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Statistical Methods for Spatial Data Analysis and Applications | 2012

Tercera edición del curso que brindará los más recientes desarrollos metodológicos para "spatial modeling estimation and prediction" y ofrecerá un panorama del estado del arte de las técnicas para "spatial and spatio-temporal data" con ejemplos de aplicaciones reales en ciencias ambientales, epidemiología, economía, geomarketing y medicina.

El curso otorga 10 créditos europeos universitarios (ECTS - European Credit Transfer and Accumulation System).

Organizado por la UniBo-BA y el Departamento de Ciencias Estadísticas de la Universidad de Bolonia, Italia.

Profesores

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.

Fedele Greco
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 spatial and econominc statistics at the Interuniversity Research Centre on Economic Development, Territory and Institutions (CIDETI), University of Bologna, Argentina.
PhD in Statistical Methodology for Scientific Research at the Department of Statistical Sciences, University of Bologna, Italy.

Massimo Ventrucci
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.

Alejandra Epíscopo
GIS Tech Support & Training Manager of Aeroterra S.A., Argentina.
Bachelor of Computer Information Systems, Universidad de Belgrano, Argentina.

Introducción

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., environmental, epidemiology, medicine, criminology, ecology, forestry, geography, geology, archaeology, economics and marketing) 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.

Objetivos

The goal of this course is to provide an introduction to the range of statistical techniques used in the analysis of spatial and spatiotemporal 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; and 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
  • Finding reduced rank structures in spatio-temporal datasets
  • Modeling and estimation of common spatial factor models.

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, epidemiology 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 packages R, Winbugs, GeoDa and ArcGIS.

Contenidos del curso

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

2. Geostatistics

  • Spatial random field
  • Spatial stationarity
  • Variogram, semi-variogram: estimation of the variogram, and variogram model fitting
  • Spatial prediction, kriging
  • Applications: environmental science

3. Point Pattern Analysis

  • Types of data: points, marks and covariates
  • Intensity, interaction, covariate effects, segregation and dependence
  • Investigating intensity and tests of Complete Spatial Randomness (CSR)
  • Maximum likelihood for Poisson processes and checking a fitted Poisson model
  • Distance methods for point patterns and inference using summary statistics
  • Marked point patterns and multitype Poisson models
  • Identify spatial clusters
  • Applications: economics and geomarketing

4. Areal (lattice) data and spatial regression analysis

  • Descriptive measures of spatial correlation
  • Specifying regression models with spatial multipliers and spatial externalities
  • Simultaneous and conditional models (SAR and CAR) and Maximum likelihood
  • Hierarchical models: Bayesian approache
  • Moran’s I test for regression residuals
  • Lagrange multiplier tests
  • Applications: epidemiology and economics

5. Spatiotemporal models

  • Separability in spatiotemporal covariance functions
  • Hierarchical models: classical and Bayesian approaches
  • Dynamic state space models
  • Applications: environmental science, epidemiology, medicine and economics

6. Non-parametric statistics for spatial data analysis

  • Non-parametric regression models: local linear regression and spline regression
  • Penalized spline regression models: roughness penalties, fitting algorithms and spatial smoothing
  • Applications: medicine, environmental science and urban geography

7. Spatial statistics with ArcGIS

  • Introduction to GIS, ArcGIS a complete system
  • Specify the characteristics of geographic data needed to perform common GIS tasks
  • Geocoding process to create GIS data
  • Spatial data analysis: structured approach to data analysis, interpolation methods, and surface types
  • Regression analysis techniques to examine how phenomena vary over space, predict where phenomena may occur, and help explain the factors behind observed spatial patterns
  • Create and distinguish between prediction maps, standard error maps, quantile maps, and probability maps.

Idiomas

Inglés y español

Calendario

Noviembre 2012

Fecha cursada: 12 al 28 de noviembre de 2012, de 18 a 22 hs.

Duración: 50 hs.

Lugar: Laboratorio de Informática del Alma Mater Studiorum - Università di Bologna, Representación en la República Argentina.

Las solicitudes de admisión podrán ser presentadas hasta el 19 de octubre de 2012 (ver formulario de admisión disponible para descargar).

Dirección: Francesca Bruno, Universidad de Bolonia - Italia
Codirección: Christian Haedo, Universidad de Bolonia - Argentina.

Arancel

Costo total del curso: AR$ 5.350

Requisitos de Admisión

El curso está dirigido a graduados universitarios que posean los siguientes títulos: Estadística, Matemática, Ciencias Económicas, Ingeniería, Sociología, Sistemas de Información, Ciencias Ambientales, Geografía. Otros títulos de grado, previa evaluación del Curriculum Vitae por parte del Comité de Evaluación.

El mencionado título universitario, para que sea válido para participar al curso, debe haber sido obtenido para el día de la inscripción, antes del inicio de la actividad didáctica.

Las solicitudes de inscripción deberán comprender:

  • Formulario de admisión completo con una fotografía 4X4 (frente);
  • Fotocopia del DNI, cédula de identidad o pasaporte;
  • Original y fotocopia del Diploma Universitario donde figure la legalización del Ministerio de Educación o Certificado de título en trámite;
  • Original y fotocopia del Certificado Analítico de estudios universitarios donde figure la legalización del Ministerio de Educación;
  • Carta de presentación del empleador y/o de la Facultad en la cual el candidato ha conseguido el diploma universitario y/o de un referente reconocido en el ámbito temático;
  • Curriculum vitae completo, conteniendo la lista detallada de los títulos eventuales obtenidos;
  • Carta de motivación, a través de la cual sea posible deducir los intereses y expectativas del candidato en relación a su participación en el Master;
  • Carta compromiso de terceros (eventual) que solventen el pago del costo;

Para los graduados de la Universidad de Bologna, el certificado universitario se ocupará de conseguirlo la Secretaría del Master.

Indicaciones para los estudiantes con título universitario obtenido en países que no sean Italia
Antes de finalización del Master, el estudiante tendrá que presentar una copia autenticada del certificado del Diploma Universitario por la Representación Diplomática, traducido y legalizado, acompañado por la Declaración de Valor realizada en la sede de la Representación Diplomática Italiana del País donde se ha obtenido, completo con la nota obtenida del título universitario.

La solicitud de admisión, completada en forma manuscrita en letra imprenta o en computadora y con la firma original del candidato, adjuntando toda la documentación solicitada, deberá:

  • ser enviada a Alma Mater Studiorum-Università di Bologna, Representación en la República Argentina, a la atención de la Secretaría General, Marcelo T. de Alvear 1149 - (C1058AAQ) - CABA - Argentina.
  • ser presentada personalmente a la Secretaría General del Alma Mater Studiorum-Università di Bologna, Representación en la República Argentina cita en Marcelo T. de Alvear 1149 - (C1058AAQ) - CABA - Argentina. Horario de atención: 10 a 18 hs.

Todos los documentos mencionados con anterioridad deberán ser presentados antes del vencimiento de la presente convocatoria: 19 de octubre de 2012.

Informes e inscripción
Alma Mater Studiorum - Università di Bologna, Representación en la República Argentina.
Marcelo T. de Alvear 1149 (C1058AAQ), Buenos Aires - Argentina
Tel: +54 11 4570-3000 int. 191, de 15 a 19 hs.
E-mail: spatial@unibo.edu.ar