Dr Mingshu Wang

2025

1. Where England’s cities are growing: Evidence from big building footprint data and explainable AI

   Xinyi Yuan, Ziqi Li, Anahid Basiri, and 1 more author

   Habitat International, Sep 2025

   Abs

   Urban development is shaped by demographic and socio-economic factors, while simultaneously influencing these dynamics. Understanding these complex relationships is essential for informed urban planning, yet previous research has struggled with fine-scale analysis over large areas due to data and methodological limitations. This study overcomes these challenges by leveraging high-resolution building footprint data and eXplainable AI (XAI). Focusing on England between 2017 and 2023, we first quantify and map the extent of new urban development and further model it as a function of population density, ethnic composition, and the Index of Multiple Deprivation (IMD) using machine-learning algorithms. We then interpret the best-performing model with SHAP values and reveal a substantial nonlinear correlation between these demographic and socio-economic factors and new urban development. This analytical framework offers a novel, scalable, interpretable approach to fine-grained urban analysis, and, for the first time, provides a nationwide quantitative assessment of how population density, ethnic composition, and deprivation jointly shape urban development in England, thereby supporting evidence-based and equitable planning and policymaking.

2. Construction enthusiasts versus demolition giants: insights from building footprint data in England

   Xinyi Yuan, Ziqi Li, Ana Basiri, and 1 more author

   Environment and Planning B: Urban Analytics and City Science, Jan 2025

   Abs

   This study uses building footprint data from the Ordnance Survey MasterMap to analyze construction and demolition activities across England from 2017 to 2023. By comparing the Topographic Object Identifiers (TOIDs) of each building between years, we identified newly constructed and demolished buildings, quantified changes, and used the bivariate color maps to visualize spatial patterns across England and within its five major cities. The study highlights the effectiveness of building footprint data in providing insights into urban changes and development trajectories, which are vital for urban planners and policymakers to understand dynamic urban processes and inform decision-making toward sustainable urban development.