空間データ基盤を用いた不整形な小区域データの空間分析ツール開発 Development of a Spatial Analysis Tool for Irregular Zones Using the Spatial Data Framework

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本稿は,国土地理院の空間データ基盤(数値地図2500)と地理情報システム(GIS)を活用して不整形な小区域データの空間分析ツールの開発を試みるものである.その開発に際しては,空間分析ツールとGISのリンケージにデータ統合方式を採用し,空間分析に必要な位相構造データを空間データ基盤から作成する.そのため,特定のGISの機能に依存しない,より柔軟かっ汎用性の高い空間分析ツールが構築できた.さらに,GISの地図作成機能を駆使して松戸市の人口密度分布のコロプレス図を作成し,空間的自己相関を検定した結果,正の空間的自己相関の存在が統計的に確認された.

The objective of this study is to use Digital Map 2500 (Spatial Data Framework, SDF) produced by the Geographical Survey Institute of Japan and the Geographical Information System (GIS) to develop a spatial analysis tool for testing spatial autocorrelations among spatial entities. In the test-ing of spatial autocorrelation, the area under study is subdivided into several unit zones, especially administrative districts with irregular spatial shapes. Problems arise in the acquisition of topological data that are necessary to calculate the weighting matrix {<i>w</i><sub>ij</sub>} which represents linkages based on the spatial configuration of irregular zones. In previous studies testing spatial autocorrelation using the GIS, the weighting matrix {<i>w</i><sub>ij</sub>} was calculated using the topological data structure of GIS such as Arc/Info. In this study, the topological data extracted from the SDF are used for calculating the weight-ing matrix {<i>w</i><sub>ij</sub>}. This study also shows that the SDF is functional and effective data fundament for spa-tial analysis with GIS technology.<br> This paper addresses the following points:<br> 1. There are two types of linkage of the GIS with spatial analysis software. The first is the inter-face type which involves the interfacing of user-developed algorithms via macro languages provided by the GIS. The second type integrates the GIS with spatial analysis software, which involves file trans-fers between the GIS and other software. The latter type is used in this study, so that flexible and general spatial analysis tools can be developed and integrated with several GIS, both with and with-out topological data structures.<br> 2. A topological vector data model provides an explicit representation of spatial relations among spa-tial entities, i. e., points, lines, or areas. In this study, the topological vector data for spatial autocorrela-tion testing and the graphic data for presenting and measuring administrative districts are drawn up using the data structure of the SDF, such as map grids and tree structures of directories and data sources. The weighting matrix {<i>w</i><sub>ij</sub>} is constructed using the topological vector data.<br> 3. Some aspects of spatial autocorrelation are observed on a choropleth of population density in Ma-tsudo City, Chiba Prefecture. This paper aims to use Moren's <i>I</i> statistic with binary and generalized weighting matrices statistically to test the observed spatial autocorrelation among spatial entities.<br> This study reveals that in testing spatial autocorrelation in irregular zones, the use of a binary weighting matrix or a generalized one depends on the size, shape, and spatial arrangement of observa-tional zones.

収録刊行物

  • 地理学評論. Ser. A  

    地理学評論. Ser. A 72(3), 166-177, 1999-03-01 

    The Association of Japanese Geographers

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各種コード

  • NII論文ID(NAID)
    10002223161
  • NII書誌ID(NCID)
    AN1016484X
  • 本文言語コード
    JPN
  • 資料種別
    SHO
  • ISSN
    00167444
  • NDL 記事登録ID
    4662772
  • NDL 雑誌分類
    ZG1(歴史・地理)
  • NDL 請求記号
    Z8-571
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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