IOSA.it Open Archaeology

Today I have added a new tutorial in the Spatial analysis section of the Quantitative Archaeology Wiki.
Using GRASS, a simple method is shown for performing a basic exploratory analysis on the relationship between settlement sites and landscape variables like elevation and aspect. Further updates to the tutorial will include more screenshots and advanced quantitative analysis.
The tutorial is available at http://wiki.iosa.it/dokuwiki/spatial_analysis:settlements_and_landscape.

Last week I was looking at some slides from a lecture here at university. One slide caught my attention more than others, it was a map with some pie charts representing the distribution of different vegetal species in quadrats of an archaeological sites. Quite common and ordinary stuff in publications, indeed.

The first question rising to my mind was: how do I do this in GRASS?. It turned out that there wasn't a module to automagically count the number of points within an area, and generate a report that keeps also information about the distribution of different classes. The chart stuff is already available since GRASS 6.0 and before, but I needed a module that could generate data from which I could plot charts.

These pages on the website of prof. Juan A. Barceló deal in detail with some of the hot topics in archaeological computing, like

• Artificial Intelligence and Neural Networks used to analyze archaeological datasets
• Statistical methods
• Spatial Analysis techniques to be performed within GIS
• and more

Content is available both in Catalá and English and can be useful to give students a complete and exhaustive overview of some of the most advanced techniques that can be used to improve and speed up archeological research.

This collection of web pages is intended to be a guide to some of the resources for the analysis of spatial data using R, and other associated software. Another useful resource is the CRAN Spatial Task View.It includes packages like spBayes, an R package for hierarchical spatial modelling, rgdal and spgrass6, which are meant to manage spatial information using the most powerful open source tools. There is a mailing list, that of the R-sig-geo.