Comparison of spatial interpolation methods based on ArcGIS

Liu, Zenglu; Yan, Tianfeng

doi:10.1088/1742-6596/1961/1/012050

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…This advanced method supports the evaluation of data during the selection of interpolation algorithm parameters. The relationships between data are modeled to adjust for the effect of extreme values and derive skeleton lines [21][22][23][24]. Variogram analyses are performed to determine the correlations between the location of network points and point values.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Vertical Movements of the Earth’s Crust in Poland with the Co-Kriging Method Based on Various Sources of Data

Kowalczyk

Chojka

2020

Applied Sciences

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The main aim of this study was to evaluate the applicability of the co-kriging method for modeling the vertical movements of the Earth’s crust based on data acquired with the use of precision leveling techniques and measurements conducted by permanent Global Navigation Satellite System (GNSS) stations. Data were processed with the use of empirical, theoretical, and directional variograms (semivariograms), as well as variogram maps. Large-scale spatial variability was determined using polynomial regression. The relationships between the length of the semi-major and semi-minor axes vs. the root mean square (RMS) and the standard error of the estimate were analyzed. The relationships between the anisotropic direction and the number of lags were determined, and other parameters were calculated. Preliminary data fitting produced non-stationary surfaces. The leveling data were anisotropic, and the GNSS data were isotropic. Nugget effects were observed in both datasets, in particular in the GNSS data. The size of the ellipse was strongly correlated with the RMS and σ (average standard deviation of prediction). The anisotropy angle was determined using the number of lags. Co-kriging was found to not be a suitable method for modeling the vertical movements of the Earth’s crust based on data from various sources. The final result was strongly influenced by the initial dataset. The obtained results show how the method of combining data sets (interpolation, network adjustment) affected the final cartographic model.

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Section: Introductionmentioning

confidence: 99%

Modeling of the Vertical Movements of the Earth’s Crust in Poland with the Co-Kriging Method Based on Various Sources of Data

Kowalczyk

Chojka

2020

Applied Sciences

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“…The interpolation maps were performed using an inverse distance weighted (IDW) algorithm to improve the visualization of possible anomalies. Details on spatial interpolation methods are provided in Liu and Yan [64] and are used to discuss methods in this section. The inverse distance weighting (IDW) interpolation method was applied to the dataset as opposed to the Kriging method.…”

Section: Methodology 221 Statistical Analysis Of the Icp-ms Stream Se...mentioning

confidence: 99%

Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru

Crespo,

Holley,

Guillen

et al. 2023

Sustainability

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Stream sediments record water–rock interactions in tributaries followed by fluid mixing in larger downstream catchments, but it can be difficult to determine the relative contributions of each tributary. A good way to analyze this problem is sediment fingerprinting, which allows researchers to identify the source of sediments within a basin and to estimate the contribution of each source to the watershed. Herein, we developed a workflow using the frequentist model FingerPro v1.3 to quantify the sediment source contribution in a semiarid watershed. We applied an unmixing model algorithm to an ICP-MS geochemical database containing information on 32 elements in 362 stream sediment samples. By modeling the source contributions to these mixed samples, we infer that the main sediment contribution comes from the upper portion of the catchment (61–70%), followed by the middle (21–29%) and lower (8–10%) parts, with geochemical anomalies (As and Cu) being closely related to mining sites. Results from this study can be helpful for future management decisions to ensure a better environment in this semiarid watershed.

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“…The calculation of predicted points using this method is based on the distance between the observation point and the predicted point itself. Therefore, closer to the observational point, the interpolation points will receive a more significant influence than more prominent interpolation points distanced (Liu & Yan, 2021). A weighted average of the values at the dispersed nearby sites serves as the interpolation value at each interpolation point.…”

Section: Interpolation Methodsmentioning

confidence: 99%

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

Pratomo,

Safira,

Stefani

2023

Geodesy and Cartography

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The bottom surface’s portrayal is crucial in many different practices. Therefore, accurate bathymetry data is required. The interpolation method is one element that influences the accuracy of a Single Beam Echosounder’s depth data. IDW, Kriging, and TIN are three standard interpolation techniques. This study compares these three methods with two scenarios utilizing the spatial analysis to establish the most effective technique for producing the digital elevation model of the seafloor beneath Bawean Island. The IDW exhibits the strongest R-squared (0.9998779 in Scenario-1 and 0.9999875 in Scenario-2) and correlation (0.9998796 in Scenario-1 and 0.9999595 in Scenario-2). It indicates that IDW and bathymetric data have the closest relationships. IDW has the lowest error, as measured by the MAE value (0.02 in Scenario-1 and 0.009 in Scenario-2), followed in both cases by Kriging and TIN. Additionally, the RMSE for IDW shows the same outcome (0.045 in Scenario 1 and 0.016 in Scenario 2). In the meantime, comparing the first and second scenarios reveals that the second, which has fewer data, is preferable to the first. Since the MAE and RMSE in the first scenario are greater than those in the second, we may infer that more data leads to more significant errors.

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Comparison of spatial interpolation methods based on ArcGIS

Cited by 10 publications

References 10 publications

Modeling of the Vertical Movements of the Earth’s Crust in Poland with the Co-Kriging Method Based on Various Sources of Data

Modeling of the Vertical Movements of the Earth’s Crust in Poland with the Co-Kriging Method Based on Various Sources of Data

Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

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