Whenever I have run into a more-than-usually knotty GIS analysis step, one which the tools bundled with ArcGIS just don’t seem to be able to unravel, I look first to Hawth’s Tools. Hawth’s Tools is a free package of add-ons for ArcGIS, capable of all manner of tricks, like “for each polygon, create a new attribute which records the range of values of the points which fall into it“. Handy stuff like that. When asked about a GIS problem, I have a bad habit of saying “oh sure, I can do that” and then discovering it’s not so easy, and as such I’ve often thanked Hawthorne L. Beyer under my breath for his freely given antidote to my hubris.

Having just such a task on my hands today, I look to spatialecology.com and discover that the Hawth has made good on his long-standing threat of re-writing the whole H-Tools package in a new and ambitious form, currently in beta distribution and very handsomely titled “The Geospatial Modelling Environment“.

“GME provides you with a suite of analysis and modelling tools, ranging from small ‘building blocks’ that you can use to construct a sophisticated work-flow, to completely self-contained analysis programs. It also uses the extraordinarily powerful open source software R as the statistical engine to drive some of the analysis tools. One of the many strengths of R is that it is open source, completely transparent and well documented: important characteristics for any scientific analytical software.”

Excellent.

update: I’ve now used GME for some basic processing, and the enhanced-command-line-interface it employs might be a little unfriendly for some users, but on the whole it looks like an excellent system with real promise. And given that it’s built on open libraries for geo-statistics and visualization, the tools can presumably be ported into other GIS packages, including open source packages, relatively easily.

Roughly a year ago, I made some noises on this blog about wanting to learn R. Not surprisingly, I didn’t do it.

A year later I’m a government scientist with some statistics to do, and I’m once again thinking of learning me some R. In the interim, I’ve received an email assuring me “you could get up and running with it within a day, I think. Master it in a week or two”. So I download the package — it’s free! — install it, and boot it up. I’m looking at a command line console labelled the “GUI” (ha ha), with the following help text:

“Type ‘demo()’ for some demos”

Demos! Perfect! Let’s see some concrete examples of how to do statistics in R-land! So I type demo() into the “GUI” prompt, and receive the following output:

Demos in package ‘base’:

is.things: Explore some properties of R objects and is.FOO() functions. Not for newbies!
recursion: Using recursion for adaptive integration
scoping: An illustration of lexical scoping.

Demos in package ‘graphics’:

Hershey: Tables of the characters in the Hershey vector fonts
Japanese: Tables of the Japanese characters in the Hershey vector fonts
graphics: A show of some of R’s graphics capabilities
image: The image-like graphics builtins of R
persp: Extended persp() examples
plotmath: Examples of the use of mathematics annotation

Demos in package ‘stats’:

glm.vr: Some glm() examples from V&R with several predictors
lm.glm: Some linear and generalized linear modelling examples from `An Introduction to Statistical Modelling’ by Annette Dobson
nlm: Nonlinear least-squares using nlm()
smooth: `Visualize’ steps in Tukey’s smoothers

Use ‘demo(package = .packages(all.available = TRUE))’ to list the demos in all *available* packages.

Tables of the characters in the Hershey vector fonts? demo(package = .packages(all.available = TRUE))? Some of the ‘stats’ packages sounded like they might make sense, so I tried to run them, but I couldn’t figure out how. I love the idea of open source bare-knuckle computing. I wish I loved it in practice.