Professor Ed Manley

Ed Manley

Position

Turing Interest Group organiser

Former position

Turing Fellow

Partner Institution

Bio

Ed Manley is a Professor of Urban Analytics in the School of Geography, and member of the Centre for Spatial Analysis and Policy (CSAP) research cluster. He is affiliated with the Leeds Institute for Data Analytics (LIDA).

Ed's research aims to deepen our quantitative understanding of human behaviour in cities, and how these behaviours shape urban dynamics. His research combines quantitative methods, such as exploratory data analysis, machine learning, agent-based modelling, and network modelling, with theory drawn from diverse disciplines such as urban geography, transportation, spatial cognition, judgment and decision-making, and sociology.

He also has interests in the role of data visualisation in communicating complex datasets for decision-making. Ed is a Fellow of the Royal Geographical Society (RGS), Royal Society for the encouragement of Arts, Manufactures and Commerce (RSA), and Higher Education Academy (HEA). He is an Associate Editor of the Applied Spatial Analysis and Policy journal, and chairs the GIScience Research Group at the RGS. He received his Engineering Doctorate from University College London in 2013.

Research interests

Ed's research at the Turing explores new ways to analyse, model and predict human movement in cities. In collaboration with psychologists, neuroscientists and geographers, his project seeks to develop detailed models of spatial cognition and decision-making in real-world spaces, combining large-scale observed movement data with advances in reinforcement learning, graphical modelling, and spatial representations.

These models of behaviour will form the basis of agent-based simulations of urban dynamics, enabling the improved prediction and analysis of the impact caused by infrastructural changes and disruption to 'usual' city functions. These computational representations of human behaviour also present the opportunity for developing new pathways for human-machine interaction in relation to navigation and urban space.