Prediction Under Spatial Misalignment

Prediction Under Spatial Misalignment is a spatial prediction framework that addresses the challenge of making accurate inferences when observational data and target spatial units are misaligned in their geographic support, requiring explicit modeling of spatial disaggregation or integration processes to reconcile scale and extent differences.

17

Publications

1.1K

Citations

57

Authors

41

Institutions

Publications per year

2017–2026

5

Authors

57

  1. AE
    Alan E. Gelfand

    Duke University, United States

    3 publications · 253 citations

    3

    h-index

  2. SB
    Sudipto Banerjee

    University of Minnesota, United States

    3 publications · 109 citations

    3

    h-index

  3. HX
    Haifeng Xia

    Tulane University, United States

    1 publications · 40 citations

    1

    h-index

  4. AA
    Adam A. Szpiro

    University of Washington, United States

    1 publications · 152 citations

    1

    h-index

  5. YG
    Yu Gao

    Beijing Forestry University, China

    1 publications · 16 citations

    1

    h-index

1–5 of 57

Venues

  1. Biostatistics

    3 publications · 414 citations

    3

    h-index

  2. 2

    h-index

  3. Biometrics

    2 publications · 242 citations

    2

    h-index

  4. Atmospheric Environment

    1 publications · 33 citations

    1

    h-index

  5. Ecology

    1 publications · 75 citations

    1

    h-index

1–5