Geofencing sounds simple on paper: draw a virtual boundary around a location and capture any device that enters it. In practice, the shape and precision of that boundary has a massive impact on the quality of your data and the accuracy of any analysis built on top of it. Radius-based fences are easy to produce at scale but structurally lossy; custom polygons are labor-intensive to build but produce visit data that holds up to scrutiny. GSDSI's POI & Geofencing product delivers polygon-based boundaries, and the MRC measurement standards codify why the distinction matters for attribution.
Key Takeaways
Radius-based geofences default to noise — circles don't match the shape of real-world buildings.
Polygon geofences reduce false-positive visit counts by roughly 25–45% in dense urban and mixed-use areas.
Radius still makes sense for large outdoor venues, parks, and awareness-level campaigns where reach matters more than precision.
Your vendor's polygon construction and refresh cadence are the core diagnostics — ask how boundaries are built, validated, and kept current.
How Radius and Polygon Geofences Actually Work
Radius fences take a latitude/longitude center point and a distance (typically 50–200 meters) and capture every device event that falls inside the circle. They are trivial to generate programmatically, which is why most self-serve targeting platforms default to them. Polygon fences trace the actual footprint of a building or parcel — corners, setbacks, parking lots, loading docks. They require parcel data, satellite validation, or manual drawing. GSDSI's POI & Geofencing product is polygon-based and refreshed against parcel and satellite sources on a recurring cadence.
The 25–45% False-Positive Gap in Dense Areas
The gap matters because circles don't match reality. A 100-meter radius around a coffee shop in a dense urban block might capture three other businesses, a sidewalk, and a bus stop — every device that pauses at that bus stop gets counted as a "visit." Internal GSDSI testing and public-domain methodology work consistently find that switching from radius to polygon attribution reduces false-positive visit counts by 25–45% depending on surrounding density. In shopping malls, transit hubs, and mixed-use developments the improvement is even more dramatic. For the broader quality framework, see the geospatial data quality framework.
When Radius Geofencing Still Makes Sense
Radius isn't always wrong — it's the right tool for a narrower set of jobs:
Large outdoor venues, parks, and event spaces with irregular boundaries where a generous radius captures the footprint adequately.
Awareness-level campaigns where precision matters less than reach and the downstream measurement is gross impressions, not attributed visits.
Early-stage market research where the question is "is there any device activity in this area" rather than "did this specific device visit this specific location."
Locations with no trusted polygon available — a radius with an explicit confidence caveat beats a polygon you don't trust.
How High-Quality Polygons Are Actually Constructed
A defensible polygon is built from multiple inputs: parcel data (where available), satellite imagery validation, and operator-level corrections. The polygon should cover the indoor footprint of the business, not the full parcel — parking lots and loading docks belong to different attribution rules. Refresh cadence matters because retail locations open, close, expand, and remodel constantly. For the POI-quality angle, see why POI data quality makes or breaks foot-traffic analytics. Census and NAICS-aligned classification — reference Census Business Dynamics Statistics and the NAICS 2022 manual — are what let polygon-tagged visits roll up correctly by industry.
What to Ask a Geofencing Vendor Before You Sign
Use these diagnostics when evaluating a provider:
How are polygon boundaries constructed — parcel data, satellite, manual drawing, or a blend? What's the QA process?
What's the refresh cadence, and how are openings, closings, and remodels reflected?
What's the published false-positive rate, and how was it measured? (Compare against MRC measurement standards and IAB quality guidelines.)
For chain locations, how is the polygon differentiated from adjacent same-chain locations in dense clusters?
How are indoor vs. exterior visits separated? (A device that walks past a storefront isn't the same as a device that enters it.)
Answers to these questions tell you more about the downstream quality of any analytics built on that foundation than any top-line coverage statistic.
Frequently Asked Questions
How much more accurate are polygon geofences than radius geofences?
In dense urban and mixed-use areas, polygon geofencing reduces false-positive visit counts by approximately 25–45% compared to radius-based attribution. In shopping malls, transit hubs, and tightly-clustered retail, the improvement can be larger. In large outdoor or low-density contexts the gap narrows because radius circles don't overlap as many adjacent locations.
When should I still use radius-based geofencing?
Radius works for large outdoor venues, parks, and event spaces with irregular natural boundaries; awareness-level campaigns where reach matters more than attribution precision; early-stage market research; and any case where a trusted polygon simply doesn't exist — a well-scoped radius with a confidence caveat beats a polygon you can't verify.
How are high-quality POI polygons constructed?
Defensible polygons blend parcel data, satellite imagery validation, and operator-level correction. The boundary should trace the indoor footprint of the business, not the full parcel — parking lots belong to different attribution rules. Refresh cadence is critical because retail locations open, close, and remodel continuously. GSDSI's POI & Geofencing product follows this methodology.
What questions should I ask a geofencing vendor before signing?
Ask how polygons are constructed, what QA process is applied, what refresh cadence keeps them current, what published false-positive rate they measure against, how clustered same-chain locations are differentiated, and how indoor vs. exterior passes are separated. The geospatial data quality framework walks through the full diligence checklist.