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

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.

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

“…STC is based on time geography or time-space geography introduced by Hägerstrand [48] to extract spatiotemporal patterns in the data [49] or to understand the movement of objects through space and time [50]. STC transforms data into a cube with three axes which are longitudinal, latitudinal, and temporal axes, respectively, and counts the number of points in equal bins of this cube (Figure 2) [35]. In this research, x and y identify the location of transactions, and z is the time of registered property transactions in that location or area of the city.…”

“…The next step of EHA is using the Mann-Kendall [51] test to identify spatial and statistical patterns in previously calculated Getis-Ord Gi* statistics (hotspots and coldspots of property transactions). More details of the Mann-Kendall test and EHA can be found in [35,49,51]. EHA, in the first step, calculates Getis-Ord G i * [42] or hotspots and coldspots of property transactions using Equations ( 3)- (5).…”

“…The next step of EHA is using the Mann-Kendall [51] test to identify spatial and statistical patterns in previously calculated Getis-Ord G i * statistics (hotspots and coldspots of property transactions). More details of the Mann-Kendall test and EHA can be found in [35,49,51].…”

“…Any type of change in characteristics of a neighborhood or neighborhood change [6,35] can be traced back to the real estate market, especially price change [36]. Therefore, housing price, an index of housing market performance, can be used as a proxy of neighborhood change and as an indicator of changes in characteristics of locations in the city.…”

“…From an economic perspective, if the number of the transaction changes dramatically, it is a shift in supply and demand curves in that location or neighborhood. Therefore, it can be concluded that any shift in supply and demand curves can lead to or be the outcome of a type of change in socioeconomic and physical characteristics of a neighborhood or neighborhood change [35] regardless of the internal or external causing factors. Finally, if this change is consistent with a similar change pattern in house prices in a neighborhood it reveals a type of upgrading or decaying neighborhood change such as gentrification and supergentrification, or a sign of a recently developed area [35].…”