Tourists' digital footprint in cities: Comparing Big Data sources

There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of...

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Detalhes bibliográficos
Autores: Salas Olmedo, María Henar, Moya Gómez, Borja, García Palomares, Juan Carlos, Gutiérrez Puebla, Javier
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/95123
Acesso em linha:https://hdl.handle.net/20.500.14352/95123
Access Level:acceso abierto
Palavra-chave:910.2:004
Urban tourism
Big Data
Photo-sharing services
Social networks
Spatial analysis
GIS
Sistemas de información geográfica
Geografía humana
Turismo
5401.02 Geografía de las Actividades
5403.01 Geografía Cultural
5312.90 Economía Sectorial: Turismo
5403 Geografía Humana
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oai_identifier_str oai:docta.ucm.es:20.500.14352/95123
network_acronym_str ES
network_name_str España
repository_id_str
spelling Tourists' digital footprint in cities: Comparing Big Data sourcesSalas Olmedo, María HenarMoya Gómez, BorjaGarcía Palomares, Juan CarlosGutiérrez Puebla, Javier910.2:004Urban tourismBig DataPhoto-sharing servicesSocial networksSpatial analysisGISSistemas de información geográficaGeografía humanaTurismo5401.02 Geografía de las Actividades5403.01 Geografía Cultural5312.90 Economía Sectorial: Turismo5403 Geografía HumanaThere is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected-accommodation). The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces and spaces specialising in one or various activities. The main conclusion is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.ElsevierUniversidad Complutense de Madrid20182018-01-0120182018-01-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/95123reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengCM Not available S2015%2FHUM-3427European Commission http://dx.doi.org/10.13039/501100000780 Seventh Framework Programme 611307Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 FPDI FPDI-2013-17001open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/951232026-06-02T12:44:21Z
dc.title.none.fl_str_mv Tourists' digital footprint in cities: Comparing Big Data sources
title Tourists' digital footprint in cities: Comparing Big Data sources
spellingShingle Tourists' digital footprint in cities: Comparing Big Data sources
Salas Olmedo, María Henar
910.2:004
Urban tourism
Big Data
Photo-sharing services
Social networks
Spatial analysis
GIS
Sistemas de información geográfica
Geografía humana
Turismo
5401.02 Geografía de las Actividades
5403.01 Geografía Cultural
5312.90 Economía Sectorial: Turismo
5403 Geografía Humana
title_short Tourists' digital footprint in cities: Comparing Big Data sources
title_full Tourists' digital footprint in cities: Comparing Big Data sources
title_fullStr Tourists' digital footprint in cities: Comparing Big Data sources
title_full_unstemmed Tourists' digital footprint in cities: Comparing Big Data sources
title_sort Tourists' digital footprint in cities: Comparing Big Data sources
dc.creator.none.fl_str_mv Salas Olmedo, María Henar
Moya Gómez, Borja
García Palomares, Juan Carlos
Gutiérrez Puebla, Javier
author Salas Olmedo, María Henar
author_facet Salas Olmedo, María Henar
Moya Gómez, Borja
García Palomares, Juan Carlos
Gutiérrez Puebla, Javier
author_role author
author2 Moya Gómez, Borja
García Palomares, Juan Carlos
Gutiérrez Puebla, Javier
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 910.2:004
Urban tourism
Big Data
Photo-sharing services
Social networks
Spatial analysis
GIS
Sistemas de información geográfica
Geografía humana
Turismo
5401.02 Geografía de las Actividades
5403.01 Geografía Cultural
5312.90 Economía Sectorial: Turismo
5403 Geografía Humana
topic 910.2:004
Urban tourism
Big Data
Photo-sharing services
Social networks
Spatial analysis
GIS
Sistemas de información geográfica
Geografía humana
Turismo
5401.02 Geografía de las Actividades
5403.01 Geografía Cultural
5312.90 Economía Sectorial: Turismo
5403 Geografía Humana
description There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected-accommodation). The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces and spaces specialising in one or various activities. The main conclusion is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/95123
url https://hdl.handle.net/20.500.14352/95123
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv CM Not available S2015%2FHUM-3427
European Commission http://dx.doi.org/10.13039/501100000780 Seventh Framework Programme 611307
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 FPDI FPDI-2013-17001
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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