Geospatial Point Density

This paper introduces a spatial point density algorithm designed to be explainable, meaning ful, and efficient. Originally designed for military applications, this technique applies to any spatial point process where there is a desire to clearly understand the measurement of density and maintain fidelity of the point locations. Typical spatial density plotting algorithms, such as kernel density estimation, implement some type of smoothing function that often results in a density value that is difficult to interpret. The purpose of the visualization method in this paper is to understand spatial point activity density with precision and meaning. The temporal tendency of the point process as an extension of the point density methodology is also discussed and displayed. Applications include visualization and measurement of any type of spatial point process. Visualization techniques integrate ggmap with examples from San Diego crime data.

Paul F. Evangelista , David Beskow

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

pointdensityP, spatstat, kde2d, bkde2D, ggplot2, ggmap, data.table

CRAN Task Views implied by cited packages

Spatial, Finance, Graphics, HighPerformanceComputing, Phylogenetics, SpatioTemporal, Survival, WebTechnologies


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Evangelista & Beskow, "The R Journal: Geospatial Point Density", The R Journal, 2018

BibTeX citation

  author = {Evangelista, Paul F. and Beskow, David},
  title = {The R Journal: Geospatial Point Density},
  journal = {The R Journal},
  year = {2018},
  note = {},
  doi = {10.32614/RJ-2018-061},
  volume = {10},
  issue = {2},
  issn = {2073-4859},
  pages = {347-356}