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Visual Tracking of the Millennium Development Goals with a Self-Organizing Neural Network

Peter Sarlin, Visual Tracking of the Millennium Development Goals with a Self-Organizing Neural Network. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM'11), 357–364, IEEE Press, 2011.

Abstract:

The Millennium Development Goals (MDGs) represent commitments to reduce poverty and hunger, and to tackle ill-health, gender inequality, lack of education, lack of access to clean water and environmental degradation by 2015. The eight goals of the Millennium Declaration are tracked using 21 benchmark targets, measured by 60 indicators. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based projection and clustering technique, facilitates monitoring of the multidimensional MDGs. First, this paper presents a SOM model for visual benchmarking of countries and for visual analysis of the evolution of MDG indicators. Second, the SOM is paired with a geospatial dimension by mapping the clustering results on a geographic map. The results of this paper indicate that the SOM is a feasible tool for visual monitoring of MDG indicators.

BibTeX entry:

@INPROCEEDINGS{iSa11a,
  title = {Visual Tracking of the Millennium Development Goals with a Self-Organizing Neural Network},
  booktitle = {Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM'11)},
  author = {Sarlin, Peter},
  publisher = {IEEE Press},
  pages = {357–364},
  year = {2011},
  keywords = {Self-organizing maps; Millennium Development Goals; projection; clustering; geospatial visualization},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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