Spatial Autocorrelation Modeling

Spatial Autocorrelation Modeling is a geostatistical methodology that quantifies and accounts for the non-random spatial dependence among observations in a geographic domain, distinguishing itself by explicitly formalizing the degree to which proximal locations exhibit similar values as a core structural component of spatial process inference.

1.3K

Publications

126K

Citations

3.6K

Authors

1.3K

Institutions

Publications per year

2017–2026

477

Authors

3.6K

  1. DA
    Daniel A. Griffith

    The University of Texas at Dallas, United States

    55 publications · 4.7K citations

    33

    h-index

  2. RR
    Robert R. Sokal

    Stony Brook University, United States

    16 publications · 3.5K citations

    15

    h-index

  3. AG
    Arthur Getis

    San Diego State University, United States

    14 publications · 3K citations

    13

    h-index

  4. JK
    J. Keith Ord

    Pennsylvania State University, United States

    12 publications · 5.4K citations

    12

    h-index

  5. LA
    Luc Anselin

    Arizona State University, United States

    12 publications · 8.4K citations

    12

    h-index

1–5 of 3.6K

Venues

  1. Geographical Analysis

    47 publications · 7.1K citations

    30

    h-index

  2. Ecological Indicators

    36 publications · 1.9K citations

    26

    h-index

  3. 25

    h-index

  4. Sustainability

    39 publications · 1.1K citations

    21

    h-index

  5. Ecography

    22 publications · 4.7K citations

    20

    h-index

1–5