Modelling Spatial Point Patterns in R

Abstract. We conducted a door-to-door survey in a residential census tract of Philadelphia to estimate the prevalence and spatial patterns of recent bed bug infestations. We interviewed 596 residents, of whom 66 (11.1%) reported recent bed bug infestations. We confirmed current infestations in a subset of 15 (68.2%) of 22 inspected households. Most residents reported that their infestation began within the past year (2012)(2013). We found no correlation between property value and infestation status. Spatial analyses showed significant clustering of bed bug infestations only at fine scales, suggesting limited active dispersal of the insects. Residents used a large variety of treatment methods to eliminate bed bugs, but only 48.1% reported success. Our results provide a prevalence estimate of recent bed bug infestations and highlight the importance of passive rather than active dispersal of bed bugs even among dense urban row homes.

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“…The Ripley's L Function for multi-distance spatial cluster analysis was also applied. [20][21][22] All analyses were performed in R. 23 …”

Section: Methodsmentioning

confidence: 99%

A Door-to-Door Survey of Bed Bug (Cimex lectularius) Infestations in Row Homes in Philadelphia, Pennsylvania

Tracy

Barbarin

et al. 2014

The American Society of Tropical Medicine and Hygiene

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“…For each species, we fitted heterogeneous Poisson models with the intensity function estimated as a log-linear function of second order polynomials of spatial coordinates x and y (Baddeley and Turner, 2006;De la Cruz, 2006;Liu et al 2007). This results in a nonstationary intensity function that accounts for the underlying spatial trend of each species, allowing the inhomogeneous K function to correctly estimate second order spatial dependencies.…”

Section: P2mentioning

confidence: 99%

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Olano¹,

Laskurain

Escudero

et al. 2009

Ann. For. Sci.

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Abstract• The density and identity of tree neighbourhood is a key factor to explain tree mortality in forests, especially during the stem exclusion phase.• To understand this process, we built a logistic model for mortality in a spatially explicit context, including tree and neighbourhood predictors. Additionally, we used this model to build mortality risk frequency distributions. Finally, we tested this model against a random mortality model to predict the spatial pattern of the forest.• Annual mortality rate was high for pedunculate oak (Quercus robur, 6.99%), moderate for birch (Betula celtiberica, 2.19%) and Pyrenean oak (Q. pyrenaica, 1.58%) and low for beech (Fagus sylvatica, 0.26%). Mortality risk models for pedunculate oak and birch included stem diameter, tree height, canopy position and neighbourhood. Mortality was affected by the specific nature of the neighbourhood showing a clear competitive hierarchy: beech > pedunculate oak > birch. Models based on random mortality and logistic regression model were able to predict the spatial pattern of survivors although logistic regression predictions were more accurate.• Our study highlights how simple models such as the random mortality one may obscure much more complex spatial interactions. Mots-clés :Ripley's K non homogènes / modèle logistique / forêt tempérée mixte / succession / mortalité des arbres Résumé -Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée ? Le rôle du voisinage.• La densité et l'identité des arbres du voisinage sont un facteur clé pour expliquer la mortalité d'arbres dans les peuplements forestiers, surtout pendant la phase d'exclusion du tronc.• Pour comprendre ce processus, nous avons construit un modèle logistique pour la mortalité dans un contexte spatialement explicite, en incluant l'arbre et des prédicteurs de voisinage. De plus, nous avons utilisé ce modèle pour construire des distributions de fréquence de risque de mortalité. Finalement, nous avons évalué ce modèle par rapport à un modèle de mortalité aléatoire pour prédire la structure spatiale de la forêt.• Le taux de mortalité annuelle était élevé pour le chêne pédonculé (Quercus robur, 6.99 %), modéré pour le bouleau (Betula celtiberica, 2.19 %) et le chêne tauzin (Q. pyrenaica, 1.58 %) et faible pour le hêtre (Fagus sylvatica, 0,26 %). Les modèles de risque de mortalité pour le chêne pédonculé et le bouleau intégraient le diamètre du tronc, la hauteur de l'arbre, la position du houppier et le voisinage. La mortalité a été affectée par la nature spécifique du voisinage montrant une hiérarchie claire dans l'aptitude compétitive : hêtre > chêne pédonculé > bouleau. Les modèles basés sur une mortalité aléatoire et le modèle logistique ont été capables de prédire la répartition spatiale des survivants bien que les prédictions du modèle logistique étaient plus précises.• Notre étude montre comment des modèles simples basés sur une mortalité aléatoire peuvent obscurcir des interactions spatiales beaucoup plus complexes.

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“…We find that GA is correlated with the spectral parameter η (Madgwick et al 2002), an indicator of the star formation rate of galaxies. Observations indicate that the lower star formation rate of group galaxies is visible out to 2R 200 (Balogh et al 1998), while ΛCDM numerical simulations show that particles that penetrate deep into dark matter halos travel out to 2.6R 200 (Gill et al 2005). In this work, we have found that galaxies represent two statistically distinct groups with a transition at η = −1.4 and d c = 1.5R 200 , a scale somewhat smaller (by 25%) than the observed radius for decreased star formation, but consistent with this value.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, we estimate K using the library spatstat (see Baddeley 2008) within the R statistical package. The command Kmeasure (spatstat) executes the following steps:…”

Section: Probing Anisotropy Around Groupsmentioning

confidence: 99%

Galaxy distribution and evolution around a sample of 2dF groups

Ribeiro¹,

Trevisan²,

Lopes³

et al. 2009

A&A

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Context. We study galaxy evolution and spatial patterns in the surroundings of a sample of 2dF groups. Aims. Our aim is to find evidence of galaxy evolution and clustering out to 10 times the virial radius of the groups and so redefine their properties according to the spatial patterns in the fields and relate them to galaxy evolution. Methods. Group members and interlopers were redefined after the identification of gaps in the redshift distribution. We then used exploratory spatial statistics based on the the second moment of the Ripley function to probe the anisotropy in the galaxy distribution around the groups. Results. We found an important anticorrelation between anisotropy around groups and the fraction of early-type galaxies in these fields. Our results illustrate how the dynamical state of galaxy groups can be ascertained by the systematic study of their neighborhoods. This is an important achievement, since the correct estimate of the extent to which galaxies are affected by the group environment and follow large-scale filamentary structure is relevant to understanding the process of galaxy clustering and evolution in the Universe.

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Modelling Spatial Point Patterns in R

Abstract: Summary. We 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.

Cited by 185 publications

References 65 publications

A Door-to-Door Survey of Bed Bug (Cimex lectularius) Infestations in Row Homes in Philadelphia, Pennsylvania

A Door-to-Door Survey of Bed Bug (Cimex lectularius) Infestations in Row Homes in Philadelphia, Pennsylvania

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Galaxy distribution and evolution around a sample of 2dF groups

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