Spatial Point Patterns: Methodology and Applications with R
Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th
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alternative analysis applied approximately argument assume calculated called Chapter cluster column command completely computed conditional containing coordinates correction counts covariate data points dataset default defined density depends described distance distribution edge effect envelopes equal estimate example expected extract factor Figure fitted fitted model formula function Gibbs given gives homogeneous independent indicates inhomogeneous intensity intensity function interaction interpretation K-function likelihood linear locations marks mean measure method multitype names nearest number of points object object of class observed obtained original package parameters performed pixel plot point pattern point process point process model Poisson process possible probability quadrat random range realisation region regression represents residual result sampling scale shown shows significance simulated smoothing spatial spatstat specified square standard stationary statistical Strauss Table term trees trend true unit values variable vector window