Spatial data configuration in statistical analysis of regional economic and related problems
著者
書誌事項
Spatial data configuration in statistical analysis of regional economic and related problems
(Advanced studies in theoretical and applied econometrics, v. 14)
Kluwer Academic, c1989
大学図書館所蔵 全50件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
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  タイ
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  ドイツ
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注記
Revision of thesis (Ph. D.)
Bibliography: p. [242]-250
Includes index
内容説明・目次
内容説明
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.
目次
1. Introduction: spatial effects and the role of configuration of data.- 1.1 Objectives and approaches.- 1.2 An overview of theoretical problems.- 1.3 A sketch of the methodology.- 1.4 An outline of the book.- 1.5 Omitted topics.- 2. Theoretical Problems Motivation.- 2.1 Introduction.- 2.2 The modifiable areal unit problem.- 2.3 The ecological fallacy problem.- 2.4 Problems in the estimation of the spatial correlogram.- 2.5 Summary and conclusion.- 3. The Configuration of Spatial Data in Regional Economics.- 3.1 Introduction.- 3.2 The nature of spatial data in regional economic analysis.- 3.3 Describing the configuration of irregular collecting areas.- 3.4 Conclusion.- Appendix 3.1 FORTRAN program to generate connectivity matrices with a considerably smaller matrix as an input.- Appendix 3.2 FORTRAN program to generate grouping matrices with a considerably smaller matrix as an input.- 4. Stochastic Spatial Processes.- 4.1 Stationary stochastic processes in two dimensions.- 4.2 Linear transformations of random processes.- 4.3 Inference on spatial stochastic processes.- 4.4 Summary and conclusion.- 5. Univariate Problems: The Modifiable Areal Unit Problem.- 5.1 Introduction.- 5.2 The scale problem : regular case.- 5.3 The scale problem : irregular case.- 5.4 The aggregation problem.- 5.5 Summary and conclusion.- Appendix 5.1 FORTRAN program for the recursive estimation of variance and covariance.- Appendix 5.2 FORTRAN program for the generation of pseudo-random regular zoning systems.- 6. Biyariate Problems: The Modifiable Areal Unit Problem and Correlation between Processes.- 6.1 Introduction.- 6.2 Scale and correlation between processes.- 6.3 Aggregation and correlation between processes.- 6.4 Summary and conclusion.- 7. Biyariate Problems: The Ecological Fallacy.- 7.1 Introduction.- 7.2 The ecological fallacy problem.- 7.3 Summary and conclusion.- Appendix 7.1: FORTRAN program for the generation of observations from a multivariate process with a very large variance-covariance matrix.- 8. The Dampening Effect of Spatial Correlograms.- 8.1 Introduction.- 8.2 The dampening effect.- 8.3 Simulation study.- 8.4 Summary and conclusion.- 9. Conclusion.- Appendices.- A.1 Population, employed and activity rates for local labour markets in Italy in 1981 Census.- A.2 Electricity consumption of Italian manufacturing industry in the first semester of 1985.- A.3 Quadrats counts of houses in Hukuno town, Tonami plain, Japan (Matui, 1932).- A.4 Weights of wheat plots of grain (Mercera Hall, 1911).- A.5 Simulation methods in two dimensions.- References.
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