Projects
Selected Projects.
EV for All: Estimating EV Charging Demand in the UK with Smart Data
“EV for All” project combines transport, energy, mobility, and demographic data to understand whether people across the UK can fairly access public electric vehicle charging. It maps current charging availability, identify inequalities, and predict where more chargers will be needed by 2030, helping government, industry, and communities plan a fairer and more reliable transition to electric vehicles. The project is funded by ESRC Smart Data Research UK programme (SDR UK) with award #UKRI4012.
AI for heat mortality: decoding climate and socio-economic nexus
This project is to innovate AI-driven techniques for modelling the complex interactions between heat exposure and human mortality, considering a full range of socio-economic determinants of weather exposure and health outcomes. This project is a collabration with Dr Eunice Lo, and funded by the Faculty of Science and Engineering’s Strategic Research Accelerator programme.
An Atlas of Economic Activities in the UK: tapping into web archives for social science research
This project uses the Web, one of the largest sources of smart data, to map economic activities in the UK at an unprecedented level of detail. Our tools and data products will allow for the continuous monitoring and mapping of economic activities. They can support policy makers to understand how economic activity evolves over time and in different places. Our project showcases the value of the Web as an untapped source of smart data and creates tools for the broader social science community to utilise these data.
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KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies
The goal of this project is to improve data-driven decision making and data analytics, specifically data analytics that involve geographic data. This project created the “KnowWhereGraph” – a knowledge graph tool that specifically enables other data-analysis knowledge tools that have a geospatial component. The project was funded by NSF’s Convergence Accelerator programmes, and an interdiciplinary collabration with over 50 team members and partners across the US. Read the paper for more details.
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