Gavin Mcardle
Biographic Data
| ID | 2744917 |
|---|---|
| NAME | Gavin Mcardle |
| GIVEN NAMES | Gavin |
| FAMILY NAME | Mcardle |
| SIGNATURE | MCARDLE G |
| AFFILIATIONS | University College Dublin |
| ORCID | 0000-0003-0613-546X |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 218 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 5 |
Movement Research in the Era of Big Data, AI, and Open Science
The increasing availability of movement data and rapid advancements in artificial intelligence have opened new frontiers in movement pattern analysis with implications for a wide range of domains such as urban planning, transportation management, public health, pandemic management, disaster response, and wildlife conservation. The sensitive nature and heterogeneity of novel movement data, however, create technical and methodological challenges ar…
Unpacking the adaptive reuse potential of underused spaces in Ireland
A Map-Based Recommendation System and House Price Prediction Model for Real Estate
In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent …
Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data
Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, …
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these a…
The praxis and politics of building urban dashboards
This paper critically reflects on the building of the Dublin Dashboard – a website built by two of the authors that provides citizens, planners, policy makers and companies with an extensive set of data and interactive visualizations about Dublin City, including real-time information – from the perspective of critical data studies. The analysis draws upon participant observation, ethnography, and an archive of correspondence to unpack the buildin…
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question 'wha…
Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards
Since the mid-1990s a plethora of indicator projects have been developed and adopted by cities seeking to measure and monitor various aspects of urban systems. These have been accompanied by city benchmarking endeavours that seek to compare intra- and inter-urban performance. More recently, the data underpinning such projects have started to become more open to citizens, more real-time in nature generated through sensors and locative/social media…
Urban indicators and dashboards
The three commentaries to our paper provide some interesting complementarities to our \narguments and we would like to thank the authors for taking the time to read and \ncomment on our work. Although coming from quite different perspectives, Mike Batty \nas an urban modeller and planner and Matt Wilson as a critical geographical information \nsystem (GIS) theorist, largely support our concerns around urban indicator and dashboard \nprojects: tha…
Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards
Since the mid-1990s a plethora of indicator projects have been developed and adopted by cities seeking to measure and monitor various aspects of urban systems. These have been accompanied by city benchmarking endeavours that seek to compare intra- and inter-urban performance. More recently, the data underpinning such projects have started to become more open to citizens, more real-time in nature generated through sensors and locative/social media…
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question 'wha…
The praxis and politics of building urban dashboards
This paper critically reflects on the building of the Dublin Dashboard – a website built by two of the authors that provides citizens, planners, policy makers and companies with an extensive set of data and interactive visualizations about Dublin City, including real-time information – from the perspective of critical data studies. The analysis draws upon participant observation, ethnography, and an archive of correspondence to unpack the buildin…
Urban indicators and dashboards
The three commentaries to our paper provide some interesting complementarities to our \narguments and we would like to thank the authors for taking the time to read and \ncomment on our work. Although coming from quite different perspectives, Mike Batty \nas an urban modeller and planner and Matt Wilson as a critical geographical information \nsystem (GIS) theorist, largely support our concerns around urban indicator and dashboard \nprojects: tha…
Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data
Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, …
Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards
Since the mid-1990s a plethora of indicator projects have been developed and adopted by cities seeking to measure and monitor various aspects of urban systems. These have been accompanied by city benchmarking endeavours that seek to compare intra- and inter-urban performance. More recently, the data underpinning such projects have started to become more open to citizens, more real-time in nature generated through sensors and locative/social media…
Urban indicators and dashboards
The three commentaries to our paper provide some interesting complementarities to our \narguments and we would like to thank the authors for taking the time to read and \ncomment on our work. Although coming from quite different perspectives, Mike Batty \nas an urban modeller and planner and Matt Wilson as a critical geographical information \nsystem (GIS) theorist, largely support our concerns around urban indicator and dashboard \nprojects: tha…
The praxis and politics of building urban dashboards
This paper critically reflects on the building of the Dublin Dashboard – a website built by two of the authors that provides citizens, planners, policy makers and companies with an extensive set of data and interactive visualizations about Dublin City, including real-time information – from the perspective of critical data studies. The analysis draws upon participant observation, ethnography, and an archive of correspondence to unpack the buildin…
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question 'wha…
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these a…
A Map-Based Recommendation System and House Price Prediction Model for Real Estate
In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent …
Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data
Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, …
Movement Research in the Era of Big Data, AI, and Open Science
The increasing availability of movement data and rapid advancements in artificial intelligence have opened new frontiers in movement pattern analysis with implications for a wide range of domains such as urban planning, transportation management, public health, pandemic management, disaster response, and wildlife conservation. The sensitive nature and heterogeneity of novel movement data, however, create technical and methodological challenges ar…
Unpacking the adaptive reuse potential of underused spaces in Ireland
Computer Science (6 works) · Human Mobility and Location-Based Analysis (6 works) · Data science (5 works) · Geographic Information Systems Studies (4 works) · Artificial Intelligence (3 works) · Big data (3 works) · Business (3 works) · Dashboard (3 works) · Data mining (3 works) · Political science (3 works)