Lead Supervisor: Nick Malleson, University of Leeds
Other Supervisors: Jon Ward (School of Mathematics), Andy Evans (School of Geography)
Contact Email: email@example.com
Partners: University of Leeds
External Partners: Leeds City Council
Start Date: October 2017
Identifying the factors that encourage and discourage attendance in urban spaces is vital from both academic and practitioner perspectives. For policy makers, an understanding of what attracts people to city centres, and what discourages them, is vital for urban planning and emergency management. From an academic perspective, understanding the drivers of footfall is essential in order to answer questions of mobility, inclusivity, and accessibility of opportunities.
Simply quantifying footfall, let alone dissecting the underlying drivers, is extremely challenging. Although there are a series of diverse ‘big’ datasets that are emerging which can contribute to footfall estimates, none in isolation provide a comprehensive picture. In addition, there are no standard methods that are appropriate for assimilating dynamic, diverse, noisy, and biased data sources to create a complete picture.
To address these gaps in our understanding of urban dynamics, this project will embark on an ambitious programme of methodological development and empirical data analysis. It will adapt relevant methods from fields such as computer science (e.g. machine learning and artificial intelligence), atmospheric modelling (e.g. data assimilation and ensemble modelling), and geography (e.g. GIS and spatial analysis) to create a robust model of footfall. Indicative data source that will underpin the analysis include: footfall data collected by Leeds City Council CCTV cameras; Census workday population estimates; dynamic weather data; geo-located Twitter data; times of public events and holidays; business opening hours; Wi-Fi sensor footfall data; and others as they become available. There is great potential to leverage these data as both an explanatory tool for understanding what has been driving city-centre attendance and as a predictive tool for forecasting future footfall under different scenarios. Leeds City Council are actively involved in designing the research questions, identifying data sources, and will jointly supervise the research.
Reference number LE03
Deadline for applications – 30th April 2017