footflow is a geospatial analytics platform that provides evidence-based footfall and pedestrian proximity metrics for urban planning and property decisions in the UK. It offers granular, modelled estimates of pedestrian activity and accessibility, enabling users to benchmark sites, optimize layouts, and forecast the impact of development scenarios. The platform is currently in its final testing phase with ten local authorities, including the City of London and York.
Funding
Funding not disclosed
Founders
Product
Problem
Urban planners, property developers, and local authorities often rely on outdated, coarse-grained, or inconsistent footfall data, making it difficult to accurately assess site viability, optimize land use, or predict the pedestrian impact of new developments. Existing tools lack the granularity and predictive capability needed for evidence-based decision-making, leading to suboptimal planning outcomes.
Solution
footflow provides a geospatial analytics platform that generates modelled estimates of footfall and pedestrian proximity metrics at an unrivalled level of granularity. The tool enables users to benchmark individual sites, optimize layout and land use mix, and run predictive forecasts of how building massing and land use changes will affect pedestrian movement. By using advanced spatial and statistical models, footflow delivers consistent, reliable metrics that help organizations make data-driven planning decisions. The platform is designed to be accessible to a wide range of users, from local authorities to private developers, and is currently in its final test phase with ten UK local authorities.
Target Audience
Primary customers are UK local authorities, urban planners, property developers, and transport consultants seeking reliable footfall data and predictive analytics for planning and development projects.
Features
- Generates modelled estimates of footfall at different times of day and week for comprehensive site benchmarking
- Produces proximity metrics quantifying pedestrian access to a range of land uses at a highly granular scale
- Enables predictive forecasting of how building massing and land use changes impact pedestrian footfall
- Uses advanced spatial and statistical models, with plans to integrate spatial AI techniques for international scalability
- Provides a common definition of pedestrian infrastructure to overcome biases from different professional perspectives