Spatial Dimension Reduction

Spatial Dimension Reduction is a statistical methodology that reduces the number of spatial variables or coordinates in a dataset while preserving essential spatial structure and dependencies, distinguishing it from non-spatial dimension reduction techniques by explicitly accounting for spatial autocorrelation and geographic relationships.

595

Publications

37.1K

Citations

2.1K

Authors

852

Institutions

Publications per year

2017–2026

272

Authors

2.1K

  1. CS
    Chongmin Song

    UNSW Sydney, Australia

    6 publications · 340 citations

    6

    h-index

  2. NC
    Noel Cressie

    Iowa State University, United States

    6 publications · 551 citations

    6

    h-index

  3. LZ
    Liangpei Zhang

    Wuhan University, China

    6 publications · 387 citations

    5

    h-index

  4. EL
    Emily L. Kang

    University of Cincinnati, United States

    4 publications · 280 citations

    4

    h-index

  5. CG
    Cássio G. Lopes

    University of California, Los Angeles, United States

    4 publications · 1.1K citations

    4

    h-index

1–5 of 2.1K

Venues

  1. 14

    h-index

  2. Journal of the American Statistical Association

    11 publications · 462 citations

    11

    h-index

  3. arXiv (Cornell University)

    Repository · 10 publications · 422 citations

    10

    h-index

  4. IEEE Access

    9 publications · 291 citations

    9

    h-index

  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 6 publications · 110 citations

    6

    h-index

1–5