77093 - Environmental Statistics

Learning outcomes

By the end of the course, the student has gained basic knowledge about the role of statistical methods for analysing environmental phenomena, mainly from a spatial perspective. The student is led through the fundamentals of the three branches of spatial statistics: areal, geostatistical and point process data are considered, from both a descriptive and modelling point of view. The student is able to apply the statistical methods by using specific R packages.

Course contents

1. Analysis of areal data

   Global measures of spatial association. Local Indicators of Spatial Association (LISA). Hypothesis testing for global and local spatial association. Spatial linear regression. Spatial smoothing of mortality rates.

2. Analysis of point process data

   Introduction to point processes. Descriptive measures for point patterns. Tests for complete spatial randomness and interpoint interaction. Homogeneous Poisson models. Inhomogeneous Poisson models. Point process models evaluation.

3. Analysis of geostatistical data (based on the remaning time for the course)
   Geostatistics: descriptive measures of spatial dependence, spatial random fields, moments of a spatial random field, variogram, covariogram, kriging.
   

Readings/Bibliography

Bivand R.S., Pebesma E., Gómez-Rubio V. (2013) Applied Spatial Data Analysis with R. Springer.

A. Baddeley, Analysing spatial point patterns in R. Downloadable at https://research.csiro.au/software/r-workshop-notes/

Teaching methods

Lectures with slides, and tutorials with the R software in the computer lab

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 test in the computer lab, which lasts 2 hours. The student has to answer some theoretical questions on a paper sheet, and some practical questions using the R software. At the end of the exam, both the paper sheet and an R script must be delivered for evaluation.

Teaching tools

Material available on Virtuale: slides, lecture notes, excercises and mock exam.

Use of R and Rstudio in the lab

Availability of the teacher during office hourse and via email.

Office hours

See the website of Linda Altieri