The Woodfine location intelligence map assigns each co-location cluster one of three tiers — T1, T2, or T3 — on the basis of retailer-category composition. The tiers are rendered as coloured dots graduated from T1 (deepest co-location) to T3 (shallowest qualifying co-location). Understanding precisely what the tiers measure — and what they do not — is necessary for reading any cluster result correctly.

What the tiers measure

The variable that drives the tier is retailer-category composition: the count and mix of distinct anchor categories co-present within a single spatial cluster. A cluster that contains a hypermarket, a home-improvement big-box, and a warehouse-club anchor has deeper composition than a cluster with a single hypermarket and one grocer. The tier is therefore an ordinal compositional classification.

The tiers measure composition. They do not measure trade-area strength, sales potential, population reached, or spend captured. Two consequences follow directly:

Composition does not imply demand. A T1 cluster has more anchor diversity than a T3 cluster. It does not follow that a T1 cluster has more people, more spend, or better accessibility in its trade area. A dense small-city cluster of three anchors can rank T1 on composition while serving a smaller catchment than a single-anchor T3 site in a major metropolitan area. The tier ranks the bundle of retailers present, not the market the bundle sits in.

Quality claims require an outcome variable. A label asserting quality implies evaluation against an outcome — footfall, revenue, return on a hypothetical investment. The compositional tier carries no such outcome. The on-map labelling therefore reads "co-location depth (anchor count)" rather than "quality tiers," and the plain-language definition of each tier is tied to composition:

• T1 — strongest co-location depth (most anchor categories co-present)
• T2 — intermediate co-location depth
• T3 — shallowest qualifying co-location

How clusters are formed: DBSCAN parameters

The clusters that receive tiers are produced by spatial clustering of anchor retailer locations using DBSCAN (density-based spatial clustering), followed by a de-duplication pass. DBSCAN is governed by three parameters that are published in the Method modal and stated here:

• eps — the neighbourhood radius that defines whether two anchor points are density-reachable; sets the spatial scale at which separate stores read as one cluster.
• minPts — the minimum number of points required to seed a cluster; sets the floor on what counts as a co-location rather than an isolated store.
• IoU threshold — the intersection-over-union cut-off used to de-duplicate overlapping candidate clusters, so the same physical agglomeration is not counted twice.

A hard 3.0 km cap on cluster span (maximum pairwise diameter) applies uniformly. Widening to 4 km is not used because it merges distinct agglomerations. A span at or under 1.0 km carries a tight_intact quality flag.

The values for eps, minPts, and the IoU threshold are read from the current build-geometric-ranking.py pipeline at each build and stated in the Method modal alongside the build version from which they are taken.

Sensitivity: the cluster count is a model output, not a measurement

DBSCAN is a descriptive procedure. It partitions the observed retailer points under a chosen density model; it does not estimate a true, parameter-independent number of clusters that exists in the world. The number of clusters the algorithm returns is a function of eps, minPts, and the IoU threshold, and it moves materially when those are varied within reasonable bounds.

Parameter sweeps conducted during development demonstrate this directly: across the reasonable range tested, the North American cluster count moves from roughly 226 to 476 depending on settings — more than a factor of two — without any change to the underlying retailer data. A headline cluster count is therefore a model output under one chosen parameterisation, not a precise count of an objective phenomenon. Any public presentation of cluster counts must be accompanied by the parameters under which the count was produced.

The planned strength score

The composition tier answers "what retailer mix is here." A separate per-cluster strength score — planned, not yet built — is intended to answer "how much market does this location command." The two dimensions are reported side by side once the scorecard is wired; they are never collapsed into a single colour or a single number.

Design principles

The intended strength score is explainable, not opaque. It is a transparent combination of named drivers, each of which can be shown in the cluster scorecard with its own value and its contribution to the total. No machine-learned weights and no hidden interaction terms. A reviewer must be able to reconstruct the score from the displayed drivers.

Proposed driver set

Three demand-side quantities the data layers already support:

1. Population reached — catchment population and households, from the WorldPop 2026 dasymetric raster aggregated to H3 resolution 7. This is the size of the addressable market.
2. Spend captured — estimated annual retail spend in the catchment, derived from population and per-capita spend proxies (BLS, StatCan, Eurostat). This carries the estimation caveats documented in the spend-population-provenance article and must be displayed with that framing.
3. Accessibility — how reachable the catchment is, expressed through observed origin-destination demand where available (LODES for the US, MITMA for Spain) and a distance-band fallback elsewhere. This inherits the demand_basis flag (observed versus interim) so that observed-mobility and ambient-fallback clusters are not ranked in the same pool without disclosure.

Weights: an open question

How these three drivers combine into a single number is an open question that this article deliberately does not resolve. Three candidate forms are on the table: an equal-weight normalised sum, a lexicographic ranking, and operator-tunable weights. Until the weighting is ratified, the scorecard displays driver values individually so any composite shown is always decomposable. A composite score with undisclosed weights would reintroduce exactly the credibility problem that the tier-label revision exists to remove.

What the scorecard is intended to show

For each clicked cluster the planned detail panel presents, at minimum:

• Composition tier and its plain-language definition.
• Catchment population and households, with vintage and the dasymetric basis noted.
• Estimated annual spend, explicitly framed as a modelled estimate.
• The list of co-located chains driving the composition.
• The strength score (when built) with its top drivers and each driver's contribution, and the demand_basis flag.

What changes from the earlier presentation

Dimension	Earlier	Current
Tier label	"QUALITY TIERS"	"Co-location depth (anchor count)" — composition only
What a tier measures	Ambiguous	Explicitly composition (anchor-category count and mix), ordinal
Decision support	Tier badge and rings only	Planned scorecard: population, spend, co-located chains, explainable strength score with named drivers
Strength score	Conflated into the tier	Planned as separate, demand-side, decomposable dimension
DBSCAN parameters	Not published	eps, minPts, IoU, and 3.0 km span cap published in Method modal and here
Cluster count	Stated as a precise figure	Model output under one parameterisation; NA sweep range disclosed

See also

• trade-area-methodology — catchment derivation, geodesic computation, and the migration from distance bands toward observed trade areas
• spend-population-provenance — the estimation chain for the spend driver in the strength score
• the settlement-level rollup and the Top-400 selection criterion
• the orchestration layer that produces the tiered clusters
• upstream retail clustering feeding the co-location index