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Typology based on three density variables central to Spacematrix using cluster analysis

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Typology based on three density variables central to Spacematrix using cluster analysis

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dc.contributor.author Berghauser Pont, Meta es_ES
dc.contributor.author Olsson, Jesper es_ES
dc.date.accessioned 2018-12-18T08:37:44Z
dc.date.available 2018-12-18T08:37:44Z
dc.date.issued 2018-04-20
dc.identifier.isbn 9788490485743
dc.identifier.uri http://hdl.handle.net/10251/114048
dc.description.abstract [EN] Since the publication of the book ‘Spacematrix. Space, density and urban form’ (Berghauser Pont and Haupt, 2010), the Spacematrix method has been linked back to its theoretical foundations by Steadman (2013), is further developed using the measure of accessible density to arrive at a density measure that more closely relates to the environment as experienced by people moving through the city (Berghauser Pont and Marcus, 2014) which then is used to arrive at a multi-scalar density typology (Berghauser Pont et al. 2017). This paper will take yet another step in the development of the Spacematrix method by including the measure of network density in the classification which until now was not used to its full potential. Important for successful classification is the ability to ascertain the fundamental characteristics on which the classification is to be based where the work of Berghauser Pont and Haupt (2010) will be followed addressing three key variables: Floor Space Index (FSI), Ground Space Index (GSI) and Network density (N) where especially the last was not fully included in the earlier work. Besides a typology based on these three variables, this paper will also result in a robust statistical method that can later be used on larger samples for city-scale comparisons. Two statistical methods are tested: hierarchical clustering and centroid-based clustering and besides a general discussion about their strong and weak points, the paper shows that the hierarchical method is more convincing in distinguishing differences in both building type and street pattern that is especially captured with Network density (N). As this method is not useful for large datasets we propose a combination of the two clustering methods as the way forward. es_ES
dc.format.extent 12 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 24th ISUF International Conference. Book of Papers es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Tipology es_ES
dc.subject Classification es_ES
dc.subject Cluster analysis es_ES
dc.subject Density es_ES
dc.subject Spacematrix es_ES
dc.title Typology based on three density variables central to Spacematrix using cluster analysis es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/ISUF2017.2017.5319
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Berghauser Pont, M.; Olsson, J. (2018). Typology based on three density variables central to Spacematrix using cluster analysis. En 24th ISUF International Conference. Book of Papers. Editorial Universitat Politècnica de València. 1337-1348. doi:10.4995/ISUF2017.2017.5319 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename 24th ISUF 2017 - City and Territory in the Globalization Age es_ES
dc.relation.conferencedate Septiembre 27-29,2017 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/ISUF/ISUF2017/paper/view/5319 es_ES
dc.description.upvformatpinicio 1337 es_ES
dc.description.upvformatpfin 1348 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela 5319 es_ES


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