Understanding the Geography of Telecommuting: Mapping Work-from-Home Patterns in Urban Contexts

Abstract:

1. Background and Purpose:

The COVID-19 pandemic has fundamentally transformed how and where we work, with telecommuting becoming a persistent feature of urban life. As work-from-home (WFH) arrangements continue at levels approximately four times higher than pre-pandemic rates, there is an urgent need to understand how this shift is reshaping our cities. This project explores the spatial implications of telecommuting, examining how different socioeconomic and industry groups adopt remote work, and what this means for urban planning and transport policy.
Working alongside the research team, you will contribute to understanding how individual telework behaviours aggregate into city-level patterns, and how these patterns vary across different urban contexts. The project combines survey data analysis, spatial modelling, and scenario planning to inform evidence-based urban planning responses to the ‘new normal’ of hybrid working.

2. Research Questions:

• How do telework preferences vary across different socioeconomic status groups and industries?
• What are the spatial patterns of telecommuting at the city level, and how do these differ from traditional commuting patterns?
• How might improvements in telework support and built environment characteristics influence future telecommuting landscapes?

Skills and experience required for the project:

(1) Academic background in urban planning, geography, engineering, computer science, transport studies, or related disciplines
(2) Basic quantitative skills and familiarity with statistical concepts (experience with SPSS, R, or similar software is beneficial but not essential)
(3) Ability to work with spatial data and basic GIS operations (training will be provided as needed)
(4) Ability to work independently whilst maintaining regular contact with the research team
(5) Prior exposure to survey data analysis would be valuable, though training will be provided for working with our questionnaire dataset
(6) Any previous experience working with large spatial datasets or location-based data (such as GPS traces or mobile phone data) would be advantageous, though not essential