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Figure 1. 1. Map of Great Britain at two different scale levels. (a) Counties, (b)Regions. '-. " Figure 1. 2. Two alternative aggregations of the Italian provincie in 32 larger areas 4 CHAPTER 1 d . , b) Figure 1. 3 Percentage of votes of the Communist Party in the 1987 Italian political elections (a) and percentage of population over 75 years (b) in 1981 Italian Census in 32 polling districts. The polling districts with values above the average are shaded. Figure 1. 4: First order neighbours (a) and second order neighbours (b) of a reference area. INTRODUCTION 5 While there are several other…mehr

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Figure 1. 1. Map of Great Britain at two different scale levels. (a) Counties, (b)Regions. '-. " Figure 1. 2. Two alternative aggregations of the Italian provincie in 32 larger areas 4 CHAPTER 1 d . , b) Figure 1. 3 Percentage of votes of the Communist Party in the 1987 Italian political elections (a) and percentage of population over 75 years (b) in 1981 Italian Census in 32 polling districts. The polling districts with values above the average are shaded. Figure 1. 4: First order neighbours (a) and second order neighbours (b) of a reference area. INTRODUCTION 5 While there are several other problems relating to the analysis of areal data, the problem of estimating a spatial correlO!J'am merits special attention. The concept of the correlogram has been borrowed in the spatial literature from the time series analysis. Figure l. 4. a shows the first-order neighbours of a reference area, while Figure 1. 4. b displays the second-order neighbours of the same area. Higher-order neighbours can be defined in a similar fashion. While it is clear that the dependence is strongest between immediate neighbouring areas a certain degree of dependence may be present among higher-order neighbours. This has been shown to be an alternative way of look ing at the sca le problem (Cliff and Ord, 1981, p. l 23). However, unlike the case of a time series where each observation depends only on past observations, here dependence extends in all directions.

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Autorenporträt
Giuseppe Arbia is a full professor of Economic Statistics at the Faculty of Economics, Catholic University of Rome (Italy) and lecturer at the University of Italian Switzerland in Lugano (Switzerland). His research focuses on spatial statistics and spatial econometrics, and he has published nine books and more than 200 articles in leading journals on these topics. He has been a chairman of the Spatial Econometrics Association since 2006 and a member of the Directory Board of the Italian Statistical Society in the period 2016-2020. He is also an active member of several other international scientific societies. From 2020, he is an editor-in-chief of the Journal of Spatial Econometrics, published by Springer-Verlag. Since 2008 he is the Director of the "Spatial Econometrics Advanced Institute", and since 2016 the leading editor of the book series "Spatial Statistics and Spatial Econometrics", Elsevier. He has been a visiting professor in several universities in the five continents of the world.