- Docente: Francesca Bruno
- Credits: 5
- SSD: SECS-S/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LS) in Statistical and Economic Sciences (cod. 0211)
Learning outcomes
Students reach the basic instruments to analyse and
interpret a spatial dataset with particular attention to the trend
component and the small scale variation component
estimation.
Course contents
Statistics for spatial data and spatial autocorrelation analysis. Graphical tools for spatial data. Geostatistical approach, lattice data analysis and spatial point patterns analysis. Spatial prediction and interpolation. Variogram, kriging and cokriging estimation. Stationanity and isotropy assumptions. Non-stationary spatial processes. Some notes on spatio-temporal models, with particular focus on separability assumption.
Spatial environmental applications by means of R and S-PLUS software.
Readings/Bibliography
Cressie N.A. (1993) Statistics for Spatial Data, Wiley. ·
Millard and Neerchal (2001) Environmental Statistics with S-Plus, CRC Press.
Kitanidis (1999) Introduction to geostatistics, Cambridge University Press
Power point slides
Teaching methods
Some individual exercises (both theoretical and practical)
will be useful to understand and apply the basic spatial methods. A
continuous discussion will help the students to deep many aspects
of the spatial data analysis in order to be able of realize
practical solutions of many problems.
Assessment methods
The final exam will be on a spatial problem analysis of a
particular dataset, and an oral discussion.
Teaching tools
informatic laboratory, software R
Office hours
See the website of Francesca Bruno