Estimate Point Density
Overview
Estimate a density raster from input point features. The result represents point concentration over space using the specified search radius and kernel function. During conversion from vector features to raster representation, resolution, cell alignment, field types, and NoData rules directly affect result accuracy.
Use Cases
- Analyze incident, facility, population sample, or observation-point concentration.
- Create heatmap-like density surfaces for visualization or spatial decision-making.
- Improve efficiency for batch processing, repeated runs, or standardized delivery.
Parameters
| Parameter | Description | Notes |
|---|---|---|
| Input point vector file | Vector file containing point features | Required |
| Search radius | Search radius for density calculation. Units match the coordinate system of the raster area | Required; default: 1000 |
| Weight field | Optional weight field for density calculation | Optional |
| Kernel function type | Kernel function used for density calculation | Required; default: gaussian; options: Gaussian (gaussian), uniform (tophat), Epanechnikov (epanechnikov), exponential (exponential), linear (linear), cosine (cosine) |
| Raster area | Coordinate system, resolution, and extent settings for the output raster | Optional |
| Output density raster file | Output point-density raster file | Required |
Steps
- Start the tool: Open Geoprocessing Toolbox → Spatial Analysis Tools > Density Analysis, then start the Estimate Point Density tool pane.
- Prepare input: Select the Input point vector file, Weight field, and Raster area as needed, then confirm that the data is complete and readable.
- Set core parameters: Configure Search radius, Weight field, and Kernel function type based on the analysis goal.
- Set output: Specify the Output density raster file and confirm that the output path, format, and naming rules meet downstream requirements.
- Run and inspect the result: Click Run, then check whether the density range, spatial distribution, and raster extent meet expectations.
Notes
- Use a projected coordinate system when the radius and cell size are distance-based; avoid treating longitude and latitude as planar distances.
- Kernel function, search radius, and cell size strongly affect smoothness and hotspot shape.