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

Peter Sarlin*

*Motsvarande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

17 Citeringar (Scopus)

Sammanfattning

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.

OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftInternational Journal of Machine Learning and Cybernetics
Volym3
Utgåva3
Sidor (från-till)233-245
Antal sidor13
ISSN1868-8071
DOI
StatusPublicerad - 09.2012
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

  • 511 Nationalekonomi

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