Distance-band methodology — primary and secondary demand zones
The Woodfine co-location platform assigns each cluster two demand zones that determine which population and spend data is attributed to it: a primary zone within 35 km of the cluster, and a secondary zone from 35 km to 150 km. Both are straight-line distance bands, and they are labelled as such: a distance band approximates where customers come from; it is not a measured catchment, and the platform does not label it one. The labelling rule, and the planned move to observed origins and drive-time boundaries, are set out in Trade-area methodology. The zone inputs to the deterministic ranking system and the V3 catchment ranking methodology flow from this model; population and spend layers are documented in trade-area data sources.
Zone definitions
Primary zone (0–35 km): the area within 35 km straight-line distance of the cluster centroid. This zone represents the immediate trade area where the majority of regular shopping trips originate.
Secondary zone (35–150 km): the area between 35 km and 150 km straight-line distance of the cluster centroid. This zone captures the wider regional draw, including occasional shoppers and cross-regional trips.
The 35 km primary boundary is a provisional parameter based on established retail geography conventions. It is subject to refinement once empirical origin-destination data becomes available. The 150 km outer boundary aligns with the platform's data collection radius, ensuring that every location contributing to a cluster's zones has been ingested and verified.
Zone membership is resolved on the platform's worldwide spatial grid, so zone figures compare directly between clusters in any country. The full grid specification and membership rule are planned for publication at gis.woodfinegroup.com.
Distance method
All distances are straight-line ground distances; no drive-time routing is used at this stage. A 35 km band therefore means the same thing in every market — urban or rural, North American or European — which is what makes cross-cluster comparison possible before observed origin data arrives. Reachability along the road network is a planned improvement, described in Trade-area methodology. The distance computation itself is planned for publication at gis.woodfinegroup.com.
HOME and AWAY perspectives
The platform distinguishes two perspectives on zone population.
HOME: Population counts derived from residential data (WorldPop 2026). Represents where people live within each zone. This is the default view and is fully implemented.
AWAY: Population counts representing daytime or workplace population. Workplace distribution differs from residential distribution — concentrated in commercial districts and employment centres rather than dispersed across residential areas. The AWAY perspective is planned; the data source is pending.
One place, multiple clusters
A single location may fall within the zones of multiple co-location clusters. This is intentional: trade areas are not exclusive territories. A household within 35 km of two competing clusters contributes to both clusters' primary-zone populations. This reflects the competitive retail landscape and is foundational to the cross-cluster comparison methodology; cluster boundary handling at the same parking lot is documented in cluster deduplication threshold.
Application
Zone membership is the basis for:
- Population aggregation (census-derived population by zone)
- Spend aggregation (grocery, hardware, wholesale spend by zone)
- Cross-cluster competitive ranking (see catchment-ranking-methodology-v3)
The zone polygons displayed on the map are generated from the same 35 km / 150 km straight-line radii, visualised in two distinct colours to distinguish primary from secondary zones.
See also
- catchment-ranking-methodology-v3
- Trade area data sources — population and spend
- Retail co-location methodology
References
- Catchment area — Wikipedia, accessed 2026-06-14
- Trade area — Wikipedia, accessed 2026-06-14
- WorldPop Global High Resolution Population Denominators Project — WorldPop, University of Southampton, accessed 2026-06-14
Wikipedia content reproduced under CC BY-SA 4.0.
Cluster centroids from which zone distances are measured are derived from OpenStreetMap POI records. OpenStreetMap data © OpenStreetMap contributors, licensed under ODbL.