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

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 neighborhoods 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.

Measuring Spatial Accessibility to Healthcare Facilities in Isfahan Metropolitan Area in Iran

“…Although urban (health) facility services infrastructure is a product of different private, public, and government actors, socioeconomic, technological, cultural factors, and planning mechanisms (Zeaiean et al, 2005;Lowe et al, 2015;Chapple, 2014;Garau, Pavan, 2018;Bibri, Krogstie, 2017;Rabiei-Dastjerdi, Matthews, 2021;Rabiei-Dastjerdi et al, 2022), this study aimed to highlight the problem the spatial accessibility to urban health services in Isfahan, a major city in Iran as a developing country, for health policy designers and urban planners through mapping different indexes of accessibility. The implemented methods in this article can be used in other cities with similar spatial structures and socioeconomic contexts, especially in developing countries where access to data is a serious barrier to research in public health and urban studies.…”

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Blackspots

“…Palayamkottai is the central business center located on the eastern bank of the amirabarani River. e town has a number of educational institutions and administrative offices like district police headquarters, medical colleges, commercial shops, and hospitals, attracting more traffic from the outskirts of the city, causing high traffic flow in the city [9,27,29,31,33,[39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]. e Municipal Corporation Act of 1994 established a municipal corporation to operate Tirunelveli.…”

“…Earlier conventional methods were used to find the blackspot locality in which only the number of accidents is considered. In most of the traditional methods, they used the statistical Poisson distribution, and the Empirical Bayes technique was utilized [9,31,39,[42][43][44][45][46][47][48][49][50][51][52][53]57]. e goal of the identification procedure is to pick a few sites from a large number of possibilities to increase safety.…”

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Black spots<strong> </strong><strong> </strong>

“…Palayamkottai is the central business center located on the eastern bank of the Thamirabarani River. The town has a number of educational institutions and administrative offices like District police headquarters, medical colleges, commercial shops, and hospitals, attracting more traffic from the outskirts of the city, causing high traffic flow in the city [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. The Municipal Corporation Act of 1994 established a municipal corporation to operate Tirunelveli.…”

“…Earlier conventional methods were used to find the black spot locality in which only the number of accidents is considered. In most of the traditional methods, they used the statistical Poisson distribution, and the Empirical Bayes technique was utilized [42,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. The goal of the identification procedure is to pick a few sites from a vast number of possibilities in order to increase safety.…”