PhD Studentship: Chrono-urbanism and well-being. Department of Computer Science, UQ-Exeter Institute PhD Studentship (Funded) for January 2027 Entry.
University of Exeter - Department of Computer Science
| Qualification Type: | PhD |
|---|---|
| Location: | Devon, Exeter |
| Funding for: | UK Students, EU Students, International Students, Self-funded Students |
| Funding amount: | Full tuition fees, stipend of £21,805 per annum, travel funds of up to £15,000, and RTSG of £10,715 are available over the 3.5 year studentship |
| Hours: | Full Time |
| Placed On: | 31st March 2026 |
| Closes: | 24th April 2026 |
| Reference: | 5842 |
Project Description
Chrono-urbanism is an urban planning approach that prioritises time as a core resource, aiming to reduce travel time by placing essential services, leisure, and work within a short walk or bike ride. This approach seeks to create sustainable and healthy cities through “urban villages.” It provides the theoretical framework for concepts like the 15-minute city, which defines a 15-minute limit for a “short walk or bike ride.” While this idea is appealing at first sight, it raises several problems.
- The assumption that 15 minutes is an appropriate threshold across all contexts, populations, and types of amenities remains largely unexamined. Why 15, rather than 10, 20, or 30 minutes?
- The assumption that the health and wellbeing benefits of this model will be evenly distributed is largely untested. There is a risk that “urban villages” will be created mainly in areas with wealthier and healthier populations, leaving poorer and less healthy groups in “urban deserts” with limited access to amenities, active travel opportunities, and health-promoting environments.
This project will empirically test both assumptions to develop a more nuanced, evidence-based framework linking chrono-urban accessibility to health and socioeconomic outcomes. It will:
- Challenge the fixed-threshold paradigm by introducing the concept of the “N-minute city,” where N is a time variable that differs across socioeconomic contexts, amenity types (ranging from pharmacies and doctors' surgeries to supermarkets and bus stops), and urban forms (e.g., low-rise versus high-rise). Amenity locations will be extracted from OpenStreetMap and validated against official datasets. A composite N will be computed as a weighted average across amenity types, with weights reflecting the relative necessity of each service (for example, a pharmacy is more essential at close proximity than a post office). The quality of amenities, not merely their presence, will be incorporated into the N measurement by drawing on open-access, crowd-sourced data such as online customer reviews. This work will produce a generalisable, open-source tool for computing variable-threshold accessibility indices.
- Examine the relationships between N and health and socioeconomic outcomes, including self-reported health status, prevalence of chronic disease, mental wellbeing, physical activity levels, income, deprivation, educational attainment, vehicle ownership, and housing typology at fine spatial scales. The composite N will be further disaggregated by age, gender, and household composition to reveal how the health consequences of urban accessibility differ across population groups. This work will generate actionable insights for urban planners and public health practitioners seeking to improve population health and reduce spatial inequities in service provision.
The project involves six cities: London and Exeter in the UK, Brisbane and Melbourne in Australia, and São Paulo and Fortaleza in Brazil. These case studies cover diverse urban sizes, forms, wealth levels, and health profiles. The project uses spatial analysis and GIS, including network-based walkability modelling with OpenStreetMap, large-scale human mobility analysis with anonymised Call Detail Records and geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data science, network science, and spatial analytics.
Contact
Questions about this project should be directed to Professor Ronaldo Menezes at R.Menezes@exeter.ac.uk
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