Trade area data sources — population and spend
TopicFrom the Woodfine Projects
Population estimates from WorldPop 2026 and annual per-capita spend proxies from national household surveys underpin the trade area statistics for each co-location cluster.
Population estimates and retail spend estimates are the two input layers that drive trade area statistics for each co-location cluster. Both are derived from publicly available data sources and applied at the H3 resolution-7 hexagonal grid level, per the O-D catchment methodology. Together they supply the population and spend axes used by the deterministic ranking system and the V3 catchment ranking methodology.
[edit]Population Data
Population estimates are sourced from the WorldPop 2026 100-metre population grid (worldpop.org). WorldPop produces modelled population estimates derived from census microdata, satellite imagery, and dasymetric redistribution. The 100 m resolution places population at the sub-block level, enabling precise trade area delineation.
[edit]Processing pipeline
-
Spatial filter: Only grid cells within 150 km of at least one co-location cluster centroid are retained, reducing data volume by approximately 80% while preserving all cells relevant to catchment computation.
-
H3 aggregation: Retained cells are assigned to their containing H3 resolution-7 hexagon and population values are summed. H3 resolution-7 cells have an average area of 5.16 km².
-
Output:
census-h3-res7.jsonl— one record per H3 cell with fields{h3, lat, lon, pop, iso}.
[edit]Countries covered
United States, Canada, Mexico, Great Britain, Germany, France, Netherlands, Austria, Portugal, Greece, Denmark, Iceland, and Poland — 13 countries as of the current pipeline version.
[edit]Spend Data
Spend estimates are synthesised by applying annual per-capita expenditure multipliers by retail category to the population grid. The multipliers are proxies derived from national household expenditure surveys.
| Country | Grocery (p.a.) | Hardware (p.a.) | Wholesale (p.a.) | Currency |
|---|---|---|---|---|
| United States | $3,500 | $1,200 | $1,500 | USD |
| Canada | C$3,200 | C$1,100 | C$1,300 | CAD |
| Mexico | MX$18,000 | MX$3,500 | MX$2,500 | MXN |
| Great Britain | £2,800 | £850 | £900 | GBP |
| Germany | €2,900 | €950 | €1,000 | EUR |
| France | €3,100 | €900 | €1,000 | EUR |
| Netherlands | €2,700 | €1,000 | €1,100 | EUR |
| Austria | €3,000 | €950 | €1,000 | EUR |
| Portugal | €2,400 | €600 | €700 | EUR |
| Greece | €2,200 | €500 | €600 | EUR |
| Denmark | €3,500 | €1,200 | €1,100 | EUR |
| Iceland | €4,000 | €1,500 | €1,500 | EUR |
| Poland | PLN 8,000 | PLN 2,000 | PLN 2,500 | PLN |
Multipliers are expressed in local currency. Cross-country spend comparisons require foreign-exchange normalisation, which is not applied in the current pipeline. Rankings are most meaningful within a single country or within the eurozone.
[edit]Retail categories
- Grocery: Supermarkets, hypermarkets, food cooperatives, and food sections of general merchandise retailers.
- Hardware: Home improvement, building materials, and garden centres.
- Wholesale: Members-only warehouse clubs and cash-and-carry retailers.
[edit]Processing pipeline
Spend values are computed at the H3 resolution-7 level by multiplying each cell's aggregated population by the per-capita multipliers for its country. Output: cleansed-spend-h3-res7.jsonl — one record per H3 cell with fields {h3, pop, spend_grocery, spend_hardware, spend_wholesale, currency}.
[edit]Catchment Aggregation
For each co-location cluster, primary and secondary catchment zones are defined by crow-flies distance rings (see: O-D Catchment Methodology). Population and spend for all H3 cells within each zone are summed to produce the cluster's trade area statistics. These aggregated values are the basis for cross-cluster competitive ranking.
[edit]Point-of-interest data
The retail anchor and secondary operator locations that form co-location cluster centroids are sourced from OpenStreetMap contributors under the Open Database Licence (ODbL). Point-of-interest data is distinct from the population and spend layers described above; it provides the geographic seed points from which catchment zones are measured. The full data-attribution statement covering all pipeline layers appears in topic-regional-markets-system. [osm-odbl]
[edit]References
- Trade area — Wikipedia, accessed 2026-06-14
- WorldPop Global High Resolution Population Denominators Project — WorldPop, University of Southampton, accessed 2026-06-14
- H3: Uber's Hexagonal Hierarchical Spatial Index — H3 Geo, accessed 2026-06-14
Wikipedia content reproduced under CC BY-SA 4.0.
OpenStreetMap data © OpenStreetMap contributors, licensed under ODbL.
[edit]See also
OpenStreetMap data © OpenStreetMap contributors, licensed under ODbL.