Geostatistical mapping can be defined as analytical production of maps by using field observations, auxiliary information and a computer program that generates predictions. The purpose of this guide is to assist you in producing quality maps by using fully-operational open source software packages: R+gstat/geoR and SAGA GIS. Materials presented in this book have been used for the five-day advanced training course "GEOSTAT: spatio-temporal data analysis with R+SAGA+Google Earth" that is periodically organized by the author and collaborators. This is an open access publication!

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GEOSTAT 2011 Summer School videos are available for download from the

Introduction by Roger Bivand on Day 1 of the GEOSTAT 2011.

Support independent publishing: Buy this book on Lulu.This is an Open Access Publication which means that a digital copy of this book is freely available under the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0.

Figure: Mean daily temperatures for four arbitrary dates predicted using spatio-temporal regression-kriging.


This figure is from: 

Hengl, T., Heuvelink, G.B.M., Percec Tadic, M., Pebesma, E., 2011. "Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images". Theoretical and Applied Climatology.


GEOSTAT posterIt is my pleasure to inform you that the next GEOSTAT summer school will be held in the period 24-31 July 2011 at the Koblenz-Landau University.

Next year a large international conference on spatial statistics will be held in Enschede, the Netherlands. For more info see:; I have been invited, together with a number of other open source enthusiasts, to talk about possibilities of combining statistics and geographical computing. Down-below is a short summary of my lecture.

Short title: 
get GSOD data
Purpose and use: 
Import, formating and binding of the Daily Global Weather Measurements, 1929-2009 (NCDC, GSOD)
Programming environment: 
R / S language
Status of work: 
Tested on a MS Windows machine only

Description of files available via the NCDC website. This is an extensive dataset and requires substantial storage and processing capacities - each station is saved in a separate directory and needs to be processed separately. The attached example shows temperature and precipitation measurements for 1st of May 2006.

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