Local attribute-similarity weighting regression algorithm for interpolating soil property valuesLocal attribute-similarity weighting regression algorithm for interpolating soil property values
Keywords:
attribute similarity, geographically weighted regression, local regression, spatial interpolationAbstract
Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance (SD). However, considering that properties of the neighbors spatially close to the unmeasured point may not be similar, the estimation of properties at the unmeasured one may not be accurate. The present study proposed a local attribute-similarity weighted regression (LASWR) algorithm, which characterized the similarity among spatial points based on non-spatial attributes (NSA) better than on SD. The real soil datasets were used in the validation. Mean absolute error (MAE) and root mean square error (RMSE) were used to compare the performance of LASWR with inverse distance weighting (IDW), ordinary kriging (OK) and geographically weighted regression (GWR). Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR. The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable. Keywords: attribute similarity, geographically weighted regression, local regression, spatial interpolation DOI: 10.25165/j.ijabe.20171005.2209 Citation: Zhou J G, Dong D M, Li Y Y. Local attribute-similarity weighting regression algorithm for interpolating soil property values. Int J Agric & Biol Eng, 2017; 10(5): 95–103.References
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[2] McBratney A B, Odeh I O A, Bishop T F A, Dunbara M S, Shatar T M. An overview of pedometric techniques for use in soil survey. Geoderma, 2000; 97(3-4): 293–327.
[3] Bostan P A, Heuvelink G B M. Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey. Int. J. Appl. Earth Obs. Geoinf, 2012; 19: 115–126.
[4] Ester M. Spatial data mining: databases primitives, algorithms and efficient DBMS support. Data Mining and Knowledge Discovery, 2000; 4: 193–216.
[5] Han J, Kamber M. Data mining: concepts and techniques, Academic Press, San Francisco, 2001.
[6] Fotheringham A S, Charlton M E. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ. Plann. A, 1998; 30: 1905–1927.
[7] Hengl T, Heuvelink G B M, Rossiter D. About regression-kriging: From equation to case studies. Comput. Geosci, 2007; 33: 1301–1315.
[8] Sun W, Minasny B, McBratney A B. Analysis and prediction of soil properties using local regression-Kriging.
Geoderma, 2012; 171-172: 16–23.
[9] Sun W, Whelan B M, Minasny B, McBratney A B. Evaluation of a local regression Kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution. Journal of Plant Nutrition and Soil Science, 2012; 175(2): 212–220.
[10] Kumar S, Lal R, Liu D S. A geographically weighted regression Kriging approach for mapping soil organic carbon stock. Geoderma, 2012; 189-190: 627–634.
[11] Zhou J G, Guan J H, Bian F L, Li P X. DCAD: a dual clustering algorithm for distributed spatial databases. Geo-spatial Information Science, 2007; 10(2): 137–144.
[12] Lin C R, Liu K H. Dual clustering: integrating data clustering over optimization and constraint domains. IEEE Trans. Knowl. Data Eng, 2005: 17(5): 628–637.
[13] Jiao L M, Liu Y L, Zou B. Self-organizing dual clustering considering spatial analysis and hybrid distance measures. Sci. China Ser. D, 2011; 54(8): 1268–1278.
[14] Hastie T, Tibshiani R. The elements of statistical leaning: Data mining, inference and prediction, second ed. Springer, New York, 2009.
[15] Harris P, FotheringhamA S, CrespoP, Charlton M. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences, 2010; 42(6): 657–668.
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Published
2017-09-30
How to Cite
Jiaogen, Z., Daming, D., & Yuyuan, L. (2017). Local attribute-similarity weighting regression algorithm for interpolating soil property valuesLocal attribute-similarity weighting regression algorithm for interpolating soil property values. International Journal of Agricultural and Biological Engineering, 10(5), 95–103. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2209
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Natural Resources and Environmental Systems
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