Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

Diggle, Peter J.

doi:10.1201/b15326

Cited by 947 publications

(1,494 citation statements)

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“…In particular, the assumption of stationarity ('spatial homogeneity') is an essential requirement for many of the classical methods for spatial point pattern analysis [22,26,28,55,56,58,64,65]. It seems clear that many real point patterns cannot be described as stationary [51,66], and the use of the classical methods on such data would be invalid.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

“…(see e.g. [28,65]). A key feature of spatstat is its generic algorithm for parametric model-fitting of spatial point process models to point pattern data.…”

Section: Scopementioning

confidence: 99%

“…We assume the reader is conversant with basic ideas of spatial point pattern analysis [28,65] and with the R language [40,38,53].…”

Section: The Spatstat Packagementioning

confidence: 99%

“…General principles of EDA are outlined in [15,18,24,68,72]. Numerous tools for exploratory analysis of spatial point pattern data are surveyed in [22,26,28,56,58,64,65,72].…”

Section: Exploratory Data Analysismentioning

confidence: 99%

“…A wide choice of summary statistics is now available [22,28,58,65,64]. In spatstat the available choices include Kest, which estimates the K function [55,56] [65,Chapter 15]; Jest, the function J(r) = (1 − G(r))/(1 − F (r)) of [70]; Kmeasure, the reduced second moment measure [9,10], [65, pp.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

See 4 more Smart Citations

Modelling Spatial Point Patterns in R

Baddeley

Turner

Case Studies in Spatial Point Process Modeling

185

225

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Section: Exploratory Data Analysismentioning

confidence: 99%

“…(see e.g. [28,65]). A key feature of spatstat is its generic algorithm for parametric model-fitting of spatial point process models to point pattern data.…”

Section: Scopementioning

confidence: 99%

“…We assume the reader is conversant with basic ideas of spatial point pattern analysis [28,65] and with the R language [40,38,53].…”

Section: The Spatstat Packagementioning

confidence: 99%

“…General principles of EDA are outlined in [15,18,24,68,72]. Numerous tools for exploratory analysis of spatial point pattern data are surveyed in [22,26,28,56,58,64,65,72].…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Section: Exploratory Data Analysismentioning

confidence: 99%

See 3 more Smart Citations

Modelling Spatial Point Patterns in R

Baddeley

Turner

Case Studies in Spatial Point Process Modeling

185

225

View full text Add to dashboard Cite

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Intraspecific interactions affect the spatial pattern of a dominant shrub in a semiarid shrubland: A prospective approach

et al. 2018

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Dispersal, physical conditions and biotic interactions contribute to determine the spatial distribution of individuals in plant populations. Much of what we know has been learned from studies that retrospectively posit mechanisms presumed to have generated the observed spatial patterns. Here we present a prospective approach. We start by measuring spatial demographic effects and evaluate if they can generate observed spatial patterns. We evaluated the influence of interactions among conspecifics on vital rates, demography and spatial distribution of Croton aff. wagneri, a dominant shrub in dry Andean ecosystems. Recruitment, survival and growth varied in relation with distance to conspecifics neighbours and with their summed cover. We built a spatial individual‐based model and simulated its population dynamics in 30 × 30 m plots for a 30‐year period. We compared the predicted spatial pattern from these demographic models with that observed among plants in 16 independent plots with the same area. Simulated populations mimicked observed spatial patterns, although in plots at high elevations the simulated populations did not reproduce the observed inhibition at small scales. Observed and simulated patterns indicated differences between elevations in maximum aggregation and location of the distances with higher aggregation. We discuss how consideration of critical seed and juvenile stages and interspecific interactions could further improve our understanding of spatial pattern and recommend that these factors be considered in future models.

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Detecting plant spatial pattern change after disperser loss: A simulation and a case study

et al. 2019

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Disruption of seed dispersal processes may affect plant population spatial structure. We used a spatial simulation model and an empirical case study to assess the conditions under which the loss of seed dispersers has a detectable effect on a species' spatial pattern. Our simulation experiments suggested that detecting spatial change following disperser loss will be difficult, except when rates of fruit removal are initially high and then completely disappear. To contextualize the simulation modeling, we used spatial point pattern analyses to characterize the spatial pattern of two large-seeded species (Leucopogon nutans, a fire-killed seeder shrub and Macrozamia riedlei, a long-lived, resprouting cycad) in the jarrah (Eucalyptus marginata) forests of southwestern Australia. The plant species' primary disperser, the emu (Dromaius novaehollandiae), was absent from one of the sites we considered, but present at the other two. There was no detectable difference for either plant species in the strength of aggregation between sites with and without emu. However, even if disperser loss may not greatly affect local spatial structure for most plant species, it is likely to be important for long distance dispersal and genetic structuring of populations, so accurate characterization of the dispersal kernel is critical, especially in terms of plant emigration. K E Y W O R D Sdefaunation, plant population structure, seed dispersal, spatial statistics, virtual ecology

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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

Cited by 947 publications

References 0 publications

Modelling Spatial Point Patterns in R

Modelling Spatial Point Patterns in R

Intraspecific interactions affect the spatial pattern of a dominant shrub in a semiarid shrubland: A prospective approach

Detecting plant spatial pattern change after disperser loss: A simulation and a case study

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