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Introduction. The kriging interpolation model conducted in part 2 of this article resulted in approximately one million water depth interpolated observations covering the entire area surveyed by the vessel. We have been able to create a simple 2D bathymetry map using the output of the kriging interpolation and now we can follow a few last steps for generating a 3D bathymetry map. Part 3 - Build a 3D map First of all, let’s convert our SpatialPixelDataFrame object created with kriging interpolation to a data frame with 3 columns : X (easting), Y (northing), and Depths (Water depth). ...

Summary Using this Shiny app, one can visualize oil prices (from 2000 to 2021) and events that may have had an impact on prices. The idea here is to overlay historical event data on top of a time series graph. The user can select a year and a type of source of event data to be displayed (Oil Timeline or Wikipedia). Oil prices were retrieved thanks to the U.S. Energy Information Administration API (EIA). ...

Introduction. In the first part of this article we managed to generate a sampling grid matching the contour of the surveyed area,starting from a data frame containing spatial metrics. This sampling grid will be implemented when building the krige function available with the gstat library. Part 2 - Kriging In a nutshell, Kriging is a geostatistical method which : measures spatial variability of a geographic data attribute. allows to predict or estimate the value at the location where the true value is unknown. ...