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Chapter 14: Kernel Density Estimation

Step 2: Density Analysis Plugin

This image describes the density analysis plugin, which automates creating density heat maps.
Screenshot of density plugin QGIS

A very useful tool for demonstrating density of a phenomenon is to run Kernel Density Estimation (KDE). KDE measures density of features in relation to their neighborhood using weights. KDE can be used for vector data and creates a smoothed, raster output. As of publishing, QGIS does not have a KDE tool out-of-the-box but you can add the Density Analysis plugin.

  • In the Top menu bar, go to Plugins
  • Search for Density Analysis
  • Install plugin
Screenshot of the density plugin toolbar
Screenshot of density toolbar in QGIS

When the plugin is successfully installed you will see a new toolbar in your workspace.

Step 3: Kernel Density Estimation

An image of the QGIS dialog to define parameters for the kernel density estimator, as described in the chapter.
Screenshot of kernel density estimation parameters in QGIS
  • from the Density toolbar, click the Raster Density tool
    • Input point layer: innerring_sinkholes_2018
    • Cell/pixel dimension: .1 (the higher the value, the lower the resolution)
    • Kernel radius: 1.0 (this is the search distance)
    • Units: miles
    • Color ramp: you choose
    • Kernel shape: uniform (feel free to try other shapes)
    • Decay ratio: default
    • Output value scaling: default
    • Interpolation: linear
    • Mode: continuous
    • Number of gradients: default

      Image of the results of the kernel density estimation of sinkholes layers over the owner occupied choropleth layer

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An Introduction to Geospatial Thinking and Open Source GIS Copyright © 2025 by Jennifer Moore is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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