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Identification of GIS attribute error using exploratory data analysis.

Exploratory data analysis (EDA)—a data-centered, inductive approach to statistical analysis—offers effective instruments for evaluating the quality and integrity of GIS attribute data. In this study, examples demonstrating EDA distribution analyses, correlational statistics, and proximity analysis...

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Published in:Professional Geographer Vol. 46; pp. 378 - 387
Main Author: Scott, Lauren M.
Format: Article
Published: Taylor & Francis Ltd August 1994
Subjects:
Geographic information systems
Geography > Methodology
Geography > Statistical methods
Online Access:View this record in EBSCOhost
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      dt: August 1994
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      pub: Taylor & Francis Ltd
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        10.1111/j.0033-0124.1994.00378.x
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        atl: Identification of GIS attribute error using exploratory data analysis.
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        au: Scott, Lauren M.
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        Geographic information systems
        Geography -- Methodology
        Geography -- Statistical methods
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        subj:
          Geographic information systems
          Geography -- Methodology
          Geography -- Statistical methods
      ab: Exploratory data analysis (EDA)—a data-centered, inductive approach to statistical analysis—offers effective instruments for evaluating the quality and integrity of GIS attribute data. In this study, examples demonstrating EDA distribution analyses, correlational statistics, and proximity analysis are provided. An integrated modular software prototype system to operationalize these techniques is described that integrates the mapping and display capabilities of PC ArcView with the statistical capabilities of STATA in an MS-Windows multi-tasking, multiple-windowed environment. The combination of data views that the examples offer is shown to be particularly effective in highlighting distributional extremes, correlational outliers, and spatial anomalies.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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