IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v41y2014i4p613-636.html
   My bibliography  Save this article

Multiscale Spatial Decomposition for Skew-Distributed Data with Parallel Spatial Kernel Smoothing

Author

Listed:
  • Linwang Yuan
  • Zhaoyuan Yu
  • Lin Yi
  • Wen Luo
  • Shaofei Chen

    (Program in Geospatial Science, The University of Texas at Dallas, ROC 21, West Campbell Road, Richardson, TX 75080-3021, USA)

Abstract

A data-adaptive multiscale spatial decomposition model is proposed to deal with skew-distributed data (eg, population or GDP). Relying on the filtering characteristics according to the bandwidth change of kernel smoothing, the parallel spatial kernel smoothing with different bandwidths is constructed as spatial filter banks for filtering spatial variations at different spatial scales. The filtering residual, a function changing with the spatial scale, is then extracted by parallel spatial kernel smoothing. With a change point detection model based on the second deviation, standard deviation of the residential data is selected for identifying robust significant scales. Then we present the iterative algorithm to extract and remove significant spatial variations at different scales. With well-designed stop criteria, the full hierarchical spatial scale structure in the original spatial process can be adaptively established without assigning the decomposition levels artificially. The computation processes and the statistical and spatial distribution characteristics are demonstrated with case studies of 2003 Chinese population data and GDP data, and the results show that the proposed model is suitable for decomposing the spatial data with spatial heterogeneity. Comparison with the 2D wavelet decomposition suggests that our model has better data-adaptive and shape-preserving ability.

Suggested Citation

  • Linwang Yuan & Zhaoyuan Yu & Lin Yi & Wen Luo & Shaofei Chen, 2014. "Multiscale Spatial Decomposition for Skew-Distributed Data with Parallel Spatial Kernel Smoothing," Environment and Planning B, , vol. 41(4), pages 613-636, August.
    • Handle: RePEc:sae:envirb:v:41:y:2014:i:4:p:613-636
    DOI: 10.1068/b38225
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b38225
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b38225?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. Hallin, Marc & Lu, Zudi & Tran, Lanh T., 2004. "Kernel density estimation for spatial processes: the L1 theory," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 61-75, January.
    2. Hopkins, Robert L. & Burr, Brooks M., 2009. "Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics," Ecological Modelling, Elsevier, vol. 220(17), pages 2024-2034.
    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. Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
    2. Tang Qingguo, 2015. "Robust estimation for spatial semiparametric varying coefficient partially linear regression," Statistical Papers, Springer, vol. 56(4), pages 1137-1161, November.
    3. Gao, Jiti & Lu, Zudi & Tjostheim, Dag, 2003. "Estimation in semiparametric spatial regression," MPRA Paper 11979, University Library of Munich, Germany, revised Jul 2005.
    4. Sacchelli, Sandro & De Meo, Isabella & Paletto, Alessandro, 2013. "Bioenergy production and forest multifunctionality: A trade-off analysis using multiscale GIS model in a case study in Italy," Applied Energy, Elsevier, vol. 104(C), pages 10-20.
    5. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    6. Kuangyu Wen & Ximing Wu & David J. Leatham, 2021. "Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 349-366, September.
    7. Jenish, Nazgul, 2012. "Nonparametric spatial regression under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 167(1), pages 224-239.
    8. Zhang, Rongmao & Chan, Ngai Hang & Chi, Changxiong, 2023. "Nonparametric testing for the specification of spatial trend functions," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    9. Mohamed El Machkouri, 2011. "Asymptotic normality of the Parzen–Rosenblatt density estimator for strongly mixing random fields," Statistical Inference for Stochastic Processes, Springer, vol. 14(1), pages 73-84, February.
    10. Pletterbauer, Florian & Graf, Wolfram & Schmutz, Stefan, 2016. "Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis," Ecological Modelling, Elsevier, vol. 327(C), pages 95-104.
    11. Amiri, Aboubacar & Dabo-Niang, Sophie, 2018. "Density estimation over spatio-temporal data streams," Econometrics and Statistics, Elsevier, vol. 5(C), pages 148-170.
    12. Sophie Dabo-Niang & Anne-Françoise Yao, 2013. "Kernel spatial density estimation in infinite dimension space," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 19-52, January.
    13. Muñoz-Mas, R. & Martínez-Capel, F. & Alcaraz-Hernández, J.D. & Mouton, A.M., 2015. "Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?," Ecological Modelling, Elsevier, vol. 309, pages 72-81.
    14. Chang, Yoosoon & Kaufmann, Robert K. & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2020. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Journal of Econometrics, Elsevier, vol. 214(1), pages 274-294.
    15. Rongrong Xu & Jinde Wang, 2008. "-estimation for spatial nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(6), pages 523-537.
    16. Liliana Forzani & Ricardo Fraiman & Pamela Llop, 2013. "Density estimation for spatial-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 321-342, June.
    17. Zhengyan Lin & Degui Li & Jiti Gao, 2009. "Local Linear M‐estimation in non‐parametric spatial regression," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 286-314, May.
    18. Zhenyu Jiang & Nengxiang Ling & Zudi Lu & Dag Tj⊘stheim & Qiang Zhang, 2020. "On bandwidth choice for spatial data density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 817-840, July.
    19. Robinson, P.M., 2011. "Asymptotic theory for nonparametric regression with spatial data," Journal of Econometrics, Elsevier, vol. 165(1), pages 5-19.
    20. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.

    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:sae:envirb:v:41:y:2014:i:4:p:613-636. 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: SAGE Publications (email available below). General contact details of provider: .

    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.