilization Efficiency of Wireless Sensor Networks. Ad Hoc Networks 6: 92-107CrossRefGoogle Scholar
  8. Chintalapudi KK, Govindan R (2003) Localized Edge Detection in Sensor Fields. Ad Hoc Networks 1: 273-291CrossRefGoogle Scholar
  9. Christianini N, Shawe-Taylor J (2000) An Introduction to Support Vector Machines. Cambridge University PressGoogle Scholar
  10. Croitoru A, Agouris P, Stefanidis A (2005) Rotation, Translation, and Scale Invariant 3D Trajectory Matching by Pose Normalization. In: Shahabi, C. & Boucelma, O. (Eds.) ACM-GIS’05. ACM Press, BremenGoogle Scholar
  11. Devroye L, Gyorfi L, Lugosi G (1996) A Probabilistic Theory of Pattern Recognition, SpringerGoogle Scholar
  12. Duckham M, Nittel S, Worboys M (2005) Monitoring Dynamic Spatial Fields Using Responsive Geosensor Networks. ACM International Workshop on Geographic Information Systems. ACM Press, BremenGoogle Scholar
  13. Durbha SS, King RL, Youman NH (2007) Support Vector Machines Regression for Retrieval of Leaf Area Index from Multirange Imaging Spectroradiometer. Remote Sensing of Environment 107: 348-361CrossRefGoogle Scholar
  14. Erwig, M., Gueting, R. H., Schneider, M. & Vazirgiannis, M. (1999) Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases. Geoinformatica 3: 143-148CrossRefGoogle Scholar
  15. Fisher, R. (1952) Contributions to Mathematical Statistics. Wiley, New YorkGoogle Scholar
  16. Ganesan, D., Estrain, D. & Heidermann, J. (2003) Dimensions: Why do we Need a New Data Handling Architecture for Sensor Networks? ACM SIGCOMM Computer Communication Review 33: 143-148CrossRefGoogle Scholar
  17. Karl, H. & Willig, A. (2005) Protocals and Architectures for Wireless Sensor Networks. Wiley, West Sussex, EnglandGoogle Scholar
  18. Melgani, F. & Bruzzone, L. (2004) Classification of Hyperspectral Remote Sensing Images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing 42: 1778-1790CrossRefGoogle Scholar
  19. Nittel, S., Duckham, M. & Kulik, L. (2003) Geographic Information Science. In: Egenhofer, M. & Mark, D. M. (Eds.) Second International Conference, GIScience 2003. SpringerGoogle Scholar
  20. Novikoff, A. B. (1962) On Convergence Proofs on Perceptrons. Symposium on the Mathematical Theory of Automata. Polytechnic Institute of BrooklynGoogle Scholar
  21. Nowak, R. & Mitra, U. (2003) Boundary Estimation in Sensor Networks: Theory and Methods. In: Guibas, L. & Zhao, F. (Eds.) Second International Workshop on Information Processing in Sensor Networks. Springer, Palo AltoGoogle Scholar
  22. Nowak, R., Mitra, U. & Willet, R. (2004) Estimating Inhomogenous Fields Using Sensor Networks. IEEE Journal on Selected Areas in Communications 22: 999-1007CrossRefGoogle Scholar
  23. Partsinevelos, P., Stefanidis, A. & Agouris, P. (2001) Automated Spatiotemporal Scaling for Video Generalization. IEEE International Conference on Image Processing. Thessaloniki, GreeceGoogle Scholar
  24. Rosenblatt, F. (1956) The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review 65: 386-408CrossRefGoogle Scholar
  25. Scholhopf, B. & Smola, A. (2002) Learning with Kernels, MIT Press, Cambridge, MAGoogle Scholar
  26. Stefanidis, A., Eickhorst, K., Agouris, P. & Partsinevelos, P. (2003) Modeling and Comparing Change Using Spatiotemporal Helixes. In: Hoel, E. & Rigaux, P. (Eds.) ACM-GIS’03. ACM Press, New OrleansGoogle Scholar
  27. Stefanidis, A. & Nittel, S. (2004) GeoSensor Networks CRC PressGoogle Scholar
  28. Vapnik, V. (1995) The Nature of Statistical Learning Theory. Wiley, New YorkGoogle Scholar
  29. Worboys, M. & Duckham, M. (2006) Monitoring Qualitative Spatiotemporal Change for Geosensor Networks. International Journal of Geographic Information Science 20: 1087-1108CrossRefGoogle Scholar
  30. Yang, R., Tan, J. & Kafatos, M. (2006) A Pattern Selection Algorithm in Kernel PCA Applications. First International Conference on Software and Data Technologies. Setubal, PortugalGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jon Devine
    • 1
  • Anthony Stefanidis
    • 2
  1. 1.Dept. of Spatial Information Science and EngineeringUniversity of Maine
  2. 2.Dept. of Earth Systems and Geoinformation SciencesGeorge Mason UniversityUSA

Personalised recommendations