Count Data Spatial Models

Count Data Spatial Models is a class of spatial econometric models that belong to the family of generalized linear models for count outcomes, distinguished by their explicit incorporation of spatial dependence structures—such as spatial lags, spatial errors, or spatial filtering components—to account for spatial autocorrelation in discrete non-negative integer-valued response variables.

70

Publications

3.4K

Citations

201

Authors

135

Institutions

Publications per year

2017–2026

26

Authors

201

  1. JM
    Joanna M. Burgar

    University of British Columbia, Canada

    4 publications · 115 citations

    4

    h-index

  2. LG
    Leroy Gonsalves

    New South Wales Department of Primary Industries, Australia

    2 publications · 48 citations

    2

    h-index

  3. QZ
    Qiang Zeng

    South China University of Technology, China

    2 publications · 49 citations

    2

    h-index

  4. PK
    Paul Kwame Nkegbe

    University for Development Studies, Ghana

    2 publications · 63 citations

    2

    h-index

  5. NB
    Nuha Bintayyash

    University of Manchester, United Kingdom

    2 publications · 58 citations

    2

    h-index

1–5 of 201

Venues

  1. 2

    h-index

  2. Regional Studies

    2 publications · 41 citations

    2

    h-index

  3. Journal of Regional Science

    JRS · 2 publications · 138 citations

    2

    h-index

  4. Biometrics

    2 publications · 190 citations

    2

    h-index

  5. PLoS ONE

    2 publications · 33 citations

    2

    h-index

1–5