| stpp {stpp} | R Documentation |
Space-Time Point Pattern simulation, visualisation and analysis
Description
This package provides models of spatio-temporal point processes in a region S\times T and statistical tools for analysing global and local second-order properties of such processes. It also includes static and dynamic (2D and 3D) plots. stpp is the first dedicated unified computational environment in the area of spatio-temporal point processes.
The stpp package depends upon some other packages:
splancs: spatial and space-time point pattern analysis
rgl: interactive 3D plotting of densities and surfaces
rpanel: simple interactive controls for R using tcltk package
KernSmooth: functions for kernel smoothing for Wand & Jones (1995)
plot3D: Tools for plotting 3-D and 2-D data
ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics
Details
stpp is a package for simulating, analysing and visualising patterns of points in space and time.
Following is a summary of the main functions and the dataset in the stpp package.
To visualise a spatio-temporal point pattern
-
animation: space-time data animation. -
as.3dpoints: create data in spatio-temporal point format. -
plot.stpp: plot spatio-temporal point object. Either a two-panels plot showing spatial locations and cumulative times, or a one-panel plot showing spatial locations with times treated as a quantitative mark attached to each location. -
stan: 3D space-time animation.
To simulate spatio-temporal point patterns
-
rinfec: simulate an infection point process, -
rinter: simulate an interaction (inhibition or contagious) point process, -
rlgcp: simulate a log-Gaussian Cox point process, -
rpcp: simulate a Poisson cluster point process, -
rpp: simulate a Poisson point process, -
stdcpp: simulate a double-cluster point process, -
sthpcpp: simulate a hot-spot point process.
To analyse spatio-temporal point patterns
-
PCFhat: space-time inhomogeneous pair correlation function, -
STIKhat: space-time inhomogeneous K-function, -
ASTIKhat: Anisotropic space-time inhomogeneous K-function, -
LISTAhat: space-time inhomogeneous pair correlation LISTA funcrions. -
KLISTAhat: space-time inhomogeneous K LISTA functions. -
gsp: Spatial mark variogram function. -
gte: Temporal mark variogram function. -
kmr: Spatial r-mark function -
kmt: Temporal t-mark function. -
kmmr: Spatial mark correlation functionn. -
kmmt: Temporal mark correlation function.
Dataset
fmd: 2001 food-and-mouth epidemic in north Cumbria (UK).
Author(s)
Edith Gabriel <edith.gabriel@univ-avignon.fr>, Peter J. Diggle, Barry Rowlingson and Francisco J. Rodriguez-Cortes
References
Baddeley, A., Rubak, E., Turner, R. (2015). Spatial Point Patterns: Methodology and Applications with R. CRC Press, Boca Raton.
Chan, G. and Wood A. (1997). An algorithm for simulating stationary Gaussian random fields. Applied Statistics, Algorithm Section, 46, 171–181.
Chan, G. and Wood A. (1999). Simulation of stationary Gaussian vector fields. Statistics and Computing, 9, 265–268.
Diggle P. , Chedwynd A., Haggkvist R. and Morris S. (1995). Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4, 124–136.
Diggle, P.J., 2013. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. CRC Press, Boca Raton.
Gabriel E. (2014). Estimating second-order characteristics of inhomogeneous spatio-temporal point processes: influence ofedge correction methods and intensity estimates. Methodology and computing in Applied Probabillity, 16(1).
Gabriel E., Diggle P. (2009). Second-order analysis of inhomogeneous spatio-temporal point process data. Statistica Neerlandica, 63, 43–51.
Gabriel E., Rowlingson B., Diggle P. (2013). stpp: an R package for plotting, simulating and analyzing Spatio-Temporal Point Patterns. Journal of Statistical Software, 53(2), 1–29.
Gneiting T. (2002). Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association, 97, 590–600.
Gonzalez, J. A., Rodriguez-Cortes, F. J., Cronie, O. and Mateu, J. (2016). Spatio-temporal point process statistics: a review. Spatial Statiscts, 18, 505–544.
Siino, M., Rodriguez-Cortes, F. J., Mateu, J. and Adelfio, G. (2017). Testing for local structure in spatio-temporal point pattern data. Environmetrics. DOI: 10.1002/env.2463.
Stoyan, D., Rodriguez-Cortes, F. J., Mateu, J., and Gille, W. (2017). Mark variograms for spatio-temporal point processes. Spatial Statistics. 20, 125-147.