Lesson 3: Spatio-Temporal Point Patterns Analysis

Published

August 31, 2024

Modified

September 7, 2025

A spatio-temporal point process (also called space-time or spatial-temporal point process) is a random collection of points, where each point represents the time and location of an event. Examples of events include incidence of disease, sightings or births of a species, or the occurrences of fires, earthquakes, lightning strikes, tsunamis, or volcanic eruptions. In this lesson, you will learn the basic concepts and methods of Spatio-temporal Point Patterns Analysis. You will also gain hands-on experience on using these methods to discover real-world point processes.

Content

  • Spatio-temporal Point Processes
    • Basic concepts of spatio-temporal point processes.
    • Examples of real-world spatio-temporal point processes.
  • Spatio-temporal Point Patterns Analysis methods
    • Spatio-temporal Kernel Density Estimation (STKDE)
    • Spatio-temporal K-functions

Lesson Slides and Hands-on Notes

References

  • Jonatan A. González, et. al. (2016) “Spatio-temporal point process statistics: A review”, Spatial Statistics, Volume 18, Part B, November 2016, Pages 505-544.
  • Alexander Hohl et. al.“Spatiotemporal Point Pattern Analysis Using Ripley’s K” in Geospatial Data Science Techniques and Applications.
  • Tonini, Marj et. al. (2017) “Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps”, Natural hazards (Dordrecht), 2017-02, Vol.85 (3), p.1489-1510. Available at SMU eJournal.
  • Juan, P et. al. (2012) “Pinpointing spatio-temporal interactions in wildfire patterns”, Stochastic environmental research and risk assessment, 2012-12, Vol.26 (8), p.1131-1150. Available at SMU eJournal.

All About R