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plotKML: Scientific Visualization of Spatio-Temporal Data

Author

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  • Hengl, Tomislav
  • Roudier, Pierre
  • Beaudette, Dylan
  • Pebesma, Edzer

Abstract

plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (XML package), and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of arguments, and allows users to visually explore spatio-temporal data available in R: points, polygons, gridded maps, trajectory-type data, vertical profiles, ground photographs, time series vector objects or raster images, along with the results of spatial analysis such as geostatistical mapping, spatial simulations of vector and gridded objects, optimized sampling designs, species distribution models and similar. A generic plotKML() function automatically determines the parsing order and visualizes data directly from R; lower level functions can be combined to allow for new user-created visualization templates. In comparison to other packages writing KML, plotKML seems to be more object oriented, it links more closely to the existing R classes for spatio-temporal data (sp, spacetime and raster packages) than the alternatives, and provides users with the possibility to create their own templates.

Suggested Citation

  • Hengl, Tomislav & Roudier, Pierre & Beaudette, Dylan & Pebesma, Edzer, 2015. "plotKML: Scientific Visualization of Spatio-Temporal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i05).
    • Handle: RePEc:jss:jstsof:v:063:i05
    DOI: http://hdl.handle.net/10.18637/jss.v063.i05
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    References listed on IDEAS

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    1. Grothendieck, Gabor, 2008. "lattice: Multivariate Data Visualization with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(b02).
    2. Pebesma, Edzer, 2012. "spacetime: Spatio-Temporal Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i07).
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    Cited by:

    1. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).

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