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
Edith Gabriel <edith.gabriel@univ-avignon.fr>, Peter J. Diggle, Barry Rowlingson and Francisco J. Rodriguez-Cortes
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).
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.