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Mapping a historic neighbourhood through user-generated content: the case of Alfama, Lisbon (Portugal)

2022-06-20Documento de conferência Acesso aberto
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dc.contributor.authorTang, Vicente
dc.contributor.authorPuri, Jaskaran
dc.contributor.authorPainho, Marco
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2022-06-20T22:14:02Z
dc.date.available2022-06-20T22:14:02Z
dc.date.issued2022-06-20
dc.descriptionTang, V., Puri, J., & Painho, M. (2022). Mapping a historic neighbourhood through user-generated content: the case of Alfama, Lisbon (Portugal). In E. Parseliunas, A. Mansourian, P. Partsinevelos, & J. Suziedelyte-Visockiene (Eds.), Proceedings of the 25th AGILE Conference on Geographic Information Science, 2022 (Vol. 3, pp. 1-8). (AGILE: GIScience Series; Vol. 63). https://doi.org/10.5194/agile-giss-3-63-2022
dc.description.abstractParticipant-based methods aimed at extracting neighbourhood definitions are labour and time intensive. On the other hand, user-generated content (UGC) can provide locations to assess the extent of neighbourhoods. We investigated the definitions of Alfama - a historic neighbourhood in Lisbon (Portugal) - using six sources of UGC and applied a modification of the DBSCAN algorithm developed in the literature. By generating shapes from each source, we were able to visually and quantitatively evaluate their agreement as well as their differences.We demonstrate how different profiles of user activity from each source yielded varied geographies of Alfama. Although discrete representations are not the optimal choice, practical applications such as urban planning usually demand sharp definitions. Lastly, our approach can be extended and improved by adding more sources of UGC data and by picking other case studies.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent1127685
dc.identifier.doi10.5194/agile-giss-3-63-2022
dc.identifier.otherPURE: 44857465
dc.identifier.otherPURE UUID: 5013599e-ebd8-4943-8ccf-eb4acbd2f2d4
dc.identifier.othercrossref: 10.5194/agile-giss-3-63-2022
dc.identifier.otherWOS: 001191137600063
dc.identifier.otherORCID: /0000-0003-1136-3387/work/151384499
dc.identifier.urihttp://hdl.handle.net/10362/140354
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001191137600063
dc.identifier.urlhttps://agile-giss.copernicus.org/articles/3/63/2022/
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FGES-URB%2F1429%2F2021/PT
dc.relationCityMe - Mapping Regions in the City from Citizens´ Perceptions
dc.subjectNeighbourhoods
dc.subjectUser-generated content
dc.subjectA-DBSCAN
dc.subjectAlfama
dc.titleMapping a historic neighbourhood through user-generated content: the case of Alfama, Lisbon (Portugal)en
dc.typeconference object
degois.publication.firstPage1
degois.publication.lastPage8
degois.publication.titleProceedings of the 25th AGILE Conference on Geographic Information Science, 2022
degois.publication.title25th AGILE Conference on Geographic Information Science, 2022
degois.publication.volume3
dspace.entity.typePublication
oaire.awardNumberEXPL/GES-URB/1429/2021
oaire.awardTitleCityMe - Mapping Regions in the City from Citizens´ Perceptions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FGES-URB%2F1429%2F2021/PT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication81487ae2-a881-41e9-8d7c-fc14533940b7
relation.isProjectOfPublication.latestForDiscovery81487ae2-a881-41e9-8d7c-fc14533940b7

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