Titlebook: Case Studies in Spatial Point Process Modeling; Adrian Baddeley,Pablo Gregori,Dietrich Stoyan Book 2006 Springer-Verlag New York 2006 Bran

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https://doi.org/10.1007/978-1-4612-4808-8th significant differences with respect to intensities. We will discuss two examples in the fields of cell biology and ecology were these methods can be applied. They have been chosen, because they demonstrate the wide range of applications for the described techniques and because both examples have

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Alfredo A. Sadun,James D. Brandts to characterize herd spatial distributions of several species comprising a savanna large herbivore community in Laikipia, central Kenya. Points are the herd centres, marks are the herd sizes. Previous research [15] identified possible discrepancies between prey and non-prey species on the basis of

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Alfredo A. Sadun,James D. Brandtustering features of earthquakes are important for seismological studies..Based on some empirical laws in seismicity studies, several point-process models have been proposed in literature, classifying seismicity into two components, background seismicity and clustering seismicity, where each earthqu

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https://doi.org/10.1007/978-1-4612-4830-9tistic, called the . or .-function, is useful for assessing the fit of point process models. The advantage of this measure of goodness-of-fit is that it can be used in situations where the null hypothesis is not a stationary Poisson model. We note a correspondence between the weighted .-function and

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Adrian Baddeley,Pablo Gregori,Dietrich StoyanFocuses on case studies, rather than methodological aspects of point process modelling.Includes supplementary material:

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Optimum Designs for Multi-Factor ModelsWe describe practical techniques for fitting stochastic models to spatial point pattern data in the statistical package R. The techniques have been implemented in our package spatstat in R. They are demonstrated on two example datasets.

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