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GEOSTAT tutorials

GEOSTAT software

Software iconList of FOSS software used in this course and installation instructions. Follow these instructions to prepare and customize the software before the beginning of the course.

Literature used

ASDAR bookList of books / lecture notes used in this course. See also: CRAN Task View: Analysis of Spatial Data.


« February 2017 »

Gomez Rubio: Spatial and Spatiotemporal point process analysis in R

Date & Time: Tuesday 20 September 13:30 -14:30; Thursday 22 September 9:00 - 12:30

Objective: This session will provide an introduction to the analysis spatial and spatio-temporal point patterns with R.

General description: The aim of this session is to provide an introduction to the analysis of spatial and spatio-temporal point patterns with R. In particular, for spatial point patterns the session will cover estimation of first and second-order properties, which will be used to assess clustering, regularity or complete randomness of a point pattern. For spatio-temporal point patterns the session will introduce some classes for space-time data and then show how to estimate the intensity of the point pattern at different time period. Finally, the link between point patterns and Poisson processes is establisehd and a Bayesian spatio-temporal modeling is done with the R-INLA package.

Required back-ground knowledge: Knowledge of the R software and the sp, spacetime and spatstat packages will help to follow this session.

Software / R packages required: sp, spacetime, spatstat, and others. A script is provided to download all the required packages (and possibly more...).


  • Bivand et al. (2013) Applied Spatial Data Anlaysis with R. New York: Springer.
  • Baddeley et al. (2015). Spatial Point Patterns -  Methodology and applications with R. Boca Raton: CRC Press.


R script to install all the required packages for this session (and possibly more...)507 bytes
slides6.78 MB
R code (used in the slides)35.96 KB
Practical 1 (spatial point patterns)74.35 KB
Practical 2 (spatio-temporal point patterns)77.06 KB
Dataset for practicals (Incidents in San Francisco)6.72 MB
datasets.zip9.42 MB
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