The spatial dimension of the data and their representation : the geo-referenced data. The topological relationships between data. The types of data. The statistical sources of spatial data. Introduction to statistical methods for the analysis of spatial data ( punctual and areal ). Exploratory spatial analysis (ESDA). Spatial correlation.
The textbooks will be announced at the beginning of classes.
Learning Objectives
The course objective is to offer an introduction to logic and practice exploratory analysis of spatial data including the use of open source software. After a brief presentation of the specific characteristics of spatial data and their analysis, the course explains - in terms of both formal and practical - some of the ways in which such data can be explored in order to draw useful insights on the phenomena under study with particular reference to tourism phenomena. At the end of the course participants will be able to view and describe quantitatively the configuration of spatial phenomena of interest .
Prerequisites
Prerequisites of this course includes an understanding of basic algebra and general statistics (e.g., knowledge of statistical significance)
Teaching Methods
Lectures and lab sessions
Further information
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Type of Assessment
Oral examination. It is required a report/presentation assigned by the instructor.
Course program
What are the spatial data.
Spatial data types.
The spatial contiguity.
Spatial weighting matrices.
The statistical sources of spatial data.
Global and local autocorrelation.
Global indices of spatial autocorrelation and spatial correlogram.
Indices of spatial autocorrelation and local Moran scatterplot.
Spatial clustering.
Outline of advanced techniques for the analysis of spatial data.
Potential and limits of spatial analysis .