Multitype Spatial Point Patterns with Hierarchical Interactions

HöUgmander, Harri; SäUrkkä, Aila

doi:10.1111/j.0006-341x.1999.01051.x

Cited by 40 publications

(35 citation statements)

References 12 publications

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“…Assuming known interaction radii, Högmander and Särkkä (1999) consider a hierarchical model with interactions for a bivariate point pattern of ants' nests. However, in our situation, we believe that the assumption of known and equal interaction radii for resprouters of the same type is highly unrealistic, since the plants vary in size, and we choose to focus on this problem in the next section.…”

Section: Assumptions and Resultsmentioning

confidence: 99%

Hierarchical spatial point process analysis for a plant community with high biodiversity

2007

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A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species' interactions and to generate new biological questions and hypotheses on species' interactions.

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Section: Assumptions and Resultsmentioning

confidence: 99%

Hierarchical spatial point process analysis for a plant community with high biodiversity

2007

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“…The full dataset records the locations of nests of two species of ants, observed in an irregular convex polygonal boundary, together with annotations showing a foot track through the region, and the boundary between field and scrub areas inside the region. Rectangular subsets of the data (marked "A" and "B" on the Figure) were analysed in Harkness and Isham (1983); Isham (1984); Takacs and Fiksel (1986); Högmander and Särkkä (1999); Baddeley and Turner (2000) and (Särkkä 1993, section 5.3). Again, as far as we are aware, the full dataset has never been subjected to detailed analysis inside the correct window.…”

Section: Region IImentioning

confidence: 99%

spatstat: AnRPackage for Analyzing Spatial Point Patterns

Baddeley¹,

Turner²

2005

J. Stat. Soft.

2,009

1,579

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Abstractspatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, 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.

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“…To address such questions a suitable modelling framework is needed. The literature on multivariate spatial point process models is mainly restricted to the bivariate case; see for example Diggle and Milne (), Harkness and Isham (), Högmander and Särkkä (), Brix and Møller (), Allard et al . (), Diggle (), Picard et al .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multispecies Point Patterns by Using Multivariate Log-Gaussian Cox Processes

Waagepetersen

Guan

Jalilian

et al. 2015

Journal of the Royal Statistical Society Series C: Applied Statistics

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Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multispecies point patterns of tree locations. In particular we address the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows a decomposition of variance that can be used to quantify to which extent the spatial variation of a species is governed by common respectively species specific factors. Crossvalidation is used to select the number of common latent fields in order to obtain a suitable trade-off between parsimony and fit of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.

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Multitype Spatial Point Patterns with Hierarchical Interactions

Cited by 40 publications

References 12 publications

Hierarchical spatial point process analysis for a plant community with high biodiversity

Hierarchical spatial point process analysis for a plant community with high biodiversity

spatstat: AnRPackage for Analyzing Spatial Point Patterns

Analysis of Multispecies Point Patterns by Using Multivariate Log-Gaussian Cox Processes

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