Statistical Analysis and Modelling of Spatial Point Patterns

By J. Illian, Antti Penttinen, H. Stoyan, Dietrich Stoyan. 2008

1,820 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7e368027508794b46be3c8035337e269a9fd4307

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In Applied Mathematics

Citations in the corpus

Evidence for spin alignment of spiral and elliptical/S0 galaxies in filaments (2012, methodology): “We use Monte Carlo approximation to estimate the orientation distribution for the case where there are no correlations between the orientations of filaments and galaxies, and to find the confidence intervals for this estimate (see e.g. Illian et al. 2008).”
Optimum design of experiments for statistical inference (2012, background): “Spatial point processes describe stochastic models where events that are generated by the model have an associated random location in space (Diggle, 2003; Illian et al., 2008).”
Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, methodology): “Similarly, Campbell’s Theorem has a bivariate form for h, a function of two points in S: ES∩D [ ∑ si, sj ∈S∩D i =j h(si, sj) ] = ∫ D ∫ D h(s, s′)γ(s, s′) ds ds′.”
Spatial distribution patterns of the gorgonians Eunicella singularis, Paramuricea clavata, and Leptogorgia sarmentosa (Cape of Creus, Northwestern Mediterranean Sea) (2011, background): “Such patterns will have a fundamental influence both on future processes within each species (feeding, growth, reproduction, intra-specific competition) and on their interactions with other species (predation, inter-specific competition) (Dale 1999) because the spatial structure determines ecologica”
Spatial logistic regression and change-of-support in Poisson point processes (2010, methodology): “In statistical science, spatial point pattern data such as Figure 1 are usually analysed by applying spatial point process methods [29, 59, 43] to the exact spatial coordinates of the data points.”
Adopting a spatially explicit perspective to study the mysterious fairy circles of Namibia (2015, methodology): “In a first analysis we computed Voronoi tessellations for the point patterns (Illian et al. 2008).”
Lectures on the Poisson Process (2017, background): “Further comments on the history of the Poisson process can be found in [27, 44, 53, 63, 65, 103].”
Spatial modelling with R-INLA: A review (2018, background): “…objects or events in space (the “points”) are the observations of interest, and one typically aims to learn about the mechanisms that generated the spatial pattern formed by the locations of the objects or events represented by these points (Møller et al., 1998; Diggle, 2003; Illian et al., 2008).”
Spatial variation in below‐ground seed germination and divergent mycorrhizal associations correlate with spatial segregation of three co‐occurring orchid species (2012, methodology): “We, therefore, used mark connection functions (Getzin et al. 2008; Illian et al. 2008) that allow us to quantify for a given distance r the contribution of each species to the overall clustering of all three orchids.”
Quantifying spatio-temporal dispersion of bark beetle infestations in epidemic and non-epidemic conditions (2011, methodology): “This algorithm is suitable for spatial point processes analysis (Illian et al., 2008) and was adapted here to distance rings.”
Fitting complex ecological point process models with integrated nested Laplace approximation (2013, background): “In other words, conditional on the random field, the point pattern may be described by the statistical model for complete spatial randomness, the Poisson process (Illian et al. 2008; Law et al. 2009).”
Tree spatial patterns and stand attributes in temperate forests: The importance of plot size, sampling design, and null model (2018, methodology): “Combining information from several plots, considered as pseudo-replications, has been proposed (Illian et al., 2008; Wiegand and Moloney, 2014) and used (De Luis et al., 2008; Raventós et al., 2010; Petritan et al., 2014; Piermattei et al., 2016) to reinforce and stabilize results from small plots,…”