Kernel Density Estimation (KDE)

Generate density surfaces from point data using kernel density estimation.

Overview

KDE creates a continuous density surface from point data, showing where points are concentrated. Higher values indicate greater point density.

Inputs

  • Dataset: Point dataset

  • Bandwidth: Smoothing parameter (default: auto-calculated)

  • Cell Size: Output raster cell size (default: auto-calculated)

  • Weight Field (optional): Field to weight points

Outputs

Raster dataset containing:

  • Density values for each cell

  • Higher values indicate greater point density

  • Proper spatial reference

Algorithm

  1. Calculate optimal bandwidth (if not specified)

  2. Create output raster grid

  3. For each cell, calculate kernel-weighted sum of nearby points

  4. Store density values in raster

Example

{
  "dataset_id": 123,
  "bandwidth": 1000,
  "cell_size": 100
}

Background Jobs

This analysis runs as a background job.

Use Cases

  • Population density mapping

  • Crime hotspot visualization

  • Species distribution modeling

  • Event density analysis

Notes

  • Bandwidth controls smoothing (larger = smoother)

  • Cell size controls output resolution

  • Weight field allows importance weighting

  • Results are sensitive to bandwidth selection