Looking for APoLUS? go here
If you are still interested in SIMLANDER, our early experimental version of a Cellular Automata model in R, read on….
SIMLANDER stands for SIMulation of LAnd use changE using R, and is a prototype Cellular Automata (CA) land use model built for the R software environment. It is a completely free experimental system without any guarantee which you are invited to play with to your heart’s content. In fact, it’s not really a system, just a collection of simple routines for the R software environment collected into a script. If you do find it useful, we’d appreciate it if you could reference us in your work:
Hewitt, R., Díaz Pacheco, J. and Moya Gómez, B., (2013), A cellular automata land use model for the R software environment (weblog), https://simlander.wordpress.com.
Download latest user manual in pdf format (September 2014) here.
Download sample data and code here. WordPress does not permit .zip extensions, so after downloading (if you are on a windows system make sure your file extensions are not hidden – if they are change this in Folder Options) you will need to change the file extension (which is .doc) back to .zip. Then you can unzip the file which contains the data and latest script version (v1.0.4, May 2014). See below for earlier versions.
DOWNLOAD SIMLANDER LATEST VERSION
Though there are a number of Free and Open Source Software (FOSS) applications presently available for other types of land use models, such as Huang’s Change Analysis (logistic regression), and Purdue University’s Land Transformation Modeller (LTM) (Artificial Neural Networks), there are few fully operational implementations of true CA models for land use modelling in the Open Source community. In addition, stand alone model frameworks do not usually incorporate appropriate statistical goodness-of-fit comparison techniques for model evaluation, something that is normally carried out externally in statistical software packages.
This website is dedicated to the development of SIMLANDER, in which we try to address some of these problems. The R platform, with its extensive developer and user community, and its clear relevance for model building and scientific computing, seems to present an ideal environment for geographical land use modelling.
Batty, M., and Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning and Design 21 Supplement, 31–48.
Tobler, W. (1979), Cellular Geography, S. Gale & G. Olsson, eds., Philosophy in Geography, Reidel, Dortrecht; pp 379-386.