On Statistical Measures for Data Quality Evaluation - Journal of Geographic Information System

JGIS > Vol.12 No.3, June 2020

Journal of Geographic Information System

Volume 12, Issue 3 (June 2020)

ISSN Print: 2151-1950 ISSN Online: 2151-1969

Google-based Impact Factor: 1.07 Citations h5-index & Ranking

On Statistical Measures for Data Quality Evaluation ()

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DOI: 10.4236/jgis.2020.123011 581 Downloads 2,437 Views Citations

Author(s)

Xiaoxia Han

Affiliation(s)

Department of Public Health Sciences, Henry Ford Health System, Detroit, USA.

ABSTRACT

Most GIS databases contain data errors. The quality of the data sources such as traditional paper maps or more recent remote sensing data determines spatial data quality. In the past several decades, different statistical measures have been developed to evaluate data quality for different types of data, such as nominal categorical data, ordinal categorical data and numerical data. Although these methods were originally proposed for medical research or psychological research, they have been widely used to evaluate spatial data quality. In this paper, we first review statistical methods for evaluating data quality, discuss under what conditions we should use them and how to interpret the results, followed by a brief discussion of statistical software and packages that can be used to compute these data quality measures.

KEYWORDS

GIS Data Quality, Sensitivity, Specificity, Kappa, Weighted Kappa, Bland-Altman Analysis, Intra-Class Correlation Coefficient

Share and Cite:

Han, X. (2020) On Statistical Measures for Data Quality Evaluation. Journal of Geographic Information System, 12, 178-187. doi: 10.4236/jgis.2020.123011.

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