This research focuses on making classification maps using the Classification And Regression Trees (CART), Random Forest and Ordinary Kriging methods. The dataset used is data from Area Traffic Control System (ATCS) of Bandung City Transportation Agency Google Maps application in April 2022. After obtained, then pre-processing process will be carried out CART learning models made, after complete...

Upscaling in situ soil moisture observations (ISMO) to multiscale pixel estimations with kriging is a key step in the comprehensive usage of ISMO and remote sensing (RS) soil moisture data. Scale effects occur and introduce uncertainties during upscaling processes because of spatial heterogeneity and the kriging method. A nested hierarchical scale series was established at the field level, and ...

Soil surface roughness (SSR) is a parameter highly suited to the study of soil susceptibility to wind and water erosion. The development of a methodology for quantifying SSR is therefore instrumental to soil evaluation. We developed such a method, based on the multifractal analysis (MFA) of soil elevation measurements collected at the intersections on a 2by 2-cm grid in a 200by 200-cm plot. Sam...

Both estimation and simulation approaches are formulated as the selection of a set of attribute values that are optimal for criteria that are typically conflicting. Estimation amounts to minimize local criteria such as a local error variance, whereas stochastic simulation aims to reproduce global Ž . statistics such as the histogram or semivariogram. A simulated annealing SA algorithm is presen...

As spatial autocorrelation latent in georeferenced data increases, the amount of duplicate information contained in these data also increases. This property suggests the research question asking what the number of independent observations, ay n*, is that is equivalent o the sample size, n, of a data set. This is the notion of effective sample size. Intuitively speaking, when zero spatial autoco...

The characterization of spatial dependence is an important component of a spatial modeling exercise. For reasons of convenience, model parsimony, or computational efficiency, the spatial covariance structure is often assumed to be isotropic (direction-invariant), completely symmetric, or reflection symmetric (the latter two being forms of directional invariance somewhat weaker than isotropy). W...