The R Journal: article published in 2018, volume 10:2

Geospatial Point Density PDF download
Paul F. Evangelista and David Beskow , The R Journal (2018) 10:2, pages 347-356.

Abstract 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.

Received: 2018-04-04; online 2018-12-08, supplementary material, (3.2 Kb)
CRAN packages: pointdensityP, spatstat, kde2d, bkde2D, ggplot2, ggmap, data.table
CRAN Task Views implied by cited CRAN packages: Spatial, Finance, Graphics, HighPerformanceComputing, Phylogenetics, SpatioTemporal, Survival, WebTechnologies


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-061,
  author = {Paul F. Evangelista and David Beskow},
  title = {{Geospatial Point Density}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-061},
  url = {https://doi.org/10.32614/RJ-2018-061},
  pages = {347--356},
  volume = {10},
  number = {2}
}