IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v062i06.html
   My bibliography  Save this article

D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables

Author

Listed:
  • Finazzi, Francesco
  • Fassò, Alessandro

Abstract

This paper discusses the software D-STEM as a statistical tool for the analysis and mapping of environmental space-time variables. The software is based on a flexible hierarchical space-time model which is able to deal with multiple variables, heterogeneous spatial supports, heterogeneous sampling networks and missing data. Model estimation is based on the expectation maximization algorithm and it can be performed using a distributed computing environment to reduce computing time when dealing with large data sets. The estimated model is eventually used to dynamically map the variables over the geographic region of interest. Three examples of increasing complexity illustrate usage and capabilities of D-STEM, both in terms of modeling and implementation, starting from a univariate model and arriving at a multivariate data fusion with tapering.

Suggested Citation

  • Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
  • Handle: RePEc:jss:jstsof:v:062:i06
    DOI: http://hdl.handle.net/10.18637/jss.v062.i06
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v062i06/v62i06.pdf
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v062i06/D-STEM_v4.7.11_Full.zip
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v062.i06?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tatiyana V. Apanasovich & Marc G. Genton, 2010. "Cross-covariance functions for multivariate random fields based on latent dimensions," Biometrika, Biometrika Trust, vol. 97(1), pages 15-30.
    2. 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.
    3. Francesco Finazzi & E. Marian Scott & Alessandro Fassò, 2013. "A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 287-308, March.
    4. Matthias Katzfuss & Noel Cressie, 2011. "Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets," Journal of Time Series Analysis, Wiley Blackwell, vol. 32, pages 430-446, July.
    5. Veronica J. Berrocal & Alan E. Gelfand & David M. Holland, 2012. "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality," Biometrics, The International Biometric Society, vol. 68(3), pages 837-848, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fassò, A. & Finazzi, F. & Madonna, F., 2018. "Statistical issues in radiosonde observation of atmospheric temperature and humidity profiles," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 97-100.
    2. Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
    3. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
    4. Francesco Finazzi, 2020. "Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 857-882, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    2. Isabelle Grenier & Bruno Sansó & Jessica L. Matthews, 2024. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
    3. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    4. Jingjie Zhang & Matthias Katzfuss, 2022. "Multi-Scale Vecchia Approximations of Gaussian Processes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 440-460, September.
    5. Zilber, Daniel & Katzfuss, Matthias, 2021. "Vecchia–Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    6. Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
    7. Karl Pazdernik & Ranjan Maitra & Douglas Nychka & Stephan Sain, 2018. "Reduced Basis Kriging for Big Spatial Fields," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 280-300, August.
    8. Roman Flury & Reinhard Furrer, 2021. "Discussion on Competition for Spatial Statistics for Large Datasets," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 599-603, December.
    9. Fassò, A. & Finazzi, F. & Madonna, F., 2018. "Statistical issues in radiosonde observation of atmospheric temperature and humidity profiles," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 97-100.
    10. Moreno Bevilacqua & Alfredo Alegria & Daira Velandia & Emilio Porcu, 2016. "Composite Likelihood Inference for Multivariate Gaussian Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 448-469, September.
    11. Ranadeep Daw & Christopher K. Wikle, 2023. "REDS: Random ensemble deep spatial prediction," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    12. Sun, Ying & Chang, Xiaohui & Guan, Yongtao, 2018. "Flexible and efficient estimating equations for variogram estimation," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 45-58.
    13. Caamaño-Carrillo, Christian & Bevilacqua, Moreno & López, Cristian & Morales-Oñate, Víctor, 2024. "Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    14. Guanzhou Wei & Xiao Liu & Russell Barton, 2024. "An extended PDE‐based statistical spatio‐temporal model that suppresses the Gibbs phenomenon," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    15. Yasumasa Matsuda, 2014. "Wavelet Analysis Of Spatio-Temporal Data," TERG Discussion Papers 311, Graduate School of Economics and Management, Tohoku University.
    16. Andrew Finley & Sudipto Banerjee & Alan Gelfand, 2012. "Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes," Journal of Geographical Systems, Springer, vol. 14(1), pages 29-47, January.
    17. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    18. Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
    19. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
    20. Zammit-Mangion, Andrew & Rougier, Jonathan, 2018. "A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 116-130.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:062:i06. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.