The self-organizing map, the Geo-SOM, and relevant variants for geosciences

Authors:
Fernando Bação;Victor Lobo;Marco Painho
Affiliations:
Instituto Superior de Estatística e Gestão de Informação, Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal;Instituto Superior de Estatística e Gestão de Informação, Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal and Portuguese Naval Academy, Almada, Portugal;Instituto Superior de Estatística e Gestão de Informação, Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal
Venue:
Computers & Geosciences
Year:
2005

Citing 4
Cited 7

Neural Networks in Computer Intelligence

Neural Networks in Computer Intelligence
Geographic Data Mining and Knowledge Discovery

Geographic Data Mining and Knowledge Discovery
Neural maps in remote sensing image analysis

Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Clustering of the self-organizing map

IEEE Transactions on Neural Networks

A semi-automatic method for analysis of landscape elements using Shuttle Radar Topography Mission and Landsat ETM+ data

Computers & Geosciences
Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics

Information Visualization
GeoSOM Suite: A Tool for Spatial Clustering

ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Self-organizing maps as substitutes for k-means clustering

ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps

Computers & Geosciences
A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity

Computers & Geosciences
Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns

EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknesses of the different variants proposed are shown through a set of tests based on artificial data. A real world application of these techniques is given through the analysis of geodemographic data from Lisbon's metropolitan area.