Spatial Point Patterns: Methodology and Applications with R

By V. Gómez‐Rubio. 2016

1,377 citations recorded by the source corpus.

A spatial pattern records the locations of events produced by an underlying spatial process in a study region. Examples include the location of trees in a forest, cases of a disease in a country

Subfields: Artificial Intelligence, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c5e9efc4cd591226f2b1344a18737399ce98425d

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Citations in the corpus

ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms (2014, methodology): “Using the R package spatstat (Baddeley, Rubak, & Turner, 2015), a Gaussian kernel smoothing is applied to either a continuous or binary resistance surface prior to applying one of the eight transformations.”
Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning (2017, methodology): “Neighboring position estimates having a distance of less than or equal to 50 cm were treated as a single tree position and merged with the R-package spatstat [36].”
Spatiotemporal wildfire modeling through point processes with moderate and extreme marks (2021, result): “We thus obtain approximately 50, 000 observations in the subsample, in line with the rule of thumb of Baddeley et al. (2014, 2015) that the subsample should be at least a factor four larger than the number of event points.”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “The bandwidth for each kernel was estimated using cross-validation to minimize mean square error (Baddeley et al. 2015).”
Large-area imaging reveals biologically driven non-random spatial patterns of corals at a remote reef (2017, methodology): “For each taxonomic and functional morphology group in each plot, we used a simulation procedure to compare the observed spatial patterns against each of the null models outlined above using the pair correlation function (PCF), a non-cumulative second order summary statistic (Baddeley et al. 2015).”
Spatial patterns of ponderosa pine regeneration in high-severity burn patches (2017, positive): “We also tested for di ﬀ erences in stem density in each individual plot with distance from forest edge by using a Thomas process (kppm: cluster process model) model that evaluates Poisson-distributed parent clusters with o ﬀ spring distributed as bivariate normal (Baddeley et al., 2015).”
Coactivator condensation at super-enhancers links phase separation and gene control (2018, positive): “This black and white image is used as input for a custom R script that uses readTIFF and im (from spatstat (72)) to select 40 random nuclear voxels per image set.”
Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, methodology): “Lower dimension approximations (see Banerjee et al., 2013; Carlin et al., 2007) may be used to deal with the GP prior and speed the computation of covariance matrices, their inverses and their Cholesky decompositions - all required for the MCMC. This type of approximation is on the second level of t”
Spatial modelling with R-INLA: A review (2018, background): “However, in many cases the usual assumption of either “window sampling” or the “small world model” (Baddeley et al., 2015) does not necessarily hold.”