ponderosa

ponderosa.extra

The data record the locations of 108 Ponderosa Pine
 (Pinus ponderosa) trees in a 120 metre square region in the Klamath
 National Forest in northern California, published as Figure 2 of
 Getis and Franklin (1987). Franklin et al. (1985) determined the locations of approximately 5000
 trees from United States Forest Service aerial photographs and
 digitised them for analysis. Getis and Franklin (1987) selected a 120
 metre square subregion that appeared to exhibit clustering. This subregion
 is the <code>ponderosa</code> dataset. In principle these data are equivalent to
 Figure 2 of Getis and Franklin (1987) but they are not exactly
 identical; some of the spatial locations appear to be slightly
 perturbed. The data points identified as A, B, C on Figure 2 of Getis and Franklin
 (1987) correspond to points numbered 42, 7 and 77 in the dataset
 respectively.

datasets

spatial

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Diggle-Cressie-Loosmore-Ford, Dao-Genton) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Adrian Baddeley

spatstat

Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

ponderosa function

Typing <code>data(ponderosa)</code> gives access to two objects,
 <code>ponderosa</code> and <code>ponderosa.extra</code>. The dataset <code>ponderosa</code> is a spatial point pattern 
 (object of class <code>"ppp"</code>)
 representing the point pattern of tree positions.
 See <code><a rd-options="" href="/link/ppp.object?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::ppp.object">ppp.object</a></code> for details of the format. The dataset <code>ponderosa.extra</code> is a list containing supplementary
 data. The entry <code>id</code> contains the index numbers of the
 three special points A, B, C in the point pattern. The entry
 <code>plotit</code> is a function that can be called to produce a nice plot
 of the point pattern.

Format

Prof. Janet Franklin, University of California, Santa Barbara

Source

The data record the locations of 108 Ponderosa Pine
 (Pinus ponderosa) trees in a 120 metre square region in the Klamath
 National Forest in northern California, published as Figure 2 of
 Getis and Franklin (1987).

 Franklin et al. (1985) determined the locations of approximately 5000
 trees from United States Forest Service aerial photographs and
 digitised them for analysis. Getis and Franklin (1987) selected a 120
 metre square subregion that appeared to exhibit clustering. This subregion
 is the <code>ponderosa</code> dataset.

 In principle these data are equivalent to
 Figure 2 of Getis and Franklin (1987) but they are not exactly
 identical; some of the spatial locations appear to be slightly
 perturbed.

 The data points identified as A, B, C on Figure 2 of Getis and Franklin
 (1987) correspond to points numbered 42, 7 and 77 in the dataset
 respectively.

ponderosa: Ponderosa Pine Tree Point Pattern

Description

Usage

Arguments

Format

Source

References

Examples