Kernel Density Estimation Maps

Kernel Density Estimation Maps is a spatial visualization method that estimates the intensity of point patterns across a continuous surface by applying a kernel function to aggregate point densities, distinguishing it from other point pattern visualization techniques through its use of smoothed, continuous surface interpolation to represent the underlying probability density of point events.

264

Publications

12.2K

Citations

1.1K

Authors

565

Institutions

Publications per year

2017–2026

152

Variants

Kde Maps

Authors

1.1K

  1. TA
    Tinghua Ai

    Wuhan University, China

    3 publications · 261 citations

    3

    h-index

  2. JM
    Jukka M. Krisp

    University of Augsburg, Germany

    3 publications · 47 citations

    3

    h-index

  3. PL
    Paul Longley

    University College London, United Kingdom

    2 publications · 95 citations

    2

    h-index

  4. BT
    Bryan T. Smith

    NOAA Storm Prediction Center

    2 publications · 112 citations

    2

    h-index

  5. DW
    David W. Macdonald

    University of Oxford, United Kingdom

    2 publications · 78 citations

    2

    h-index

1–5 of 1.1K

Venues

  1. Sustainability

    15 publications · 387 citations

    12

    h-index

  2. ISPRS International Journal of Geo-Information

    IJGI · 11 publications · 268 citations

    11

    h-index

  3. PLoS ONE

    9 publications · 452 citations

    9

    h-index

  4. Remote Sensing

    5 publications · 116 citations

    5

    h-index

  5. International Journal of Health Geographics

    5 publications · 543 citations

    5

    h-index

1–5