Location Intelligence — Café Site Selection
Problem
74% of new cafés in the UK fail within five years. Site selection costs £50,000+ to get wrong and most decisions rely on gut instinct and borough-level demographics that miss how dramatically conditions vary within short distances across London.
What it does
A three-task data-driven framework across 4,835 London LSOAs at granular sub-borough level. Task one: predict café success potential by area. Task two: predict commercial rent prices. Task three: find the intersection where success potential is high and rent is below market rate — that is where you open.
Key finding
Public transport accessibility dominated the model (16.92% AHP weight), above median house price, demographics, or foot traffic proxies. The more commercially valuable output was identifying emerging neighbourhoods with medium-high success scores not visible in traditional market research — areas that conventional consultancy would miss entirely.
Scale
16,361-word dissertation covering multi-source data integration, AHP weighting methodology, geospatial ML modelling, and rent prediction — then synthesised into an interactive map deployed as part of this portfolio.