Work

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

Year: 2020

Type: conference-paper

Abstract: In 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 reached saturation point in the volume of Airbnb lettings, while other areas which are undergoing different forms 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.

Source: International Conference on Smart Grid and Smart Cities (ICSGSC)

DOI: 10.1109/icsgsc50906.2020.9248558

Authors: Hamidreza Rabiei-Dastjerdi, Gavin McArdle

Institution: University College Dublin

Language: English

Topic: Sharing Economy and Platforms

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Sustainable cities and communities, Reduced inequalities

Open Access status: green

Funder: European Commission