- Docente: Fedele Pasquale Greco
- Credits: 6
- SSD: SECS-S/01
- Language: Italian
- Moduli: Fedele Pasquale Greco (Modulo 1) Linda Altieri (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
- Corso: First cycle degree programme (L) in Statistical Sciences (cod. 8873)
Learning outcomes
By the end of the course, the student should have gained basic knowledge about the role of statistical methods for analysing environmental phenomena. In particular, the student will be introduced to the analysis of geostatistical data and should be able to describe and model the spatial structure of an environmental data set, performing spatial prediction by means of kriging methods. Moreover, the student should be provided with introductory theory concerning extreme value distributions for analysing extreme environmental phenomena. The student should be able to apply all the statistical methods by using specific R packages
Course contents
Analysis of areal data
Global measures of spatial association. Local Indicators of Spatial Association (LISA). Hypothesis testing for global and local spatial association. Areal data models (CAR and SAR). Spatial linear regression. Spatial smoothing of mortality rates.
Analysis of geostatistical data
Geostatistics: descriptive measures of spatial dependence, spatial random fields, moments of a spatial random field, variogram, covariogram, kriging.
Analysis of point process data
Introduction to point processes. Descriptive measures for point patterns. Tests for complete spatial randomness. Interpoint interaction. Homogeneous Poisson models. Inhomogeneous Poisson models. Marked point processes. Point process models evaluation.
Readings/Bibliography
Bivand R.S., Pebesma E., Gómez-Rubio V. (2013) Applied Spatial Data Analysis with R. Springer.
Illian J.B. et al (2008) Statistical Analysis and Modelling of Spatial Point Patterns
Diggle P.J. (2014) Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. Third Edition
Teaching methods
Lectures and tutorials
Assessment methods
The final exam aims at evaluating the achievement of the following educational targets:
- deep knowledge of the topics covered along the course
- ability to analyse spatial data
- ability to implement statistical methods suited for spatial analysis in R.
The exam consists of a practical test in the computer lab and a facultative oral exam.
Office hours
See the website of Fedele Pasquale Greco
See the website of Linda Altieri