The location data market has exploded in the last five years, with dozens of providers offering foot-traffic, mobility, and POI datasets. For data buyers — in advertising, real estate, financial services, or government — the challenge is no longer finding location data, but evaluating which datasets meet the quality bar required for high-stakes decisions. The Media Rating Council's research on audience measurement and the IAB Tech Lab's standards for data-quality attestation both make the same point: without a documented quality framework, comparing providers is guesswork. GSDSI's Global Mobility & Location Data and POI & Geofencing products are built around a three-dimensional quality framework we think every buyer should ask their vendors to meet.
Key Takeaways
Evaluate location data across three orthogonal dimensions: signal accuracy, geographic coverage, and temporal consistency. Ignoring any one of them produces misleading reads.
Coverage uniformity matters as much as total device count. A large dataset that's thin in suburban and rural markets is unusable for site selection, market sizing, or competitive benchmarking.
Panel stability over time is the single most common failure mode. A 10% move in the signal must reflect behavior, not SDK distribution drift or methodology change. IAB Tech Lab's data-quality attestation is the industry reference.
Dimension 1: Signal Accuracy
Does a recorded "visit" to a Starbucks actually represent a person entering that Starbucks — or is it a device passing by on the sidewalk, a resident in the apartment above, or GPS drift from an adjacent building? The best datasets layer three controls: stop-detection algorithms (filtering out transit), dwell-time thresholds (a 2-second ping is noise; a 4-minute stay is a visit), and polygon-based POI attribution (the building footprint, not a radius). For the detailed treatment of the polygon question — including the 30–40% false-positive rate typical of radius geofencing in mixed-use retail — see why POI data quality makes or breaks foot-traffic analytics.
Dimension 2: Coverage Uniformity
How consistent is device density across geographies? Many datasets offer excellent coverage in dense urban cores but thin out in suburban and rural markets. Any use case that requires geographic representativeness — site selection, market sizing, competitive benchmarking — demands uniform coverage, not just a high total device count. The buyer-side diagnostic is straightforward: request coverage counts by DMA or Census tract and inspect the tails. If the top-10 DMAs account for the majority of devices and the bottom half are effectively blank, the dataset is not fit for national benchmarking. The FCC's broadband data collection program provides a useful reference for how geographic-grain reporting should work at national scale.
Dimension 3: Temporal Consistency
Does the dataset maintain consistent methodology and panel composition over time? For trend analysis, backtesting, or year-over-year comparisons, you need confidence that a 10% increase in foot traffic reflects an actual change in visitation — not a change in the data panel, SDK distribution, or processing methodology. The best providers publish methodology documentation and flag any methodological changes that could affect time-series comparability. IAB Tech Lab's data transparency standards codify this expectation for advertising-grade data; the same principle applies to CRE, equity research, and policy use cases.
How to Audit a Prospective Vendor
A defensible procurement process asks for evidence across all three dimensions:
Signal accuracy: written methodology for stop-detection, dwell thresholds, and POI attribution logic; sample comparison against a ground-truth visit count.
Coverage uniformity: device counts by DMA + Census tract + rural/urban split; map visualization of coverage tails.
Temporal consistency: panel composition changes by month over the last 24 months; documented methodology changelog; a stability report showing the signal's month-over-month variance during a period of known stable visitation.
GSDSI's data-quality framework addresses all three dimensions through multi-source signal fusion, automated geographic coverage monitoring, and longitudinal panel stability analysis. We encourage prospective clients to request sample data and apply their own quality assessments — confidence in data quality should be earned through evidence, not marketing claims. For the adjacent procurement-diligence context when the data product is specifically a MAID feed, see 5 Questions to Ask Before Licensing a MAID Feed.
Frequently Asked Questions
Why are the three dimensions orthogonal — can't you just use total visit accuracy as one metric?
A dataset can be highly accurate at the venue level and still be useless for a national benchmarking study because of coverage bias. Similarly, a dataset can be accurate and uniformly covered and still produce bogus year-over-year reads if the panel composition drifted. Each dimension fails differently, so each needs to be evaluated separately.
What's the minimum dwell-time threshold to filter false-positive visits?
Practical thresholds typically start around 2–3 minutes for retail and rise to 10+ minutes for dwell-heavy venues (sit-down restaurants, entertainment, hotels). Shorter thresholds let transit and sidewalk pings through; longer thresholds miss legitimate quick-service QSR visits. The correct threshold depends on the venue category, which is another reason NAICS-tagged POI data matters.
How do you evaluate coverage uniformity without access to raw device counts?
Ask for coverage tables broken out by DMA and by rural/suburban/urban split. Reputable vendors publish these without hesitation. If a vendor cannot or will not produce a geographic coverage breakdown, that itself is diagnostic — either they don't measure it or they don't want the buyer to see it.
What causes panel-stability failures, and how do you detect them?
Most common causes: an SDK partner dropped out (cliff in device count), a processing methodology change (step in visit rate), or ATT/Privacy Sandbox rollout (gradual drift in iOS:Android ratio). Detection requires a methodology changelog from the vendor and a month-over-month stability report during a known-stable period — a 12-month run that includes no holidays and no weather anomalies, for example. The IAB Tech Lab transparency standards are the practical reference for what vendor documentation should look like.