Background and Motivation
As a student of Economics and Finance and an aspiring entrepreneur, I’ve been fascinated by the evolution of marketplaces and how data can reveal stories about economic, social, and technological shifts. In recent years, the rise of e-commerce giants like Amazon has radically transformed retail, leading many to assume that physical shops are obsolete. However, I strongly believe in the enduring value of physical retail—not just for commerce, but for community. High streets and town centres offer real-world connection, local identity, and spaces for people to interact, learn, and discover.
This personal belief, combined with my interest in entrepreneurship, led me to seize the opportunity offered by the Laidlaw Programme to deeply engage with this theme under the supervision of Dr Andy Newing. My research will leverage Ordnance Survey Points of Interest (POI) data, which spans from 2015 to 2025, to identify and visualise key trends in retail change across the UK. In doing so, I aim to develop the skills necessary to conduct effective market research and location analysis, skills that will be essential as I pursue my long-term goal of launching and running successful, ethical businesses.
Research Aim and Questions
This project aims to understand the transformation of British retail landscapes by analysing 10 years of POI data. Specifically, I want to trace how the composition and distribution of retailers and services within retail centres have changed, both nationally and within major cities like Leeds and London.
At this point, my central research questions are:
- How has the retail mix changed in key retail centres across Great Britain between 2015 and 2025?
- What patterns emerge in the expansion, contraction, or relocation of specific retail brands (e.g. Debenhams, M&S, Boots, Costa)?
- How have national events like Brexit, the COVID-19 pandemic, and the inflation crisis influenced retail change at local levels?
- Can spatial patterns in retail change help predict where future opportunities might exist for entrepreneurs or policymakers?
Hypothesis
I hypothesise that:
- Physical retail is not disappearing but shifting, with certain types of retailers (e.g. food and drink, experiential services) adapting more effectively to socio-economic shocks.
- Large cities like London and Leeds have shown greater resilience in retail diversity, while smaller towns have faced sharper declines in traditional high street occupiers.
- The failure of major retail chains due to external economic pressures has opened up prime locations for innovative, adaptable businesses to step in.
Impact and Future Vision
This research will serve as a foundation for my future business ventures, particularly in understanding where to open a store, how to assess demand, and how to interpret spatial trends in consumer behaviour. It also contributes to academic efforts to understand post-pandemic urban resilience, the role of retail in social cohesion, and the real-world implications of macroeconomic events.
By working through real data challenges, I will develop the resilience, adaptability, and strategic thinking essential to entrepreneurship. Beyond technical skills, I will gain confidence in handling large datasets, extracting business insights from spatial trends, and telling compelling stories through data.
Alignment with the Laidlaw Programme
This research is not only aligned with my academic interests—it reflects my deeper motivation to grow as a responsible and effective leader. Through the Laidlaw Programme, I seek to refine my leadership by:
- Tackling complex real-world problems.
- Engaging in ethical decision-making and data interpretation.
- Publishing findings that could inspire other scholars or future entrepreneurs.
- Working alongside talented peers in the Laidlaw community.
In the long term, I aspire to build a company that is data-driven, socially conscious, and community-focused. This project is a key stepping stone on that path.
Methodology
My research will follow a structured data analysis approach using the OS POI dataset (2015–2025), which includes information on every retail unit in the UK, categorised by type and occupier.
1. Data Compilation & Cleaning
I have already prepared the base dataset across 11 time periods, using UPRN (Unique Property Reference Number) to track individual units.
Data were cleaned for verified addresses and duplicates removed using Excel and Python.
2. Data Integration & Structure
Using Easting/Northing coordinates, I will link retail units to their corresponding retail centre boundaries, allowing comparisons over time.
I will work closely with my supervisor to merge spatial and categorical data into an analysis-ready format.
3. Analysis & Visualisation
I will use Excel, Power BI, and Python (Pandas, Matplotlib) to visualise trends in brand activity and retail diversity.
In some phases, QGIS will be used to map changes spatially across the UK.
I will explore case studies (e.g. Debenhams closures) to evaluate specific brand trajectories.
4. Interpretation & Communication
Each insight will be translated into clear narratives through short blog-style publications.
I plan to publish some of these on Medium, LinkedIn, or The Conversation, aiming for both academic and entrepreneurial impact.