Clustering and hot spot detection in socio-economic spatio-temporal data

Détails

ID Serval
serval:BIB_2F7D1361011D
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Institution
Titre
Clustering and hot spot detection in socio-economic spatio-temporal data
Titre du livre
Transactions on Computational Science VI
Auteur(s)
Tuia Devis, Kaiser Christian, Da Cunha Antonio, Kanevski Mikhail
Editeur
Springer
Lieu d'édition
Berlin
Statut éditorial
Publié
Date de publication
2009
Editeur scientifique
Gavrilova M., Kenneth Tan C.J.
Série
Lecture Notes in Computer Science
Pages
234-250
Langue
anglais
Résumé
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 Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. 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 service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.
Mots-clé
Scan statistics, Artificial Neural Networks, Classification, Clustering, Urban geography
Création de la notice
01/08/2010 11:44
Dernière modification de la notice
20/08/2019 13:13
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