Local Spatial Autocorrelation

Local Spatial Autocorrelation is a spatial statistical method that identifies localized patterns of clustering or dispersion by measuring the degree of similarity between a variable's values at a specific location and those in its neighboring locations, thereby distinguishing local departures from global spatial randomness within the broader class of spatial autocorrelation techniques.

639

Publications

44.2K

Citations

2.1K

Authors

935

Institutions

Publications per year

2017–2026

271

Authors

2.1K

  1. RR
    Robert R. Sokal

    Stony Brook University, United States

    12 publications · 1.4K citations

    12

    h-index

  2. DA
    Daniel A. Griffith

    The University of Texas at Dallas, United States

    10 publications · 1K citations

    10

    h-index

  3. AG
    Arthur Getis

    San Diego State University, United States

    7 publications · 5K citations

    7

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  4. NL
    Neal L. Oden

    Emmes (United States), United States

    6 publications · 1.1K citations

    6

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  5. BB
    Barry Boots

    Wilfrid Laurier University, Canada

    6 publications · 629 citations

    6

    h-index

1–5 of 2.1K

Venues

  1. Geographical Analysis

    23 publications · 8.1K citations

    19

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  2. Sustainability

    26 publications · 947 citations

    18

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  3. 17

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  4. PLoS ONE

    18 publications · 571 citations

    14

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  5. 12

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