SPAWNN

SPatial Analysis With self-organizing Neural Networks

The SPAWNN toolkit is an innovative toolkit for spatial analysis with self-organizing neural networks which is particularily useful for spatial analysis, visualization and geographical data mining.

Features:

• Implements Self-Organizing Map and Neural Gas algorithms
• Supports different approaches for considering spatial dependence
• Provides linkage between networks and geographical data
• Implements powerful clustering algorithms for structuring the networks
• Provides powerful visualizations of networks and data

Download

A pre-build jar-file of the toolkit can be found here

Example usage

An exemplary description of the usage of the toolkit can be found here.

Please cite:

• Hagenauer, J., & Helbich, M. (2016). SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks. Transactions in GIS, 20(5), 755-775.

Other related publications:

• Hagenauer, J. (2016). Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. Journal of Geographical Systems, 18(1), 1-15.
• Hagenauer, J., & Helbich, M. (2013). Contextual neural gas for spatial clustering and analysis. International Journal of Geographical Information Science, 27(2), 251-266.

License

GNU GENERAL PUBLIC LICENSE Version 3