Identifying Patterns of Neighbourhood Change Based on Spatiotemporal Analysis of Airbnb Data in Dublin

DC FieldValueLanguage
dc.contributor.authorRabiei-Dastjerdi, Hamidreza-
dc.contributor.authorMcArdle, Gavin-
dc.date.accessioned2021-11-11T14:19:32Z-
dc.date.available2021-11-11T14:19:32Z-
dc.date.copyright2020 IEEEen_US
dc.date.issued2020-08-21-
dc.identifier.isbn9781728194042-
dc.identifier.urihttp://hdl.handle.net/10197/12629-
dc.descriptionThe 2020 4th International Conference on Smart Grid and Smart Cities (ICSGSC), Osaka, Japan, 18-21 August 2020en_US
dc.description.abstractIn general, neighbourhoods are susceptible to changes such as economic expansion or decline, new developments and infrastructure, new business and industry, gentrification or super gentrification, decline and abandonment. In this paper, we assess the ability of Airbnb data to identify locations prone to neighbourhood change using data from the Airbnb platform in Dublin, Ireland. Emerging Hotspot Analysis was utilized to identify areas where change is potentially occurring. The results of the analysis were validated by analysing literature about different types of neighbourhood change occurring in Dublin. The results show patterns of change which are occurring in many neighbourhoods in Dublin can be captured by changes in the Airbnb data. The city centre appears to have reachedsaturation point in the volume of Airbnb lettings, while other areas which are undergoing differentforms of Airbnb change are emerging as changing neighbourhoods. This paper shows that Airbnb data has a high potential to reveal underlying socioeconomic processes in the city and also highlights the importance of open access to data for urban studies and monitoring.en_US
dc.description.sponsorshipEuropean Commission Horizon 2020en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofThe 4th International Conference on Smart Grid and Smart Cities (ICSGSC 2020)en_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectNeighbourhoud changeen_US
dc.subjectSpatiotemporal analysisen_US
dc.subjectSpace-time cubeen_US
dc.subjectAirbnben_US
dc.subjectDublinen_US
dc.subjectGentrificationen_US
dc.titleIdentifying Patterns of Neighbourhood Change Based on Spatiotemporal Analysis of Airbnb Data in Dublinen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactotherhamid.rabiei@ucd.ieen_US
dc.internal.webversionshttps://www.ieee-pes.org/meetings-and-conferences/conference-calendar/monthly-view/166-technically-cosponsored-by-pes/979-2020-4th-international-conference-on-smart-grid-and-smart-cities-icsgsc-
dc.statusPeer revieweden_US
dc.identifier.doi10.1109/ICSGSC50906.2020.9248558-
dc.neeo.contributorRabiei-Dastjerdi|Hamidreza|aut|-
dc.neeo.contributorMcArdle|Gavin|aut|-
dc.description.admin2021-07-01 JG: PDF replaced with correct versionen_US
dc.date.updated2021-06-28T15:28:50Z-
dc.identifier.grantid713654-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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