To create a nice bounded Voronoi polygons tessellation of a point layer
in `R`

, we need two libraries: `sp`

and `deldir`

. The following function
takes a `SpatialPointsDataFrame`

as input, and returns a
`SpatialPolygonsDataFrame`

that represents the Voronoi tessellation of
the input point layer.

- Wed 16 September 2009 cfarmer
# Python, Matlab, and R

Wed 12 August 2009 cfarmerOne project I’m working on at the moment involves exploring a dynamic extension of the Isomap algorithm for visualising constantly varying real-world road networks. Currently, we are testing out the method on a small scale simulated road network, and most of the original code (written by Laurens van der Maaten, with updates by Alexei Pozdnoukhov), was done in Matlab. Since this work is eventually going to have to run on relatively large datasets, and probably behind the scenes on a server somewhere, we decided that Python was the way to go. The goal therefore was to reproduce the Matlab code using only Python libraries, and the fewer additional libraries required, the better.

# R featured in New York Times

Wed 28 January 2009 cfarmerI’m sure everyone has seen this already, but I’m going to post it anyway, as I think the more exposure open-source tools get, the better off we’ll all be!

Check out this New York Times article which features

`R`

, the open-source statistical programming language.`R`

now has quite an extensive range of spatial analysis options, and is the software of choice for researchers using spatial statistics and geographic information analysis.# View spatial data attribute tables in R

Tue 14 October 2008 cfarmerMany GIS offer the ability to view the attribute table of a vector layer. While this is perhaps less obvious in the R environment, it is not impossible. The following command allows you to visually inspect, and change any data.frame (or other vector, matrix, etc.), including Spatial*DataFrames.

# R spatial indentify tool

Tue 23 September 2008 cfarmerThis is useful for visually exploring R spatial data such as

`SpatialPointDataFrames`

or`SpatialGridDataFrames`

. By clicking on various features, the value at that point will be displayed.library(rgdal) y = readGDAL(system.file("pictures/Rlogo.jpg", package="rgdal")[1], band=1) y.grid = y@grid y.coords = coordinates(y.grid) image(y) identify(x=y.coords, y=NULL, n=1)

where

`x`

and`y`

refer to coordinates (in this case because`y.coords`

contains both`x`

and`y`

coordinates,`y`

can be set to`NULL`

), and`n`

is the number of features to identify.