Research projects

LIDA is co-producing research and innovation between the academic, commercial, government and third sector communities. Below are examples of projects conducted by researchers from LIDA and our partners.

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Spatio-temporal prediction of wind behaviour about the Bristol microclimate for use in an early warning system

  Robert Clay, Nikolaos Nikitas, Antonio Abellan - University of Leeds This project aims to predict and warn dangerous wind around key infrastructure to increase public safety and wellbeing. Critical transport infrastructure, such as the Clifton Suspension Bridge and Severn Crossings, are essential...

Probabilistic programming and data assimilation for next generation city simulation

Luke Archer, Prof. Nick Malleson, Dr. Jon Ward - University of Leeds The field of social simulation is dominated by Agent-Based Models (ABMs). Individual ‘agents’ are given simple rules, and the complex phenomena they produce downstream, termed emergence, can then be studied in detail. However, a major limitation of ABMs...

Procedural environment generation for agent-based models of crime

  Jack Lewis, Dr Dan Birks, Dr Toby Davies - University of Leeds. This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the “Digital Twins: Urban Analytics” theme within that grant & The Alan Turing Institute. Exploring how procedural...

A visual analytics workflow for investigating customers’ transactions in convenience stores

  Ivana Kocanova, Muhammad Adnan, Georgios Aivaliotis, Roy Ruddle - University of Leeds This research aims to enhance the analysis of customer’s shopping behaviour and provide new insights into the motivations behind customers shopping trips by combining novel data mining and visualisation techniques. "What...

SPENSER Synthetic Population Estimation and Scenario Projection Model

  Gonzalo Cruz Garcia, Nik Lomax, Andrew Smith - University of Leeds SPENSER provides the framework for estimates of population which are dynamic and high resolution (at household level), and a comprehensive set of tools for user customisable scenario projections. With increasing populations and...

Uncovering behavioural patterns within spatial-temporal data using network science and unsupervised machine learning

  Benjamin Isaac Wilson, Alison Heppenstall, Roger Beecham and Minh Le Kieu - University of Leeds How can we explore for hidden patterns of behaviour in the vast amounts of available data captured by networked sensors within our cities using a combination of machine learning and network science methods?  The...

Extracting actionable insights from free text police data

  Alex Coleman, Daniel Birks, Nick Malleson, Graham Farrell - University of Leeds Can creative writing catch criminals? When a crime occurs large amounts of information are captured within the narrative description of the incident. This data contains useful information that is not fully utilised...

Improving causal inference in real-world data analytics

Causal inference in real-world data is the pinnacle aim of applied data science, unfortunately, it is also the most challenging. New graphic methods - such as causal directed acyclic graphs (DAGs) - promise a revolution in the estimation of causal effects but are relatively unrecognised and untested in applied health and social...

FixMyStreet: Micro-geographies of civic engagement and neighbourhood environmental quality

Professor Alasdair Rae, University of Sheffield This project was funded through the Consumer Data Research Centre Innovation Fund, backed by the ESRC. Developed to drive innovative social science research across a broad range of disciplines...

PigSustain

Aims:   To use a multi-disciplinary, integrated systems approach to model and assess the resilience...