Journals on Biomedical Research - BJSTR Journal

Abstract

In the course of the study, we have disclosed methods for correcting and analyzing spatial data recorded in a vector format. The latter is best suited for spatial analysis of discrete objects. However, in the case when the spatial variable is represented as a field of #scalar or vector magnitudes (for example, spatial concentration distribution of concentrations of heavy metals in soils or the velocity field of groundwater movement). Convenient ways of data recording is a raster format. This approach is most often used for #phenomena of processes that are characterized by significant anisotropy. However, the characteristic feature of the inverse distance method is the fact that the interpolated value at the measured point is equal to the measured value.Figure 1 In it, the space consists of an infinite number of points and is divided into a certain number of rectangular fields called pixels, #mudflows, and a specific value is attributed [1]. Most often we own limited information about the spatial layout of the variable being studied. In general, our knowledge is reduced to a certain finite measuring points, on the basis of which conclusions are drawn about the possible distribution of quantities to the remaining points of space. Data collection at measuring points can be considered as a random series, based on which the forecasting of parameters outside the testing points is performed. This process is called interpolation of spatial data. There are many methods of interpolating data.

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Interpolation of Spatial Data  by Rae Zh Aliyev in BJSTR