A collection of notes, examples, references, and thoughts on working with spatial data.

I recently upgraded to GEOS 3.0.0rc4 in order to take advantage of its more robust handling of invalid geometries (damn shapefiles!). However, there are two mini-bugs in this release which need to be sorted out when compiling against it (or at least for the two application I was interested in: QGIS and StarSpan). Note that the first issue presented here has been resolved in GEOS 3.0.0rc5.

The creation of print quality maps can be a daunting task, especially when a scaleble output format (such as PDF) is required.

 
Overview:
A simple example of how to use R to perform interpolation with ordinary kriging, using data from a text file. This example makes use of the gstat library for R. Additional examples of how to use the following gstat functions are included:

• variogram maps
• directional variogram plots
• ploting the interpolated surface directly from R

Note that this example is not meant to be an authoritative guide on variogram selection, or proper modeling of anisotropy-- just an example. The Kansas Geological Survey has an interesting set of reports that illustrate selection of a directional variogram in the presence of a strong, regional trend.

 
Overview:
A simple example of how to use GRASS+R to perform interpolation with ordinary kriging, using data from the spearfish sample dataset. This example makes use of the gstat library for R.

 
Helpful References:

• Issaks, E.H. & Srivastava, R.M. An Introduction to Applied Geostatistics Oxford University Press, 1989
• GSTAT Manual
• GSTAT Examples

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Overview
A simple experiment illustrating the calculation of elevation profiles along arbitrary line segments using a combination of GRASS and R. Fractal dimension was calculated for each profile by spectral decomposition (FFT), as suggested in (Davis, 2002). Note that the elevation profiles are calculated in a rather non-standard way, as elevation is plotted against 3D distance along the original line segment. This means that resulting transect length calculations are sensitive to the resolution of the raster (or distance between sample points), across a rough surface: i.e. smaller grid spacing will result in a longer effective (3D) transect length.

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