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Visual tracking of the millennium development goals with a fuzzified self-organizing neural network

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Abstract

This paper uses the self-organizing map (SOM), a neural network-based projection and clustering technique, for monitoring the millennium development goals (MDGs). The eight 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. This paper presents a SOM model for cross sectional and temporal visual benchmarking of countries and pairs the map with a geospatial dimension by mapping the clustering onto a geographic map. The temporal monitoring is facilitated by fuzzifying the second-level clustering with membership degrees. By creating an MDG index, and associating the SOM model with it, the model enables cross sectional and temporal analysis of the overall MDG progress of countries or regions. Further, the SOM model enables analysis of country-specific as well as regional performance according to a user-specified level of aggregation. The result of this paper is an MDG map for visual tracking and monitoring of the progress of MDG indicators.

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Notes

  1. The HDI, for example, has been criticized for the way its component indices are derived by the raw data (see Noorbakhsh [17]) and the additivity of the aggregation method (see Sagar and Najam [23]).

  2. For a thorough discussion of the software, see Deboeck [6].

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Acknowledgments

I acknowledge Barbro Back and Tomas Eklund for helpful comments and suggestions.

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Correspondence to Peter Sarlin.

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Sarlin, P. Visual tracking of the millennium development goals with a fuzzified self-organizing neural network. Int. J. Mach. Learn. & Cyber. 3, 233–245 (2012). https://doi.org/10.1007/s13042-011-0057-5

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