Computational Issues and R Packages for Spatial Data Analysis

Authored by: Marta Blangiardo , Michela Cameletti

Handbook of Spatial Epidemiology

Print publication date:  April  2016
Online publication date:  April  2016

Print ISBN: 9781482253016
eBook ISBN: 9781482253023
Adobe ISBN:

10.1201/b19470-29

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Abstract

As seen in previous chapters, several types of models are used with spatial data, depending on the aim of the study. If we are interested in summarizing spatial variation between areas using risks or probabilities, then we could rely on statistical methods such as disease mapping to compare maps and identify clusters (see Ugarte et al. 2005 and Chapters 3 and 28 in this book). Moran’ s Index is extensively used to check for spatial autocorrelation (Moran 1950), while the scan statistics, implemented in SaTScan (Kulldorf 1997), performs cluster detection and geographical surveillance. The same type of tools can also be adopted in studies where there is an aetiological aim to assess the potential effect of risk factors on outcomes (see Chapter 5).

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