Skip to main content
Log in

Local polynomials for data detrending and interpolation in the presence of barriers

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

We discuss features of local polynomial interpolation (LPI), focusing on the problem with unstable solutions of the LPI system of linear equations. We develop a new diagnostic based on condition number values. Also, a variant of Tikhonov regularization is proposed, which allows the production of continuous predictions and prediction standard errors nearly everywhere in the data domain. This variant of LPI can be used in the presence of barriers defined by polylines. LPI model is a good candidate for real time automatic mapping of the data regularly collected from the environmental monitoring networks. We illustrate the LPI usage with both simulated data and real data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Belsley DA (1991) Conditioning diagnostics: collinearity and weak data in regression. Wiley-Interscience, New York

    Google Scholar 

  • Cressie N (1998) Fundamentals of spatial statistics. In: Muller WG (ed) Collecting spatial data: optimum design of experiments for random fields. Physica-Verlag, Heidelberg, pp 9–33

    Google Scholar 

  • EUR 21595 EN (2005) Automatic mapping algorithms for routine and emergency monitoring data. In: Dubois G (ed) Report on the spatial interpolation comparison (SIC2004) exercise. European Commission, Office for Official Publications, Luxembourg

  • Gandin LS (1959) The problem of optimal interpolation. Trudy GGO 99:67–75 (in Russian)

    Google Scholar 

  • Gandin LS (1963) Objective analysis of meteorological fields (trans: Israel program for scientific translations, Jerusalem, 1965). Gidrometeorologicheskoe Izdatel’stvo (GIMIZ), Leningrad

  • Gilchrist B, Cressman GP (1954) An experiment in objective analysis. Tellus 6(4):309–318

    Article  Google Scholar 

  • Gribov A, Krivoruchko K (2004) Geostatistical mapping with continuous moving neighborhood. Math Geol 36(2):267–281

    Article  Google Scholar 

  • Krivoruchko K (2011) Spatial statistical analysis for GIS users. Esri Press, Redlands, CA

    Google Scholar 

  • Medak D, Pribicevic B, Krivoruchko K (2008) Geostatistical analysis of bathymetric measurements: Lake Kozjak case study. Geodetski list 3:1–18

    Google Scholar 

  • Tikhonov AN (1943) On the stability of inverse problems. Doklady Akademii Nauk SSSR 39(5):195–198 (in Russian)

    Google Scholar 

  • Waller LA, Zhu L, Gotway CA, Gorman DM, Gruenewald PJ (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stoch Env Res Risk Assess 21(5):573–588

    Article  Google Scholar 

  • Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7(2):161–187

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Konstantin Krivoruchko.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gribov, A., Krivoruchko, K. Local polynomials for data detrending and interpolation in the presence of barriers. Stoch Environ Res Risk Assess 25, 1057–1063 (2011). https://doi.org/10.1007/s00477-011-0488-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-011-0488-2

Keywords

Navigation