Accuracy Assessment and Comparative Analysis of IDW, Spline and Kriging in Spatial Interpolation of Landform (Topography): An Experimental Study

Abstract

It is practically impossible and unnecessary to obtain spatial-temporal information of any given continuous phenomenon at every point within a given geographic area. The most practical approach has always been to obtain information about the phenomenon as in many sample points as possible within the given geographic area and estimate the values of the unobserved points from the values of the observed points through spatial interpolation. However, it is important that users understand that different interpolation methods have their strength and weaknesses on different datasets. It is not correct to generalize that a given interpolation method (e.g. Kriging, Inverse Distance Weighting (IDW), Spline etc.) does better than the other without taking into cognizance, the type and nature of the dataset and phenomenon involved. In this paper, we theoretically, mathematically and experimentally evaluate the performance of Kriging, IDW and Spline interpolation methods respectively in estimating unobserved elevation values and modeling landform. This paper undertakes a comparative analysis based on the prediction mean error, prediction root mean square error and cross validation outputs of these interpolation methods. Experimental results for each of the method on both biased and normalized data show that Spline provided a better and more accurate interpolation within the sample space than the IDW and Kriging methods. The choice of an interpolation method should be phenomenon and data set structure dependent.

Spatial Representation of Surface Water Monitoring and Its Assessment Using Geostatistical and Non-Geostatistical Techniques in Gis

“…The interpolation techniques help to assessment and monitor values for unknown point and create a continuous surface dataset of the spatial distribution. Spatial interpolation has continued to be an important tool for estimating continuous spatial environmental variables for effective decision making (Ikechukwu, Ebinne, Idorenyin, & Raphael, 2017). Interpolation tools available in geographical information systems are useful and allow the operator to easily perform different kind of elaborations and to display them graphically in order to show the results in a way intelligible also to non-skilled subjects (Garnero & Godone, 2013).…”

Development of digital bathymetry maps for Lakes Azuei and Enriquillo using sonar and remote sensing techniques

“…Any of these aforementioned factors may introduce errors or inaccuracies in the final product. Moreover, these factors influence the selection of a suitable interpolation method for a specific dataset (Ikechukwu, Ebinne, Idorenyin, & Raphael, ). For instance, IDW and spline methods are known to be effective when the density of source data is high and their distribution is homogeneous, because these methods depend largely on the neighborhood of target points (Leon & Cohen, ).…”

“…For instance, IDW and spline methods are known to be effective when the density of source data is high and their distribution is homogeneous, because these methods depend largely on the neighborhood of target points (Leon & Cohen, ). Tests have shown that IDW yields more accurate results than other methods when generating a DTM from a contour map, which is regularly spaced source data (Tan & Xu, ), while splines have been used successfully in cases where the data were more irregularly spaced (Ikechukwu et al, ). In contrast, the kriging approach, which is a statistical model, does not require homogeneity of the spatial distribution but requires more steps, such as the normalization of the dataset, and is thus more labor‐intensive (Ikechukwu et al, ).…”

“…Tests have shown that IDW yields more accurate results than other methods when generating a DTM from a contour map, which is regularly spaced source data (Tan & Xu, ), while splines have been used successfully in cases where the data were more irregularly spaced (Ikechukwu et al, ). In contrast, the kriging approach, which is a statistical model, does not require homogeneity of the spatial distribution but requires more steps, such as the normalization of the dataset, and is thus more labor‐intensive (Ikechukwu et al, ). Another method of generating DTMs is ANUDEM, a hydrological‐based interpolation with an underlying iterative finite difference technique (Hutchinson, ), similar to the use of splines (Lewis, ).…”

Spatial Distribution of BTEX and Inorganic Pollutants During Summer Season in Yalova, Turkey

“…These techniques also provide very useful knowledge about the structure of air quality patterns. There is no clear agreement on any approach whether it could be accepted optimal globally, although a number of studies compare these techniques [16][17][18]. No matter which method, environmental health agencies are increasingly using GIS technology to protect the health of populations and understand the impacts of the air pollution on human health.…”