A sequential point process model and Bayesian inference for spatial point patterns with linear structures

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7 Citations (Scopus)

Abstract

We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model. Under this model, the points can be of one of three types: a ‘background point’ an ‘independent cluster point’ or a ‘dependent cluster point’. The background and independent cluster points are thought to exhibit ‘complete spatial randomness’, whereas the dependent cluster points are likely to occur close to previous cluster points. We demonstrate the flexibility of the model for producing point patterns with linear structures and propose to use the model as the likelihood in a Bayesian setting when analysing a spatial point pattern exhibiting linear structures. We illustrate this methodology by analysing two spatial point pattern datasets (locations of bronze age graves in Denmark and locations of mountain tops in Spain).
Original languageEnglish
JournalScandinavian Journal of Statistics
Volume39
Issue number4
Pages (from-to)618-634
Number of pages17
ISSN0303-6898
DOIs
Publication statusPublished - Dec 2012

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Spatial Point Pattern
Bayesian inference
Point Process
Process Model
Spatial Point Process
Process model
Point process
Dependent
Model
Randomness
Likelihood
Likely
Flexibility

Cite this

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title = "A sequential point process model and Bayesian inference for spatial point patterns with linear structures",
abstract = "We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model. Under this model, the points can be of one of three types: a ‘background point’ an ‘independent cluster point’ or a ‘dependent cluster point’. The background and independent cluster points are thought to exhibit ‘complete spatial randomness’, whereas the dependent cluster points are likely to occur close to previous cluster points. We demonstrate the flexibility of the model for producing point patterns with linear structures and propose to use the model as the likelihood in a Bayesian setting when analysing a spatial point pattern exhibiting linear structures. We illustrate this methodology by analysing two spatial point pattern datasets (locations of bronze age graves in Denmark and locations of mountain tops in Spain).",
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A sequential point process model and Bayesian inference for spatial point patterns with linear structures. / Møller, Jesper; Rasmussen, Jakob Gulddahl.

In: Scandinavian Journal of Statistics, Vol. 39, No. 4, 12.2012, p. 618-634.

Research output: Contribution to journalJournal articleResearchpeer-review

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