A data science journey through the analysis of spatio-temporal point pattern data. Spatial Data Handling and Modelling Using R. 2400-ZEWW901

The course will be taught in an intensive workshop setting in two weeks in November. The students are asked to bring their own laptops with R v.3.3.0+ and RStudio Desktop installed in order to take active part in the practical live code exercises discussed during the class.

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The first part of the course:
Instructor:
Prof. Jorge Mateu
Department of Mathematics
University Jaume I of Castellon, Spain
mateu@uji.es
https://www3.uji.es/~mateu/

The list of course topics:
Important: scope of the course will be adapted to the needs and interests of the students.
1. Introduction and motivation. Real data examples with a focus on infectious diseases and crime
2. Some basic technical background. Spatio-temporal point process statistics
3. Non-parametric intensity estimation for spatial point patterns with R
4. Poisson, and mecanistic versus empirical models
5. A deeper look at mechanistic spatio-temporal modeling frameworks
6. Spatio-temporal point process models based on neural kernels
7. Semi-parametric spatio-temporal Hawkes-type point processes with periodic background
8. A deeper look at empirical mdels through log-Gaussian Cox processes
9. Origin-destination point patterns
10. Reducing dimensionality through barycenters
11. Velocities for spatial growth models
12. Stochastic integro-differential equations
13. Detecting focusses and generators
14. Statistical learning for spatio-temporal point processes

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The second part of the course:
Instructor:
Prof. Christopher Brunsdon
Director of the National Centre for Geocomputation
Maynooth University, Ireland
Christopher.Brunsdon@mu.ie

The list of course topics:
1. Spatial Data in R
2. Some spatial algorithms
1. Trip modelling
2. Location allocation models
4. Spatial regression in R
5. Markov random field models
6. Bayesian Approaches
7. Real world computing issues
8. Challenges for spatial data analytics

Both parts of the course will be complementary and will provide the students with a broad overview of the spatial data and spatio-temporal data analysis issues and their applications in R.