Advanced statistical methods in geography

Teacher(s)

Hafner Christian;

Language

French

Content

1. Spatial data and representation in R
2. Spatial point processes
3. Classical linear regression
4. Spatial linear regression
5. Interpolation and spatial prediction
6. Spatio-temporal data
7. Discrete choice models

Teaching methods

lectures on theory and illustrations on computers

Evaluation methods

The evaluation is based on a personal project on a realistic dataset, and an oral exam.

Other information

Prerequisite GEO1341 Modélisation statistique en géographie (or similar).

Online resources

Every note, chunk of R code, or dataset used in the course is available on the moodle site associated with this course.
MOOC "Introduction à l'économétrie" sur edx (ouvert en self pace) :
https://learning.edx.org/course/coursev1: LouvainX+Louv14x+3T2024/home
Vidéos, exercices, études de cas, implémentation des méthodes en R

Bibliography

• L Anselin, Spatial Econometrics, Kluwer, 1988.
• R Bivand, E Pebesma and V Gomez-Rubio, Applied Spatial Data Analysis with R, Springer, New York, 2nd edition 2013.
• Crawley, Statistics : An Introduction Using R, Wiley, 2005.Web site: http://www.imperial.ac.uk/bio/research/crawley/statistics
•  Wikle, C.K., Zammit-Mangion, A. and Cressie, N. (2019), Spatio-Temporal Statistics with R, CRC Press.
•  J LeSage and RK Pace, Introduction to Spatial Econometrics, Chapman & Hall/CRC, Boca Raton, Florida, 2009.
•  O Schabenberger and C Gotway, Statistical Methods for Spatial Data Analysis, Chapman & Hall, 2005.

Faculty or entity

> GEOG