Places filtering

GIS tier rankings depend on knowing where regional institutions sit, not where every local clinic or community facility sits. The platform's places-filtering step keeps only civic and institutional facilities that meet a regional scale — hospitals, universities, and validated major transport hubs above fixed size thresholds — and consolidates multi-point campus records into a single regional anchor. Local-service density is removed at this stage, so downstream rankings reflect institutional concentration rather than raw facility count.

What the filter keeps

The filter applies fixed, structural thresholds rather than configurable parameters: a hospital must reach a minimum staffed-bed count, a university a minimum full-time-equivalent enrollment, and an airport must be a validated major regional hub rather than a general aviation facility. Institutions below these thresholds are dropped before any downstream scoring runs.

Consolidating campus records

A large institutional campus often appears in raw open geospatial data as many separate points. The filter merges points that plausibly belong to the same physical campus into a single regional anchor with one unified centroid, preventing a single large institution from being counted many times over.

Where this fits in the pipeline

Filtering 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. Its output feeds GIS orchestration application alongside the retail clustering step from Business clustering when the pipeline assigns final co-location tiers. This article does not restate the pipeline's specific thresholds, buffer distances, or internal file names; the general pattern (drop sub-regional facilities, consolidate multi-point campuses to a single anchor) is the stable, public-facing part of the design.

See also

• GIS orchestration application — the pipeline this filtering step is part of
• Business clustering — the retail clustering step that runs alongside it
• GIS data lake — the raw civic and retail data this step consumes
• Retail co-location tier methodology — the tier methodology the filtered data feeds

References

• Point of interest — Wikipedia, accessed 2026-06-14