A Comparative Analysis of Areal Interpolation Methods for Representing Spatial Distribution of Population Subgroups

Abstract It is vital to accurately map the spatial distribution of precipitation, which is widely used in many fields such as hydrology, climatology, meteorology, ecology, and agriculture. In this study, it was aimed to reveal the spatial distribution of seasonal long-term average precipitation in the Euphrates Basin by using various interpolation methods. For this reason, Simple Kriging (SK), Ordinary Kriging (OK), Universal Kriging (UK), Ordinary CoKriging (OCK), Empirical Bayesian Kriging (EBK), Radial Basis Functions (Completely Regularized Spline (CRS), Thin Plate Spline (TPS), Multiquadratic, Inverse Multiquadratic (IM), Spline with Tensor (ST)), Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), Inverse Distance Weighting (IDW) methods have been applied in the Geographical Information Systems (GIS) environment. Long-term seasonal precipitation averages between 1966 and 2017 are presented as input for the prediction of precipitation maps. The accuracy of the precipitation prediction maps created was based on root mean square error (RMSE) values obtained from the cross-validation tests. The method of precipitation by interpolation yielding the lowest RMSE was selected as the most appropriate method. As a result of the study, OCK in spring and winter precipitation, LPI in summer precipitation, and OK in autumn precipitation were determined as the most appropriate estimation method.

Download Full-text

Determination of spatial distribution of iron and manganese contents with different interpolation methods at rice cultivated areas.

ANADOLU JOURNAL OF AGRICULTURAL SCIENCES ◽

10.7161/omuanajas.287480 ◽

2017 ◽

Vol 32 (1) ◽

pp. 64-64

Author(s):

Mehmet TAŞAN ◽

Yusuf DEMİR

Keyword(s):

Spatial Distribution ◽

Interpolation Methods ◽

Iron And Manganese

Download Full-text

Influence of Sampling Point Discretization on the Regional Variability of Soil Organic Carbon in the Red Soil Region, China

Sustainability ◽

10.3390/su10103603 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3603 ◽

Cited By ~ 1

Author(s):

Zhongqi Zhang ◽

Yiquan Sun ◽

Dongsheng Yu ◽

Peng Mao ◽

Li Xu

Keyword(s):

Spatial Distribution ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Spatial Interpolation ◽

Red Soil ◽

Soil Sampling ◽

Sampling Point ◽

Regional Variability ◽

Interpolation Methods ◽

Sampling Points

Research on the regional variability of soil organic carbon (SOC) has focused mostly on the influence of the number of soil sampling points and interpolation methods. Little attention has typically been paid to the influence of sampling point discretization. Based on dense soil sampling points in the red soil area of Southern China, we obtained four sample discretization levels by a resampling operation. Then, regional SOC distributions were obtained at four levels by two interpolation methods: ordinary Kriging (OK) and Kriging combined with land use information (LuK). To evaluate the influence of sample discretization on revealing SOC variability, we compared the interpolation accuracies at four discretization levels with uniformly distributed validation points. The results demonstrated that the spatial distribution patterns of SOC were roughly similar, but the contour details in some local areas were different at the various discretization levels. Moreover, the predicted mean absolute errors (MAE) and root mean square errors (RMSE) of the two Kriging methods all rose with an increase in discretization. From the lowest to the largest discretization level, the MAEs of OK and LuK rose from 4.47 and 3.02 g kg−1 to 5.46 and 3.54 g kg−1, and the RMSEs rose from 5.13 and 3.95 g kg−1 to 5.76 and 4.76 g kg−1, respectively. Though the trend of prediction errors varied with discretization levels, the interpolation accuracies of the two Kriging methods were both influenced by the sample discretization level. Furthermore, the spatial interpolation uncertainty of OK was more sensitive to the discretization level than that of the LuK method. Therefore, when the spatial distribution of SOC is predicted using Kriging methods based on the same sample quantity, the more uniformly distributed sampling points are, the more accurate the spatial prediction accuracy of SOC will be, and vice versa. The results of this study can act as a useful reference for evaluating the uncertainty of SOC spatial interpolation and making a soil sampling scheme in the red soil region of China.

Download Full-text