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GEOSTAT tutorials

GEOSTAT software

Software iconList of FOSS software used in this course and installation instructions. Follow these instructions to prepare and customize the software before the beginning of the course.

Literature used

ASDAR bookList of books / lecture notes used in this course. See also: CRAN Task View: Analysis of Spatial Data.

Events

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Hengl: Automated spatial prediction and visualization in 3D+T

Date & time: Thursday 22 September 2016, 13.30-17.00

Objective: Provide demo of functionality of the GSIF and plotKML packages with a focus on automated mapping
General descriptionplotKML (R package) is a platform for scientific visualization of spatio-temporal data and models. It allows fast parsing of spatial, spatio-temporal and profile objects in R to KML and allows users to quickly visualize results of spatial analysis from R to Google Earth. GSIF package contains wrapper functions for automated soil mapping in 2D and 3D; current version of the autopredict function relies on using Machine Learning algorithms mainly from ranger and quantregforest (quantile regression forests) packages. By combining GSIF and the plotKML packages spatial predictions can be created and visualized in Google Earth by using few lines of code.
Required back-ground knowledge: gstat, sp and spacetime classes in R; machine learning, variogram modelling;
Software / R packages required: plotKML, GSIF, sp, raster, rgdal, gstat, ranger, randomForestSRC, xgboost, aqp, XML, randomForest, quantregForest;

Software installation instructions:

First install plotKML and GSIF packages. Other packages can be added on the fly.

Provisional programme:

12:30–13:30 Lunch break 

13:30–15:00 Installation of the package and general functionality; using machine learning to create spatial predictions:

15:30–17:00 Importing and visualizing profile data, polygon maps and gridded maps; making visualizations with your own datasets (1.5 hrs)

Literature:

 

Location

Albacete
Spain
iframe: 
Automated spatial prediction
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