3D Bathymetry -Part 3- Build a 3D map

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). ...

3D Bathymetry -Part 2- Kriging

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. ...

3D Bathymetry -Part 1- Build a sampling grid

Introduction. While conducting a marine seismic survey, a high variety of data is collected by the seismic vessel operating the survey. In addition to the seismic data acquired, water depths measured by the vessel’s echo sounder are providing useful information about the ocean floor. The high density of the spatial points with measured water depths makes it possible to create a 3D bathymetry map of the local area surveyed. In R, several packages allow for building 3D surface maps, such as rgl, plot3D, lattice, . ...

Household Energy Consumption (Multilevel Model Approach) - Shiny App

Introduction. As many other countries, France is facing the challenge of modelling its residential energy consumption (which is cccounting for more than 30% of the total energy consumption and is contributing for more than 16% of national CO2 emissions ). Modelling residential energy consumption is essential to be able to understand national energy problematic and predict future trends, thus to be prepared to adapt policies and legislation in order to meet energy efficiency requirements at global and European levels as, for instance, the recent adopted Loi Relative à la Transition Energetique (2015) ...