Key Takeaways
- Connected vehicle GPS data typically ends in the parking lot, not inside the POI, which makes raw vehicle telemetry unreliable for visit attribution on its own.
- Parking lot polygons provide the spatial bridge between where a vehicle stopped and the POI it actually served.
- The join runs through a defined methodology: clean the telemetry, cluster parking events, spatially match to a polygon, then resolve to a POI using the dataset's placekey and related_poi fields.
- Polygon-plus-parking-lot attribution meaningfully outperforms centroid radius methods, especially in multi-tenant environments like strip malls and shopping centers.
- Shared lots, parking garages, and overflow parking remain edge cases that need explicit handling, not full automation.
- The connected vehicle fleet is expanding fast (203 million US connected car drivers projected by 2029), making this attribution gap more consequential over time, not less.
Connected vehicle data is one of the fastest-growing sources of location intelligence, but it has a structural blind spot: the GPS trail stops in the parking lot, not inside the store. That gap makes it hard to answer a simple question, which POI did this vehicle actually visit, and it is the reason vehicle telemetry alone produces unreliable visit attribution.
This article walks through why that gap exists, the actual join methodology data scientists use to close it with parking lot polygons, how that approach compares to centroid and geofence attribution, and where it still breaks down (shared lots, garages, multi-tenant retail).
Vehicle Telematics Visit Attribution: Why GPS Alone Falls Short
Mobile device location data typically resolves inside a POI’s geofence, because people carry their phones with them into the store. Connected vehicle data behaves differently. A car’s GPS trail ends where the car parks, which is often 50 to 300 feet outside the actual building footprint. Without a way to connect that parking event to the POI it served, vehicle-based visit attribution defaults to guesswork: drawing a radius around the store and hoping the parked vehicle falls inside it.
The scale of this problem is growing. Connected cars in the US gained 9.1 million drivers between 2024 and 2025, and the market is on track to reach 203 million connected car drivers by 2029, according to eMarketer’s December 2025 automotive connectivity forecast. As that fleet expands and more businesses turn to connected car data analytics for fleet visitation data and traffic pattern analysis, the parking-lot-to-POI gap becomes a bigger constraint on data quality, not a smaller one.
From Parking Lot to POI: How the Attribution Bridge Works
The fix is a spatial join, not a bigger radius. Instead of estimating where a vehicle probably went based on distance from a store’s center point, you match the vehicle’s parking coordinates against a polygon that traces the actual shape of the parking lot, then use that polygon’s documented relationship to the POI (or POIs) it serves.
This is what SafeGraph Parking Lots provides: polygon geometry for the lots serving roughly 6 million US Places, built from satellite imagery and refined to correct for AI-generated errors like fragmented boundaries and jagged edges. You can read the full build methodology, including how edge cases like broken polygons and oversized lots were filtered out, in SafeGraph’s parking lot dataset breakdown.
A Worked Example: Joining Vehicle Telemetry to Parking Polygons
Here is how that join runs in practice, using SafeGraph’s actual Parking Lots schema.
Step 1: Clean the telemetry
Vehicle GPS pings, like mobile pings, carry signal drift and horizontal accuracy noise. Filter out non-stationary readings and anything above a calibrated accuracy threshold before doing any spatial work.
Step 2: Identify parking events
Cluster consecutive pings where the vehicle was stationary for a meaningful dwell period (typically several minutes or more) to separate a parking event from a vehicle simply stopped at a light or passing through.
Step 3: Spatially join the parking event to a parking lot polygon
Match the parking event’s coordinates against polygon_wkt in SafeGraph’s Parking Lots dataset, with a small buffer to absorb residual GPS uncertainty.
Step 4: Resolve the polygon to a POI using the precomputed Placekey relationship
Each parking lot row carries a Placekey and a related_poi field, the Placekey(s) of the POI or POIs SafeGraph already believes that lot serves. If your Places data is licensed alongside Parking Lots, this same relationship is appended in reverse as related_parking directly on the POI record, so the join can run in either direction without building your own matching logic.
Step 5: Disambiguate when related_poi returns more than one Placekey
A single lot often serves multiple tenants, so related_poi can be a list. When it is, use category data, dwell time, and time-of-day patterns to predict which tenant was most likely visited. A vehicle parked for 45 minutes at 7pm next to a restaurant and a nail salon is more likely a diner than a walk-in customer arriving at closing time.
The output is a POI-level visit record instead of a parking-lot-level approximation, which is the difference between usable attribution and a directional guess.
Comparing Attribution Methods: Centroid Radius vs. Geofence vs. Polygon + Parking Lot
Method | How It Works | Precision | Best For | Key Limitation |
Centroid radius | Fixed-distance circle drawn around a store’s center point | Low | Standalone buildings with generous setback | Bleeds into neighboring businesses and misses parked vehicles outside the radius |
Building footprint geofence | Polygon matching the exact building outline | High for indoor visits | Mobile device data that enters the building | Still doesn’t resolve where a vehicle parked outside the building |
Polygon + parking lot | Building footprint plus a matched parking lot polygon and related_poi/related_parking linkage | Highest for vehicle-based attribution | Connected vehicle and fleet telemetry, multi-tenant retail | Requires a maintained parking lot dataset and a resolution step for shared lots |
Illustrative Accuracy Uplift
To put the gap in concrete terms: on an illustrative 200-vehicle sample parked at a 12-tenant strip mall, centroid-radius attribution might correctly assign somewhere around 55 to 65 percent of visits to the right tenant, since the radius overlaps several storefronts at once. Polygon-plus-parking-lot attribution, which resolves the visit through the related_poi relationship rather than distance, can push correct assignment well above 90 percent in the same scenario. These figures are illustrative rather than a published benchmark, but they reflect the same pattern SafeGraph has documented in building-footprint-versus-centroid comparisons in its visit attribution guide: precision gains compound fastest in exactly the dense, multi-tenant environments where radius-based methods struggle most.
Edge Cases: Shared Lots, Garages, and Multi-Tenant Ambiguity
Parking-lot-based attribution is a major improvement over radius guessing, but it isn’t automatic. A few recurring edge cases need explicit handling:
Shared lots : Strip malls, shopping centers, and mixed-use developments often have one parking lot serving five, ten, or more tenants, reflected as multiple entries in related_poi. Resolving these requires the disambiguation step described above, not just the spatial join.
Garages vs. surface lots : SafeGraph Parking Lots covers surface lots. Multi-level parking structures don’t behave the same way: a vehicle’s horizontal GPS position inside a garage can’t indicate which floor it is parked on, and garages frequently serve multiple buildings at once, so garage-served visits typically carry lower confidence and should be modeled accordingly.
Multi-tenant ambiguity at scale : In dense urban retail corridors, a single parking area can sit between competing brands. Category data and dwell time help, but some volume of visits in these environments will always carry a probability score rather than a certainty.
Overflow and street parking : Not every vehicle uses the primary lot. Overflow parking, adjacent lots, and on-street parking near a POI can undercount visits if the attribution model only looks at the one polygon closest to the building.
Closing Thoughts
None of this replaces good judgment. The methodology closes the biggest structural gap in connected vehicle visit attribution, which is the fact that GPS data stops where the pavement does, but it still depends on maintained, accurate parking lot geometry and a resolution layer for shared spaces. Treat polygon-plus-parking-lot attribution as the foundation that makes vehicle telemetry usable for POI-level analysis, not as a fully automated answer that removes the need for validation against known outcomes like foot traffic or point-of-sale data.
Frequently Asked Questions
1. Why is connected vehicle data alone insufficient for POI visit attribution?
Connected vehicle GPS data typically ends when a vehicle parks, often outside a POI’s geofence, making it unclear which place was actually visited without additional context.
2. How do parking lot polygons improve attribution accuracy?
Parking lot polygons capture the exact area where vehicles stop and link that area to the POI or POIs it serves, replacing distance-based guessing with a direct spatial match.
3. What is the difference between centroid radius and polygon-based attribution?
A centroid radius draws a fixed circle around a store’s center point, which often includes neighboring businesses or excludes real visits. A polygon match uses the actual footprint and its documented relationships, which is significantly more precise, especially in multi-tenant locations.
4. How does a data scientist actually join vehicle telemetry to parking lot data?
By cleaning the GPS data, clustering pings into parking events, spatially joining those events to parking lot polygons, then resolving to a POI using the dataset’s precomputed placekey and related_poi fields, with category and dwell-time signals for shared lots.
5. Which POI types benefit most from parking-lot-based attribution?
Multi-tenant locations such as strip malls, shopping centers, and large retail complexes benefit the most, since these are exactly the environments where centroid radii perform worst.
6. How is this different from mobile device visit attribution?
Mobile device data usually enters a POI’s geofence directly, since people carry phones indoors. Vehicle GPS data generally does not, which is why it needs the added parking-lot join step.
7. Can parking lot polygons be used with other mobility datasets, not just connected vehicles?
Yes. Parking lot geometry can complement any location dataset with a final-destination ambiguity problem, including fleet telemetry and rideshare data.
