IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v38y2011i11p2471-2483.html
   My bibliography  Save this article

A new space--time multivariate approach for environmental data analysis

Author

Listed:
  • Sandra De Iaco

Abstract

Air quality control usually requires a monitoring system of multiple indicators measured at various points in space and time. Hence, the use of space--time multivariate techniques are of fundamental importance in this context, where decisions and actions regarding environmental protection should be supported by studies based on either inter-variables relations and spatial--temporal correlations. This paper describes how canonical correlation analysis can be combined with space--time geostatistical methods for analysing two spatial--temporal correlated aspects, such as air pollution concentrations and meteorological conditions. Hourly averages of three pollutants (nitric oxide, nitrogen dioxide and ozone) and three atmospheric indicators (temperature, humidity and wind speed) taken for two critical months (February and August) at several monitoring stations are considered and space--time variograms for the variables are estimated. Simultaneous relationships between such sample space--time variograms are determined through canonical correlation analysis. The most correlated canonical variates are used for describing synthetically the underlying space--time behaviour of the components of the two sets.

Suggested Citation

  • Sandra De Iaco, 2011. "A new space--time multivariate approach for environmental data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2471-2483, January.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2471-2483
    DOI: 10.1080/02664763.2011.559206
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.559206
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.559206?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. De Iaco, S. & Myers, D. E. & Posa, D., 2002. "Space-time variograms and a functional form for total air pollution measurements," Computational Statistics & Data Analysis, Elsevier, vol. 41(2), pages 311-328, December.
    2. Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
    3. De Iaco, S. & Palma, M. & Posa, D., 2005. "Modeling and prediction of multivariate space-time random fields," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 525-547, March.
    Full references (including those not matched with items on IDEAS)

    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. Sandra De Iaco, 2010. "Space-time correlation analysis: a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 1027-1041.
    2. José-María Montero & Gema Fernández-Avilés & Tiziana Laureti, 2021. "A Local Spatial STIRPAT Model for Outdoor NO x Concentrations in the Community of Madrid, Spain," Mathematics, MDPI, vol. 9(6), pages 1-33, March.
    3. S. De Iaco & M. Palma & D. Posa, 2013. "Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 133-150, April.
    4. Montero, José-María, 2018. "Geostatistics: Unde venis et quo vadis? /Geoestadística:¿De dónde vienes y a dónde vas?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 81-106, Enero.
    5. Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
    6. Bardia Bayat & Mohsen Nasseri & Banafsheh Zahraie, 2015. "Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 515-541, March.
    7. De Iaco, S. & Palma, M. & Posa, D., 2005. "Modeling and prediction of multivariate space-time random fields," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 525-547, March.
    8. P. Gregori & E. Porcu & J. Mateu & Z. Sasvári, 2008. "On potentially negative space time covariances obtained as sum of products of marginal ones," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 865-882, December.
    9. José-María Montero & Coro Chasco & Beatriz Larraz, 2010. "Building an environmental quality index for a big city: a spatial interpolation approach combined with a distance indicator," Journal of Geographical Systems, Springer, vol. 12(4), pages 435-459, December.
    10. Bruno Scarpa, 2005. "Non parametric space-time modeling of SO2 in presence of many missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 67-82, February.
    11. Mehdi Omidi & Mohsen Mohammadzadeh, 2016. "A new method to build spatio-temporal covariance functions: analysis of ozone data," Statistical Papers, Springer, vol. 57(3), pages 689-703, September.
    12. Rui Jiang & Chunxue Liu & Xiaowei Liu & Shuai Zhang, 2022. "Space–Time Effect of Green Total Factor Productivity in Mineral Resources Industry in China: Based on Space–Time Semivariogram and SPVAR Model," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    13. Hongxia Wang & Jinde Wang & Bo Huang, 2012. "Prediction for spatio-temporal models with autoregression in errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 217-244.
    14. Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    15. Ying Zhang & Song Xi Chen & Le Bao, 2023. "Air pollution estimation under air stagnation—A case study of Beijing," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    16. Porcu, E. & Mateu, J. & Zini, A. & Pini, R., 2007. "Modelling spatio-temporal data: A new variogram and covariance structure proposal," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 83-89, January.
    17. Alexandre Rodrigues & Peter J. Diggle, 2010. "A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 553-567, December.
    18. Matt Higham & Michael Dumelle & Carly Hammond & Jay Hoef & Jeff Wells, 2024. "An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 491-515, September.
    19. De Iaco, S., 2023. "Spatio-temporal generalized complex covariance models based on convolution," Computational Statistics & Data Analysis, Elsevier, vol. 183(C).
    20. Firoozeh Rivaz & Mohsen Mohammadzadeh & Majid Jafari Khaledi, 2011. "Spatio-temporal modeling and prediction of CO concentrations in Tehran city," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1995-2007, November.

    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:taf:japsta:v:38:y:2011:i:11:p:2471-2483. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.