“The ballet of the good city sidewalk never repeats itself from place to place, and in any one place is always replete with new improvisations.”
Originally from Dublin, I am currently Associate Professor at the Centre for Advanced Spatial Analysis (CASA) at University College London where I lead a research group focused on data-driven models for economic development and the emergence of complexity for urban systems.
I was previously a Senior Research Fellow at the Mathematical Institute at the University of Oxford. Before this I was a Fulbright Scholar and Postdoctoral Research Fellow at the Center for International Development at the Harvard Kennedy School following my PhD (mathematics) at Imperial College. I am also founder and Editor in Chief of Angle – a journal based at Imperial College focusing on the intersection of policy, politics and science – since 2009. More …
PEAK Urban is a 4 year international multidisciplinary programme funded by the Global Challenges Research Fund (RCUK GCRF) involving researchers at the University of Oxford, Peking University, University of Cape Town, the Indian Institute for Human Settlements, and EAFIT University (Colombia).
The team from the Oxford Mathematical Institute will focus on studying cities as complex social, spatial and economic systems, with particular themes including knowledge diffusion, skills and agglomeration economies, industrial complexity and urban form. We will use a broad range of analytical tools from network science to machine learning.
Network modelling of the UK’s urban skill base
The UK Government is encouraging cities to prepare for the economy of the future through, amongst other things, investing in skills. Policy makers, however, face a ‘data deficit’ on skills, which impedes strategic planning and decision-making. Through a novel data modelling approach, a new project is aiming to shed light on the industrial diversification potential of UK cities from a skills-based perspective. This project received funding from the Turing-HSBC-ONS Economic Data Science Awards 2018.
The Oxford Martin Programme on Informal Cities
Globally, the informal sector is estimated to represent over 60 percent of cities, with the vast majority of jobs in many of the world’s emerging economies considered to be informal. Understanding informal cities, and the millions of lives lived within them, is fundamental to meeting the SDGs. Yet the diverse and dynamic nature of informal cities creates a huge challenge for large-scale data collection and analysis. Find out more.
My research lies at the intersection of a number of fields including urban systems, economic complexity and economic geography, development, and network science.
- Samuel Heroy
- Daniel Straulino
- Rafael Prieto Curiel
- Samira Barzin
- John Fitzgerald [2019-]
- Mattie Landman [2018-]
- Sanna Ojanpera [2017-, online labour and development]
- Nils Rochowicz [2017-, technological progress]
- Francesca Froy [UCL, urban agglomeration]
- Silvia Nunez Gomez [2019, industry automation in Mexico]
- Ioan Alexandru Puiu [2018, network comparison]
- Yufei Zeng [2018, knowledge flow in Ireland]
- Zhe Wang [2018, industry complexity]
- Francois Hulot [2017, urban agglomeration]
- Steffan Ridderbusch [2017, labour networks]
- Haoting Zhang [green diversification paths]
URBAN SYSTEMS AND ECONOMIC COMPLEXITY, GEOGRAPHY AND DEVELOPMENT
Heroy S, Loaiza I, Pentland A & O’Clery N (2020) Controlling COVID-19: Labor structure is more important than lockdown policy. ArXiv:2010.14630.
Ying F & O’Clery N (2020) Modelling COVID-19 transmission in supermarkets using an agent-based model. ArXiv:2010.07868.
Prieto Curiel R, Walther O & O’Clery N (2020) Uncovering the internal structure of Boko Haram through its mobility patterns. Applied Network Science 5: 28.
Landman M & O’Clery N (2020) The impact of the Employment Equity Act on female inter-industry labour mobility and the gender wage gap in South Africa. United Nations WIDER Working Paper 2020/52
O’Clery N, Prieto Curiel R & Lora E (2019) Commuting times and the mobilisation of skills in emergent cities. Applied Network Science 4: 118.
O’Clery N, Flaherty E & Kinsella S (2019) Modular structure in labour networks reveals skill basins. ArXiv:1909.03379
O’Clery N, Heroy S, Hulot F & Beguerisse-Diaz M (2019) Unravelling the forces underlying urban industrial agglomeration. ArXiv:1903.09279 [to appear in Handbook of Cities and Networks, Edward Elgar Publishing]
Diodato D, Neffke F & O’Clery N (2018) Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time. Journal of Urban Economics 106 (1-26).
O’Clery N, Yildirim M & Hausmann R (2016) Productive Ecosystems and the Arrow of Development. ArXiv:1807.03374.
O’Clery N, Gomez A & Lora E (2016) The Path to Labour Formality: Urban Agglomeration and the Emergence of Complex Industries. Center for International Development Working Paper.
O’Clery N (2015) A Tale of Two Clusters: The Evolution of Ireland’s Economic Complexity since 1995. Journal of the Statistical and Social Inquiry Society of Ireland, One Hundred and Sixty-Eight Session.
Straulino D, Landman M & O’Clery N (2020) A bi-directional approach to comparing the modular structure of networks. ArXiv:2010.06568.
O’Clery N, Yuan Y, Stan G-B, Barahona M (2018) Global Network Prediction from Local Node Dynamics. ArXiv:1809.00409.
Schaub M, O’Clery N, Billeh Y, Delvenne J-C, Lambiotte R & Barahona M (2016) Graph partitions and cluster synchronization in networks of oscillators. Chaos 2016;26 (9).
O’Clery N, Yuan Y, Stan G-B & Barahona M (2013) Observability and coarse graining of consensus dynamics through the external equitable partition. Physical Review E. 2013;88 (4).
O’Clery N (2013) Node Dynamics on Graphs Dynamical Heterogeneity in Consensus, Synchronisation and Final Value Approximation for Complex Networks. PhD Thesis, Imperial College London.
Tackling global challenges, one issue at a time. From energy and the environment to economics, development and global health, Angle focuses on the intersection of science, policy and politics in an evolving and complex world.