stpp-package: Space-Time Point Pattern simulation, visualisation and...

Description Details Author(s) References

Description

This package provides models of spatio-temporal point processes in a region S x 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

To simulate spatio-temporal point patterns

To analyse spatio-temporal point patterns

Dataset

fmd: 2001 food-and-mouth epidemic in north Cumbria (UK).

Author(s)

Edith Gabriel <[email protected]>, 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.


stpp-GitHub-community/stpp documentation built on April 19, 2018, 12:12 p.m.