- Docente: Francesca Bruno
- Credits: 5
- SSD: SECS-S/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in STATISTICS, ECONOMICS AND BUSINESS (cod. 8056)
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. Spatial Econometrics Models. Spatial Point Patterns analysis. Some notes on spatio-temporal models.
Spatial environmental applications by means of R 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