Tutorial: Analysis of Spatial Point Patterns in 'R'

This is a detailed set of workshop notes on analyzing spatial point patterns using the statistical software package 'R'.

These workshop notes, written in 2008, cover statistical methods available in public domain software.

The workshop uses the statistical package 'R' and is based on 'spatstat', an add-on library for 'R' for the analysis of spatial data.

Topics covered include:

* statistical formulation and methodological issues
* data input and handling
* R concepts such as classes and methods
* nonparametric intensity estimates
* goodness-of-fit testing for Complete Spatial Randomness
* maximum likelihood inference for Poisson processes
* model validation for Poisson processes
* distance methods and summary functions such as Ripley’s K function
* non-Poisson point process models
* simulation techniques
* fitting models using summary statistics
* Gibbs point process models
* fitting, simulating and validating Gibbs models
* multitype and marked point patterns
* exploratory analysis of marked point patterns
* multitype Poisson process models and maximum likelihood inference
* multitype Gibbs process models and maximum pseudolikelihood
* line segment data.

This workshop tutorial requires the software 'R' version 2.6.0 or later, and 'spatstat' version 1.14-5 or later.

Download the guide here, in PDF.