.svg)
How Glasgow University & UBDC Use Location Data for Smarter Transport Planning
How the University of Glasgow and UBDC validated huq mobility data and applied it to transport and neighbourhood planning.
The Challenge
The University of Glasgow and the Urban Big Data Centre needed mobility evidence for transport and neighbourhood research, but academic use required confidence that the data reflected real behaviour and represented society across different demographic areas.
.svg)
Validate representativeness
Test whether the mobility panel accurately reflected people across multiple demographic areas.
.svg)
Understand travel patterns
Reveal origin-destination flows and movement between neighbourhoods across the Glasgow City region.
.svg)
Support practical planning
Convert research findings into usable evidence for local-government transport and place decisions.
The Solution
huq provided a live behavioural intelligence layer that unified movement trends, catchment visibility, and district benchmarking into a single platform.
.svg)
Movement Intelligence
Live visibility into visitor activity and engagement patterns.
.svg)
Catchment Analysis
Postcode-level insight into visitor origins and regional movement.
Data Validation & Research
Validate mobility evidence and apply it in robust academic, commercial or policy research.
Transport Planning
Understand origin-destination patterns and movement across transport and neighbourhood networks.
The University of Glasgow and UBDC validated huq mobility data and applied it to transport and neighbourhood planning.
UBDC used huq mobility data to study transport and neighbourhood-space patterns. Researchers compared the dataset with CACI Acorn classifications across multiple demographic areas to test how closely it represented the wider population.
Case study highlights
- Independent validation: Academic analysis found the data highly representative across the areas tested.
- Origin-destination analysis: Movement evidence showed how journeys connected places across the city region.
- Neighbourhood insight: Researchers could identify what attracted people to individual local areas.
- Local-government use: Outputs were adopted by public-sector colleagues for practical travel analysis.
The work established a credible evidence base that could support both rigorous research and day-to-day transport planning.
Results & Operational Impact
With huq first clients achieve significantly stronger visibility into commercial demand, engagement growth, and estate performance.
.avif)
Faster leasing evaluations
Improved understanding of real-world commercial demand
Stronger investment confidence
Behavioural intelligence supported more evidence-led decisions
Improved district visibility
Teams benchmarked engagement across multiple locations
Better recovery tracking
Live movement signals replaced delayed reporting cycles
Related Case Studies
Challenger Coffee Brand Site Selection Case Study
A UK challenger coffee brand used huq's footfall data to identify high-potential sites, avoid oversaturated markets and outperform larger rivals.
How JD Sports Used Customer Catchments to Inform Outlet Expansion Strategies
huq's mobility location data enabled JD Sports to refine customer catchment analysis, optimise store network performance and make data-driven expansion decisions, reducing risk and enhancing strategic growth.
.svg)
Empowering strategic teams with the world's most accurate and accessible location intelligence.
Contact
.svg)