land Article Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin Hamidreza Rabiei-Dastjerdi * and Gavin McArdle   Citation: Rabiei-Dastjerdi, H.; McArdle, G. Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin. Land 2021, 10, 566. https:// doi.org/10.3390/land10060566 Academic Editors: Miroslaw Belej, Malgorzata Krajewska and Izabela R ˛ acka Received: 19 April 2021 Accepted: 25 May 2021 Published: 28 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). School of Computer Science and CeADAR, University College Dublin, Dublin 4, Ireland; gavin.mcardle@ucd.ie * Correspondence: hamid.rabiei@ucd.ie Abstract: The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neigh- borhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted. Keywords: real estate market; residential property; exploratory spatiotemporal analysis; small area; Dublin 1. Introduction The real estate market is attractive from different perspectives to analyze urban prob- lems such as spatial inequality [1,2], gentrification [3], land development [4], and urban economy [5]. Housing prices are not constant in all areas of the city, and intraurban varia- tion of residential properties price is determined by different characteristics of the location, including urban facilities and services, environmental and socioeconomic conditions, and security and safety [6,7]. On the other hand, the property price is changing over time because of changes in endogenous and exogenous factors such as the local and national economy [8–10], urban policy [11], macro-economic factors such as financial crises [12], world real estate economy, and stock markets [13]. Therefore, we can conclude that location and time of residential property transactions are two of the primary components of a residential property price if all other building characteristics are constant such as the size of the building and the lot, the number of rooms, and energy system [14]. Land 2021, 10, 566. https://doi.org/10.3390/land10060566 https://www.mdpi.com/journal/land