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13
Spatstat: An R package for analyzing spatial point patterns
 Journal of Statistical Software
, 2005
"... spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, modelfitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, ..."
Abstract

Cited by 62 (2 self)
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spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, modelfitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and ‘marks ’ attached to the points of the point pattern. A unique feature of spatstat is its generic algorithm for fitting point process models to point pattern data. The interface to this algorithm is a function ppm that is strongly analogous to lm and glm. This paper is a general description of spatstat and an introduction for new users.
Residual analysis for spatial point processes (with discussion
 Journal of the Royal Statistical Society (series B
, 2005
"... [Read before The Royal Statistical Society at a meeting organized by the Research Section on ..."
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Cited by 24 (5 self)
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[Read before The Royal Statistical Society at a meeting organized by the Research Section on
Modern statistics for spatial point processes
, 2006
"... We summarize and discuss the current state of spatial point process theory and directions for future research, making an analogy with generalized linear models and random effect models, and illustrating the theory with various examples of applications. In particular, we consider Poisson, Gibbs, and ..."
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Cited by 3 (1 self)
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We summarize and discuss the current state of spatial point process theory and directions for future research, making an analogy with generalized linear models and random effect models, and illustrating the theory with various examples of applications. In particular, we consider Poisson, Gibbs, and Cox process models, diagnostic tools and model checking, Markov chain Monte Carlo algorithms, computational methods for likelihoodbased inference, and quick nonlikelihood approaches to inference.
Structured spatiotemporal shotnoise Cox point process models, with a view to modelling forest fires
, 2008
"... Abstract: Spatiotemporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shotnoise process, are considered. Such models are flexible and tract ..."
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Cited by 1 (0 self)
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Abstract: Spatiotemporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shotnoise process, are considered. Such models are flexible and tractable for statistical analysis, using spatiotemporal versions of intensity and inhomogeneous Kfunctions, quick estimation procedures based on composite likelihoods and minimum contrast estimation, and easy simulation techniques. These advantages are demonstrated in connection to the analysis of a relatively large dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatiotemporal logGaussian Cox point process model, and likelihoodbased methods are discussed to some extent. Keywords: composite likelihood; Cox process; forest fires; inhomogeneous Kfunction; intensity; logGaussian process; minimum contrast estimation; multiplicative model; pair correlation function; Poisson process; simulation; shotnoise process; spatiotemporal point process. 1
Probing clustering features around Cl 0024+17
, 905
"... I present a spatial analysis of the galaxy distribution around the cluster Cl 0024+17. The basic aim is to find the scales where galaxies present a significant deviation from an inhomogeneous Poisson statistical process. Using the generalization of the Ripley, Besag, and the pair correlation functio ..."
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I present a spatial analysis of the galaxy distribution around the cluster Cl 0024+17. The basic aim is to find the scales where galaxies present a significant deviation from an inhomogeneous Poisson statistical process. Using the generalization of the Ripley, Besag, and the pair correlation functions for nonstationary point patterns, I estimate these transition scales for a set of 1,000 Monte Carlo realizations of the Cl 0024+17 field, corrected for completeness up to the outskirts. The results point out the presence of at least two physical scales in this field at 31.4 ′ ′ and 112.9 ′ ′. The second one is statistically consistent with the dark matter ring radius ( ∼ 75 ′ ′ ) previously identified by Jee et al. (2007). However, morphology and anisotropy tests point out that a clump at ∼ 120 ′ ′ NW from the cluster center could be the responsible for the second transition scale. These results do not indicate the existence of a galaxy counterpart of the dark matter ring, but the methodology developed to study the galaxy field as a spatial point pattern provides a good statistical evaluation of the physical scales around the cluster. I briefly discuss the usefulness of this approach to probe features in galaxy distribution and Nbody dark matter simulation data. Key words: astrophysics, galaxy clusters 1.
Getting started with spatstat
"... For spatstat version 1.310 Welcome to spatstat, a package in the R language for analysing spatial point patterns. This document will help you to get started with spatstat. It gives you a quick overview of spatstat, and some cookbook recipes for doing basic calculations. What kind of data does spats ..."
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For spatstat version 1.310 Welcome to spatstat, a package in the R language for analysing spatial point patterns. This document will help you to get started with spatstat. It gives you a quick overview of spatstat, and some cookbook recipes for doing basic calculations. What kind of data does spatstat handle? Spatstat is mainly designed for analysing spatial point patterns. For example, suppose you are an ecologist studying plant seedlings. You have pegged out a 10 × 10 metre rectangle for your survey. Inside the rectangle you identify all the seedlings of the species you want, and record their (x, y)
Dear Colleagues
"... Welcome to another issue of Pedometron. As usual we have a diverse contents for you to peruse. Although I am writing this on the first working day in March, this is the last Pedometron before the Pedometrics 2009 conference in Beijing at the end of August. April 30 th is the deadline for abstracts; ..."
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Welcome to another issue of Pedometron. As usual we have a diverse contents for you to peruse. Although I am writing this on the first working day in March, this is the last Pedometron before the Pedometrics 2009 conference in Beijing at the end of August. April 30 th is the deadline for abstracts; and I hope that all readers of Pedometron will consider submitting an item and participating in the first Pedometrics meeting on the continent of Asia. In late 2001 Max Perutz, who won a Nobel Prize for working out the structure of the haemoglobin molecule, discovered that he had terminal cancer. One paper that he rushed to finish was on the molecular structure of the amyloid fibres that cause various brain diseases. According to his biographer, Georgina
Finding Holes in Data
, 2009
"... Attempts are made to employ persistent homology to infer topological properties of point cloud data. Two potential approaches are introducted and briefly evaluated, both relying on Monte Carlo estimation and the program PLEX [9]. First, homology signatures are compared to a null distribution that ..."
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Attempts are made to employ persistent homology to infer topological properties of point cloud data. Two potential approaches are introducted and briefly evaluated, both relying on Monte Carlo estimation and the program PLEX [9]. First, homology signatures are compared to a null distribution that exhibits complete spatial randomness. This can be regarded as similar to hypothesis testing with the bootstrap. The second applies permutation testing to landmark selection. 1 1 Intro Topological Statistics is a relatively new and exciting field that lies in the intersection of Mathematics (Algebraic Topology) and Statistics. The main points are that methods from geometry and topology attempt to make precise the notions that concepts such as measures and coordinate systems
POINT PATTERN ANALYSIS 3 Contents
, 2011
"... I’m very grateful to Dr. Stephen Connor for his guidance and support throughout this Dissertation. I would also like to acknowledge the support ..."
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I’m very grateful to Dr. Stephen Connor for his guidance and support throughout this Dissertation. I would also like to acknowledge the support