stpp Documentation
Space-Time Point Pattern Simulation, Visualisation and Analysis
Edith Gabriel, Peter J Diggle, Barry Rowlingson and Francisco J Rodriguez-Cortes
2018-02-13
Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in ‘stpp’. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function among others. It also includes tools to get static and dynamic display of spatio-temporal point patterns.
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., 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.
Gabriel E., Diggle P. (2009). Second-order analysis of inhomogeneous spatio-temporal point process data. Statistica Neerlandica, 63, 43–51.
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(2), 411–431.
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.