Spatial interpolation via GWR, a plausible alternative?

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Spatial interpolation can be done through either univariate methods that rely solely on the spatial structure of the data or by combining the spatial information and attribute information. Geographically weighted regression, although is used primarily in modeling the spatially varying relationships, falls within the category of combining both spatial and attribute information to interpolate unknown values. Using both artificially generated data with predefined parameters and actual house data from the City of Milwaukee, this study evaluates the interpolation accuracy of the univariate interpolation method represented by ordinary Kriging and multivariate interpolation represented by regression Kriging and GWR interpolation. It is found that by including relevant auxiliary variable(s), RK and GWR interpolations yield more accurate results than the univariate interpolation method, though the subtlety of how the spatial structure is assumed produces slight difference between RK and GWR. This study suggests GWR can serve as a useful alternative interpolation method in data analysis in addition to providing more detailed understanding of the spatially varying relationships between target and auxiliary variables.

Original languageEnglish
Title of host publication2009 17th International Conference on Geoinformatics, Geoinformatics 2009
DOIs
StatePublished - 1 Dec 2009
Event2009 17th International Conference on Geoinformatics, Geoinformatics 2009 - Fairfax, VA, United States
Duration: 12 Aug 200914 Aug 2009

Other

Other2009 17th International Conference on Geoinformatics, Geoinformatics 2009
CountryUnited States
CityFairfax, VA
Period12/08/0914/08/09

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Interpolation

Keywords

  • City of Milwaukee
  • Geographically weighted regression
  • Ordinary Kriging
  • Regression Kriging
  • Spatial interpolation

Cite this

Yu, D. (2009). Spatial interpolation via GWR, a plausible alternative? In 2009 17th International Conference on Geoinformatics, Geoinformatics 2009 [5293526] https://doi.org/10.1109/GEOINFORMATICS.2009.5293526
Yu, Danlin. / Spatial interpolation via GWR, a plausible alternative?. 2009 17th International Conference on Geoinformatics, Geoinformatics 2009. 2009.
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Yu, D 2009, Spatial interpolation via GWR, a plausible alternative? in 2009 17th International Conference on Geoinformatics, Geoinformatics 2009., 5293526, 2009 17th International Conference on Geoinformatics, Geoinformatics 2009, Fairfax, VA, United States, 12/08/09. https://doi.org/10.1109/GEOINFORMATICS.2009.5293526

Spatial interpolation via GWR, a plausible alternative? / Yu, Danlin.

2009 17th International Conference on Geoinformatics, Geoinformatics 2009. 2009. 5293526.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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N2 - Spatial interpolation can be done through either univariate methods that rely solely on the spatial structure of the data or by combining the spatial information and attribute information. Geographically weighted regression, although is used primarily in modeling the spatially varying relationships, falls within the category of combining both spatial and attribute information to interpolate unknown values. Using both artificially generated data with predefined parameters and actual house data from the City of Milwaukee, this study evaluates the interpolation accuracy of the univariate interpolation method represented by ordinary Kriging and multivariate interpolation represented by regression Kriging and GWR interpolation. It is found that by including relevant auxiliary variable(s), RK and GWR interpolations yield more accurate results than the univariate interpolation method, though the subtlety of how the spatial structure is assumed produces slight difference between RK and GWR. This study suggests GWR can serve as a useful alternative interpolation method in data analysis in addition to providing more detailed understanding of the spatially varying relationships between target and auxiliary variables.

AB - Spatial interpolation can be done through either univariate methods that rely solely on the spatial structure of the data or by combining the spatial information and attribute information. Geographically weighted regression, although is used primarily in modeling the spatially varying relationships, falls within the category of combining both spatial and attribute information to interpolate unknown values. Using both artificially generated data with predefined parameters and actual house data from the City of Milwaukee, this study evaluates the interpolation accuracy of the univariate interpolation method represented by ordinary Kriging and multivariate interpolation represented by regression Kriging and GWR interpolation. It is found that by including relevant auxiliary variable(s), RK and GWR interpolations yield more accurate results than the univariate interpolation method, though the subtlety of how the spatial structure is assumed produces slight difference between RK and GWR. This study suggests GWR can serve as a useful alternative interpolation method in data analysis in addition to providing more detailed understanding of the spatially varying relationships between target and auxiliary variables.

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Yu D. Spatial interpolation via GWR, a plausible alternative? In 2009 17th International Conference on Geoinformatics, Geoinformatics 2009. 2009. 5293526 https://doi.org/10.1109/GEOINFORMATICS.2009.5293526