Algorithms for clustering data
Algorithms for clustering data
Self-Organizing Maps
Power comparisons for disease clustering tests
Computational Statistics & Data Analysis
Analysis of Parliamentary Election Results and Socio-Economic Situation Using Self-Organizing Map
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of the cantons of Vaud and Geneva in Western Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the region and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape of the region. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of activities. The method is applied to the distribution of business managers and working class at the intra-urban scale. Results show the effect of peri-urbanization of the region and can be analyzed in both transportation and social terms.