Quantile Classification

Quantile Classification is a statistical data classification method in choropleth mapping that partitions ordered data values into equal-sized classes, where each class contains approximately the same number of observations, thereby distinguishing it from other classification techniques by emphasizing distributional equivalence rather than interval or natural break optimization.

24

Publications

1.7K

Citations

74

Authors

52

Institutions

Publications per year

2017–2026

14

Variants

Quantile Data Classification

Authors

74

  1. RG
    Rongfen Gong

    University of Science and Technology Liaoning, China

    2 publications · 54 citations

    2

    h-index

  2. EF
    Eugene F. Schuster

    University College London, United Kingdom

    2 publications · 84 citations

    2

    h-index

  3. XL
    Xiaoping Liu

    Lakehead University, Canada

    2 publications · 54 citations

    2

    h-index

  4. MC
    Maoxiang Chu

    University of Science and Technology Liaoning, China

    2 publications · 54 citations

    2

    h-index

  5. JC
    Jonathan Chevrier

    McGill University, Canada

    1 publications · 13 citations

    1

    h-index

1–5 of 74

Venues

  1. 2

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  2. 1

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  3. BMC Geriatrics

    1 publications · 17 citations

    1

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  4. Resources Policy

    1 publications · 131 citations

    1

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  5. Machine Learning and Knowledge Extraction

    1 publications · 62 citations

    1

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1–5