Francesca Bruno
Researcher in statistics at the Department of Statistical Sciences, University of Bologna, Italy.
PhD in Statistical Methodology for Scientific Research at the Department of Statistical Sciences, University of Bologna, Italy.
Christian Haedo
Researcher in econominc statistics at the Research Center, University of Bologna, Argentina.
PhD in Statistical Methodology for Scientific Research at the Department of Statistical Sciences, University of Bologna, Italy.
Duración: 20 hs
Idioma: inglés
Statistical methods for spatial data have been an intense area of research in the last twenty years. Spatial statistics, as an area within statistics, grew out of numerous real-world problems (e.g., epidemiology, archaeology, climatology, medicine, criminology, ecology, forestry, geography, geology, and economy) and includes statistical methods applied to data that are spatially referenced. Most of these methods operate under the following idea: data collected over a region in space found close together tend to be more alike than points farther apart.
Much of the methodology developed for analyzing spatial data mimics that of analyzing time series data (data correlated over time), where the data have a natural temporal ordering. However, for data in two or more dimensions, no such ordering is generally present. This is the primary stumbling block preventing a straightforward extension of time series methods to spatial data.
The course will cover the methodology and modern developments for spatial modeling estimation and prediction, and goes beyond standard practices, exposes the researcher to many developments and state of the art techniques for spatial and spatiotemporal data. All the methods presented will be introduced in the context of a specific dataset, and then the motivation behind a particular method will be evident as it is developed.
The goal of this course is to provide an introduction to the range of statistical techniques used in the analysis of spatial data. The course focuses on exploration, description and modeling spatial data.
A tentative list of more specific topics is:
- Introduction to spatial statistics
- Point level models
- Areal (lattice) models
- Spatial point processes
- Estimation and modeling of spatial correlations
- Prediction and interpolation (kriging)
- Predicting at multiple sites
- Modeling spatial dependent data
- Modeling spatiotemporal data
- Finding spatial clusters
All topics will be introduced with examples using real data. Participants will learn how to use existing software with emphasis on analysis of real data from the environmental sciences and economics.
The investigation and modeling of spatial data is the focus of this course with a strong emphasis on the "hands-on" application of data utilizing spatial statistical techniques, which are discussed in class. The main software to be used is the statistical package R.
The course is organized into five broad topics, an outline of this course is sketched bellow.
1. Introduction to spatial data analysis
- Focus on main concepts
- Motivation for spatial analysis
- Distinguishing characteristics of spatial analysis
- Why spatial data analysis is different?
2. Geostatistics
- Spatial random field
- Spatial stationarity
- Variogram, semi-variogram
- Correlogram
- Range, sill, nugget
- Spherical and exponential variogram
- Optimal spatial prediction, kriging
3. Point Pattern Analysis
- Pattern
- First order statistics
- Nearest neighbor statistics
- Second order statistics
4. Lattice data and spatial regression analysis
- Specifying regression models with spatial multipliers and spatial externalities
- Simultaneous and conditional models (SAR and CAR)
- Maximum likelihood and instrumental
- Moran’s I test for regression residuals
- Lagrange multiplier tests
- Spatial specification searches
5. Spatiotemporal models
- Separability in spatiotemporal covariance functions
- Hierarchical models: classical and Bayesian approaches
- Dynamic state space models
Alma Mater Studiorum - Università di Bologna, Representación en Buenos Aires
Tel: (011) 4878 2900
E-mail
Costo total del curso: $AR 300.-
Las solicitudes de admisión podrán ser presentadas hasta el 9 de octubre de 2009. (Ver formulario de admisión para descargar)