2009
DOI: 10.1002/env.1019
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Spatial—temporal marked point processes: a spectrum of stochastic models

Abstract: SUMMARYMany processes that develop through space and time do so in response not only to their own individual growth mechanisms but also in response to interactive pressures induced by their neighbours. The growth of trees in a forest which compete for light and nutrient resources, for example, provides a classic illustration of this general spatialtemporal growth-interaction process. Not only has its mathematical representation proved to be a powerful tool in the study and analysis of marked point patterns sin… Show more

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Cited by 3 publications
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“…When minimum distances between objects are computable, the statistics described here are applicable to spatial data relating to objects and therefore have a broad applicability, in particular when interactions occur between marginal areas of objects. To advance spatial studies, it is necessary to construct spatial community dynamic models incorporating growth, mortality, reproduction and species interactions (Muko, Sakai & Iwasa ,b; Renshaw, Comas & Mateu ; Renshaw ), based on multispecies spatial data with size variations. Modelling requires summary statistics for estimating maximum likelihood, testing model fit, and other modelling processes (Penttinen, Stoyan & Henttonen ; Shimatani & Kubota ; Comas & Mateu ; Micheas & Wikle ; Waagepetersen & Guan ; Shimatani ).…”
Section: Discussionmentioning
confidence: 99%
“…When minimum distances between objects are computable, the statistics described here are applicable to spatial data relating to objects and therefore have a broad applicability, in particular when interactions occur between marginal areas of objects. To advance spatial studies, it is necessary to construct spatial community dynamic models incorporating growth, mortality, reproduction and species interactions (Muko, Sakai & Iwasa ,b; Renshaw, Comas & Mateu ; Renshaw ), based on multispecies spatial data with size variations. Modelling requires summary statistics for estimating maximum likelihood, testing model fit, and other modelling processes (Penttinen, Stoyan & Henttonen ; Shimatani & Kubota ; Comas & Mateu ; Micheas & Wikle ; Waagepetersen & Guan ; Shimatani ).…”
Section: Discussionmentioning
confidence: 99%