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Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems

  • Giuseppe Arbia

Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 14)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Giuseppe Arbia
    Pages 7-31
  3. Giuseppe Arbia
    Pages 43-92
  4. Giuseppe Arbia
    Pages 177-194
  5. Giuseppe Arbia
    Pages 195-220
  6. Giuseppe Arbia
    Pages 221-241
  7. Back Matter
    Pages 225-257

About this book

Introduction

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.

Keywords

Simulation Stochastic Processes calculus statistical analysis time series time series analysis

Authors and affiliations

  • Giuseppe Arbia
    • 1
    • 2
  1. 1.Istituto di Statistica Economica, Faculty of StatisticsUniversity of Rome “La Sapienza”Italy
  2. 2.Fitzwilliam CollegeCambridgeUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-2395-9
  • Copyright Information Springer Science+Business Media B.V. 1989
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-7578-7
  • Online ISBN 978-94-009-2395-9
  • Series Print ISSN 1570-5811
  • Buy this book on publisher's site
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