You are in the accessibility menu

Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/113541
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRodrigues da Silva, Antonio Nelson-
dc.contributor.authorManzato, Gustavo Garcia-
dc.contributor.authorSantos Pereira, Heber Tiago-
dc.date.accessioned2014-12-03T13:11:47Z-
dc.date.accessioned2016-10-25T20:15:07Z-
dc.date.available2014-12-03T13:11:47Z-
dc.date.available2016-10-25T20:15:07Z-
dc.date.issued2014-04-01-
dc.identifierhttp://dx.doi.org/10.1016/j.jtrangeo.2014.03.001-
dc.identifier.citationJournal Of Transport Geography. Oxford: Elsevier Sci Ltd, v. 36, p. 79-88, 2014.-
dc.identifier.issn0966-6923-
dc.identifier.urihttp://hdl.handle.net/11449/113541-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/113541-
dc.description.abstractThe concept of Functional Urban Regions (FURs), also called Metropolitan Regions (MRs), is not simple. It is clear, though, that they are not simply a combination of adjacent municipalities or areas. Different methods can be used for their definition. However, especially in developing countries, the application of some methods is not possible, due to the unavailability of detailed data. Alternative approaches have been developed based on spatial analysis methods and using variables extracted from available data. The objective of this study is to compare the results of two spatial analysis methods exploring two variables: population density and an indicator of transport infrastructure supply. The first method regards Exploratory Spatial Data Analyses tools, which define uniform regions based on specific variables. The second method used the same variables and the spatial analysis technique available in the computer program SKATER - Spatial 'K'luster Analysis by Tree Edge Removal. Assuming that those classifications of regions with similar characteristics can be used for identifying potential FURS, the results of all analyses were compared with one another and with the 'official' MR. A combined approach was also considered for comparison, but none of the results match the existing MR boundaries, what challenges the official definitions. (C) 2014 Elsevier Ltd. All rights reserved.en
dc.format.extent79-88-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectFunctional urban regionsen
dc.subjectMetropolitan regionsen
dc.subjectSpatial analysisen
dc.subjectSpatial statisticsen
dc.subjectCluster analysisen
dc.titleDefining functional urban regions in Bahia, Brazil, using roadway coverage and population density variablesen
dc.typeoutro-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUniv Sao Paulo, Sao Carlos Sch Engn, Dept Transportat Engn, BR-13560590 Sao Carlos, SP, Brazil-
dc.description.affiliationSao Paulo State Univ, Fac Engn Bauru, Dept Civil & Environm Engn, BR-17033360 Bauru, SP, Brazil-
dc.description.affiliationUnespSao Paulo State Univ, Fac Engn Bauru, Dept Civil & Environm Engn, BR-17033360 Bauru, SP, Brazil-
dc.identifier.doi10.1016/j.jtrangeo.2014.03.001-
dc.identifier.wosWOS:000336700300008-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofJournal Of Transport Geography-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

There are no files associated with this item.
 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.