The environmental phenomena, but not only, have very often a spatial component that can not be neglected in their analysis.
The aim of the course is to introduce students to techniques statistics collection, description and analysis of spatial data (data where there is a spatial dependence) and to allow students to
acquire the technical skills to address the problem of management and development of geographic information.
- Bailey TC, Gatrell AC (1995) Interactive Spatial Data Analysis, Longman.
- Bivand RS, Pebesma EJ, Gomez-Rubio V (2008) Applied Spatial Data Analysis with R, Springer.
Learning Objectives
The course aims to introduce students to the main techniques used for the analysis of data where it is present to a significant spatial dependence.
Prerequisites
Preparatory teaching: Statistical Inference.
Teaching Methods
Lessons of frontal teaching in the classroom and in the laboratory.
Further information
e-learnig Moodle
Type of Assessment
Oral examination.
Course program
- Introduction to spatial statistics.
- Stochastic spatial processes and their properties.
- Point Process data: kernel estimate of the intensity, first-order nearest neighbor distance methods (functions F and G) and K function for the estimation of the intensity of the second order. Marked point processes. Spatial cluster detection.
- Data or geodata random surface: methods for estimate of the area such as moving averages spatial kernel, tessellation. Variogram-covariogram and correlogram models for variogram and covariogram, trend surface analysis, kriging.
- Area Data: Moran's I for spatial autocorrelation, conditional autoregressive models simultaneous SAR and CAR, CAR Bayesian models. Ecological regression and Geographical Weighted Regression. Spatial cluster detection.
- Spatial interaction Data: gravity models.
In the laboratory will take presented some libraries of the R softhware for the description,
representation, spatial data analysis.