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Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.
The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach.
This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.
Table of Contents
Chapter 1 – Introduction to spatial data
Chapter 2 – Structuring spatial relationships: role of the weighing matrix
Chapter 3 – Exploratory analysis methods of datato detect patterns of spatial dependence between observations. (Global and local) spatial autocorrelation is formally tackled and described and the statistics to detect these patterns are also presented. The “problem” related to the presence of spatial autocorrelation can lead to complications in the analyzes of conventional multiple linear regressions.
Chapter 4 – Modeling of spatial effects
Chapter 5 – Spatio-temporal considerations
Chapter 6 – Conclusion