author: AhmadReza Boskabadi date: 2 Feb 2023 Email: ahmadreza.boskabadi@gmail.com

Spatial-Data-Analysis

Applied Spatial Data Analysis in geoscience

##1)Data Our geological and mineral data includes the following: sediment samples drilling trenches IP/RS profiles faults map Geological map (lithology, dike, ...)

sample location:

Plot+legend+Grid...{R}

geom_point {ggplot2}

2D Show (x.y, sediment samples,trenches,IP/RS profiles,...)

geom_point {ggplot2}

geom_bin_2d {ggplot2}

geom_raster {ggplot2}

geom_point {ggplot2}

data.frame,geom_point,scale_color {ggplot2}

mapView {mapview}

3D Show (x.y.z,drilling,...)

scatterplot3d {scatterplot3d}

plot3D {plot3D}

plotly {plotly}

level plot 3D Show (x.y.z,3d model,3d kriging,...)

Levelplot,contourplot,cloud,wireframe{gstat}

##2)Data Parameter

Desc {DescTools}

geom_histogram{ggplot2}

##3)Data Analysis

qqnorm {stats}

classIntervals {classInt}

trend analysis

##4)Geostatistics

The empirical variogram and variogram Cloud

variogram {gstat}

Generates a variogram model and Fit ranges, sills, nugget

vgm {gstat}, fit.variogram {gstat} SSE of manual fit is 0.01252028 and automatic fit is 0.005756448

##5)interpolation

interpolation methods

###idw

idw = function for inverse distance weighted interpolation, Idp = numeric; specify the inverse distance weighting power

idw{gstat}, idp = 2,krige {gstat}, spplot {raster}, resolution is 100*100

###kriging

krige {gstat}, spplot {raster}, resolution is 100*100

krige {gstat}, spplot {raster}, st_crop {sf}, resolution is 718*836

krige {gstat}, geom_stars {stars}, st_crop {sf}, resolution is 718*836

plot_ly {plotly} 3D visualization

raster {raster}, KML {raster} used by Google Earth

###model-based geostatistics

SPDE

inla.mesh.2d {INLA} High-quality triangulations max.edge is the largest allowed triangle length; lower number equal higher the resolution max.edge = c(inside the boundary triangle, outside the boundary triangle) offset is defining how far you want to extend your domain (i.e. a secondary boundary box) cutoff can be used to avoid building too many small triangles around clustered data locations

inla.mesh.2d {INLA} High-quality triangulations

INLA

inla.spde.make.A {INLA} Observation/prediction matrices for mesh models inla.spde2.matern {INLA} Matern SPDE model object for INLA inla.spde.make.index {INLA} SPDE model index vector generation inla.stack {INLA} Data stacking for advanced INLA models formula <- y ~ 0 + b0 + f(s, model = spde) inla {INLA} Bayesian analysis of structured additive models

author: AhmadReza Boskabadi date: 2 Feb 2023 Email: ahmadreza.boskabadi@gmail.com