Business clustering

Retail data is inherently messy — a single commercial site often contains multiple distinct points, such as a big-box anchor, a nested pharmacy, and a fuel outlet sharing the same parking area. The platform's business-clustering step turns those raw points into commercial clusters using a parent-child pattern, so downstream GIS analysis reasons about one unified commercial entity per physical site rather than several overlapping records.

The parent-child pattern

Points that plausibly belong to the same physical site are merged in a small number of proximity-based passes, using different distance thresholds depending on whether the points share an identifying signal (the same retail chain, for example) or only a brand-level match. The highest-weight named point at a merged site becomes the parent record; the rest become children. Without this step, several co-located tenants at one site would each count as an independent signal in downstream scoring, overstating that location's commercial weight.

Where this fits in the pipeline

Clustering runs as part of the same Python-based GIS pipeline documented in GIS orchestration application — the code that turns raw geographic and business data into the regional co-location index — rather than as a separately deployed service. This article does not restate the pipeline's specific distance thresholds or internal script names; the general pattern (merge co-located points, promote the strongest anchor to parent) is the stable, public-facing part of the design.

See also

• GIS orchestration application — the pipeline this clustering step is part of
• GIS data lake — the raw data this step consumes