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

  • Book
  • © 1989

Overview

Authors:
  1. Giuseppe Arbia

    1. Istituto di Statistica Economica, Faculty of Statistics, University of Rome “La Sapienza”, Italy
      Fitzwilliam College, Cambridge, UK

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

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction: spatial effects and the role of configuration of data

    • Giuseppe Arbia
    Pages 1-6

  3. Theoretical problems motivation

    • Giuseppe Arbia
    Pages 7-31

  4. The configuration of spatial data in regional economics

    • Giuseppe Arbia
    Pages 32-42

  5. Stochastic spatial processes

    • Giuseppe Arbia
    Pages 43-92

  6. Univariate problems: the modifiable areal unit problem

    • Giuseppe Arbia
    Pages 93-153

  7. Bivariate problems: The modifiable areal unit problem and correlation between processes

    • Giuseppe Arbia
    Pages 154-176

  8. Bivariate problems: The ecological fallacy

    • Giuseppe Arbia
    Pages 177-194

  9. The dampening effect of spatial correlogram

    • Giuseppe Arbia
    Pages 195-220

  10. Conclusion

    • Giuseppe Arbia
    Pages 221-241

  11. Back Matter

    Pages 225-257
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Keywords

About this book

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.

Authors and Affiliations

  • Istituto di Statistica Economica, Faculty of Statistics, University of Rome “La Sapienza”, Italy

    Giuseppe Arbia

  • Fitzwilliam College, Cambridge, UK

    Giuseppe Arbia

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