Granularity Matching

Granularity Matching is a methodological principle in computational or data-intensive research that ensures correspondence between the level of detail in data representation and the scale of analysis, thereby preserving informational coherence and analytical validity.

4

Publications

61

Citations

19

Authors

8

Institutions

Publications per year

2017–2026

4

Authors

19

  1. JW
    Jinghui Wang

    Foshan University, China

    1 publications · 17 citations

    1

    h-index

  2. NC
    Natacha Crooks

    The University of Texas at Austin, United States

    1 publications · 18 citations

    1

    h-index

  3. HY
    Hongying Yang

    Liaoning Normal University, China

    1 publications · 11 citations

    1

    h-index

  4. CD
    Cong Ding

    University of Göttingen, Germany

    1 publications · 18 citations

    1

    h-index

  5. HS
    Haibo Sun

    Foshan University, China

    1 publications · 17 citations

    1

    h-index

1–5 of 19

Venues

  1. Journal of Open Innovation Technology Market and Complexity

    JOItmC · 1 publications · 15 citations

    1

    h-index

  2. Journal of Applied Physics

    1 publications · 17 citations

    1

    h-index

  3. Multimedia Tools and Applications

    1 publications · 11 citations

    1

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 18 citations

  5. ISPRS International Journal of Geo-Information

    IJGI · 1 publications · 12 citations

1–5