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Spatial data compression via adaptive dispersion clustering

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  • Marchetti, Yuliya
  • Nguyen, Hai
  • Braverman, Amy
  • Cressie, Noel

Abstract

Adaptive Spatial Dispersion Clustering (ASDC), a new method of spatial data compression, is specifically designed to reduce the size of a spatial dataset in order to facilitate subsequent spatial prediction. Unlike traditional data and image compression methods, the goal of ASDC is to create a new dataset that will be used as input into spatial-prediction methods, such as traditional kriging or Fixed Rank Kriging, where using the full dataset may be computationally infeasible. ASDC can be classified as a lossy compression method and is based on spectral clustering. It aims to produce contiguous spatial clusters and to preserve the spatial-correlation structure of the data so that the loss of predictive information is minimal. An extensive simulation study demonstrates the predictive performance of these adaptively compressed datasets for several scenarios. ASDC is compared to two other data-reduction schemes, one using local neighborhoods and one using simple binning. An application to remotely sensed sea-surface-temperature data is also presented, and computational costs are discussed.

Suggested Citation

  • Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
    • Handle: RePEc:eee:csdana:v:117:y:2018:i:c:p:138-153
    DOI: 10.1016/j.csda.2017.08.004
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    1. Gilles Guillot & Denis Kan‐King‐Yu & Joël Michelin & Philippe Huet, 2006. "Inference of a hidden spatial tessellation from multivariate data: application to the delineation of homogeneous regions in an agricultural field," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(3), pages 407-430, May.
    2. Birch, Colin P.D. & Oom, Sander P. & Beecham, Jonathan A., 2007. "Rectangular and hexagonal grids used for observation, experiment and simulation in ecology," Ecological Modelling, Elsevier, vol. 206(3), pages 347-359.
    3. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    4. Hu, Tianming & Sung, Sam Yuan, 2006. "A hybrid EM approach to spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1188-1205, March.
    5. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
    6. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    7. Hai Nguyen & Noel Cressie & Amy Braverman, 2012. "Spatial Statistical Data Fusion for Remote Sensing Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1004-1018, September.
    8. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    9. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    10. Luke Bornn & Gavin Shaddick & James V. Zidek, 2012. "Modeling Nonstationary Processes Through Dimension Expansion," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 281-289, March.
    11. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518, July.
    12. Huiyan Sang & Jianhua Z. Huang, 2012. "A full scale approximation of covariance functions for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 111-132, January.
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