Geostatistics Interpolation
Why: So far, none of the interpolation methods discussed so
far provides direct estimates of the quality of the predication
made in terms of an estimation variance for the predicted
values at certain location.
In all methods, t

Analysing spatial point patterns in R
Adrian Baddeley
Adrian.Baddeley@csiro.au
adrian@maths.uwa.edu.au
Workshop Notes
February 2008
Copyright c CSIRO 2008
Abstract
This is a detailed set of notes for a workshop on Analysing spatial point patterns that has

4.1.1 The Empirical Variogram
The empirical variogram provides a description of how the data are related
(correlated) with distance. The semivariogram function, 702), was origi-
nally deﬁned by Matheron (1963) as half the average squared differ-(nice
betw

The principles of geostatistical analysis
IN THIS CHAPTER
Understanding deterministic
methods
Understanding geostatistical
methods
Working through a problem
Basic principles behind
geostatistical methods
Modeling a semivariogram
Predicting unknown v

INTRODUCTION TO GEOSTATISTICS
And
VARIOGRAM ANALYSIS
C&PE 940, 17 October 2005
Geoff Bohling
Assistant Scientist
Kansas Geological Survey
geoff@kgs.ku.edu
864-2093
Overheads and other resources available at:
http:/people.ku.edu/~gbohling/cpe940
1
What is

3/30/2010
Spatial Point Patterns
Lecture #2
More complex study questions
So far, weve only looked at methods that can answer:
But really, we may be more interested in questions like:
Do events appear to cluster in space?
Over what scale does clustering oc

Spatial dependence - The tendency for observations close together in space to be more
highly correlated than those that are further apart. Also called spatial autocorrelation.
Spatial dependence imputes that up to some distance apart from each other, two

3/11/2010
Spatial Point Patterns
Lecture #1
Point pattern terminology
Point is the term used for an arbitrary location
Event is the term used for an observation
Mapped point pattern: all relevant events in a study
area R have been recorded
Sampled point p

Semivariogram Analysis and
Estimation
Tanya, Nick Caroline
Semivariogram
Gives information about the nature
and structure of spatial dependency
in a random field must be
estimated from the data
Estimating a semivariogram:
1. Derive empirical estimate from

Modelling Spatial Point Patterns in R
Adrian Baddeley1 and Rolf Turner2
1
2
School of Mathematics and Statistics, University of Western Australia, Crawley
6009 WA, Australia, adrian@maths.uwa.edu.au
Department of Mathematics and Statistics, University of

NOTEBOOK FOR SPATIAL DATA ANALYSIS
Part II. Continuous Spatial Data Analysis
_
6. Simple Spatial Prediction Models
In this section we consider the simplest spatial prediction models that incorporate random
effects. These spatial prediction models are part

Lecture 10: Introduction to Kriging
Math 586
Beginning remarks
Kriging is a commonly used method of interpolation (prediction) for spatial
data. The data are a set of observations of some variable(s) of interest, with
some spatial correlation present.
Usu

KRIGING
C&PE 940, 19 October 2005
Geoff Bohling
Assistant Scientist
Kansas Geological Survey
geoff@kgs.ku.edu
864-2093
Overheads and other resources available at:
http:/people.ku.edu/~gbohling/cpe940
1
What is Kriging?
Optimal interpolation based on regre

Use R!
Series Editors:
Robert Gentleman
Kurt Hornik
Giovanni Parmigiani
Use R!
Albert: Bayesian Computation with R
Bivand/Pebesma/Gmez-Rubio: Applied Spatial Data Analysis with R
Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis:
With R and