Sheetz
U.S. Location Dataset // 690
Sheetz is a privately held, family-controlled convenience-and-fuel chain with 690 locations concentrated across a six-state Mid-Atlantic corridor—Pennsylvania, Virginia, West Virginia, North Carolina, Maryland, and Ohio—making it one of the densest regional c-store networks east of the Mississippi. Unlike its arch-rival Wawa, which has expanded into Florida and pursues a coastal-urban density model, Sheetz grows along interstate and secondary-highway corridors, giving it an outsized presence in smaller metro and exurban trade areas where it often functions as both gas station and de facto quick-service restaurant via its pioneering MTO (Made-To-Order) touchscreen food program. This dataset of 690 locations with geocoordinates is essential for CPG direct-store-delivery route planners optimizing Mid-Atlantic coverage, QSR strategists evaluating c-store foodservice cannibalization in markets like the I-81 corridor, and EV charging network developers targeting high-traffic fueling sites for NEVI-funded buildouts.
●Coverage Map
Sheetz dealer locations in the USA
690 locations · Source: LocationLists.com
☆Who uses this data
Sales & Business Development
Territory planning, lead generation, and prospecting for teams selling products or services to stores
Market Research & Competitive Intelligence
Analyzing Sheetz's geographic footprint, density, and overlap with competitors
Logistics & Supply Chain
Route optimization, delivery planning, and distribution network analysis across 690 locations
Real Estate & Site Selection
Co-tenancy analysis, trade area studies, and identifying expansion opportunities near existing stores
✓Data quality & methodology
This dataset is compiled from multi-source public geographic POI data covering Sheetz's store network across the US. Each record is cross-referenced across upstream sources and deduplicated by 50-meter spheroid distance to produce one verified record per physical store.
Monthly
Refresh frequency
Deduplicated
By 50m proximity
Validated
Format checks on all fields
Every record goes through automated quality checks including ZIP code format validation, coordinate bounds verification (US only), phone number standardization, and duplicate detection. Records that fail validation are flagged and excluded from the dataset.
Location data aggregated from public geographic sources including © Overture Maps Foundation contributors.
{}Data Fields
≡Sample Data Preview
| name | address | city | state | zip | country | phone | latitude |
|---|---|---|---|---|---|---|---|
| Sheetz | 286 NC-801 N | Advance | NC | 27006 | US | +13369419240 | 36.010446 |
| Sheetz | 4 E Mall Rd | Barboursville | WV | 25504 | US | +13047696500 | 38.416607 |
| Sheetz | 20 W Waterloo Rd | Akron | OH | 44319-1117 | US | +12343341146 | 41.028336 |
| Sheetz | 19910 Riverside Commons Plz | Ashburn | VA | 20147-5083 | US | +15718313113 | 39.064734 |
| Sheetz | 29225 Smith Rd | Romulus | MI | 48174 | US | 42.2456 |
Sheetz Locations
690 locations · CSV · Updated monthly
?Frequently Asked Questions
How does Sheetz's geographic footprint compare to Wawa's, and can this dataset help map their overlap?
Sheetz's 690 stores are concentrated in western and central Pennsylvania, Virginia, West Virginia, North Carolina, Maryland, and Ohio, while Wawa's ~1,000 stores dominate eastern Pennsylvania, New Jersey, Florida, and the D.C. suburbs. Their overlap zone is narrow—primarily the greater Philadelphia exurbs and parts of central Virginia. If you need a head-to-head overlap analysis (e.g., locations within a 3-mile radius of each other), LocationLists can run a custom spatial join across both networks.
I'm a CPG field sales rep covering the Mid-Atlantic. How do I use this data to build efficient DSD routes for Sheetz stores?
The dataset includes lat/long for all 690 locations, which you can import directly into route-optimization tools like Badger Maps or OptimoRoute. Because Sheetz clusters heavily along interstate corridors—I-81, I-79, I-77, and I-64—you'll find natural route groupings that differ from urban grid patterns typical of CVS or Walgreens calls. Sorting by state and zip will immediately reveal the Pennsylvania concentration (roughly 40% of all stores) so you can prioritize territory assignments accordingly.
We're evaluating sites for a competing c-store or QSR concept in Sheetz's core markets. What can this dataset tell us about white-space opportunities?
Plotting all 690 Sheetz locations on a map reveals clear density clusters in central PA, the Shenandoah Valley, and the Charlotte metro fringe, but also visible gaps—particularly in eastern Ohio, southern West Virginia, and parts of the NC Piedmont. LocationLists offers a white-space mapping service where we overlay Sheetz, Wawa, and other c-store or QSR footprints against population and traffic-count data to pinpoint underserved trade areas for your concept.
Does the Sheetz dataset help distinguish high-volume interstate locations from smaller suburban stores?
The dataset itself doesn't include store-format flags or fuel volume, but the geocoordinates let you cross-reference each location against interstate interchange proximity using GIS tools. Sheetz is known for building oversized travel-center-style stores at highway interchanges (often 5,000+ sq ft with 16+ fuel positions) versus smaller in-town formats. If you need that segmentation pre-built, we can deliver a custom enrichment layer that tags each location by proximity to interstate exits and surrounding population density.
How does Sheetz's footprint compare to the CVS and Walgreens datasets already in your catalog for convenience-channel competitive analysis?
Sheetz's 690 locations are roughly one-thirteenth the size of Walgreens' ~8,700 U.S. stores but serve a fundamentally different mission—fuel, made-to-order food, and tobacco dominate the Sheetz basket versus pharmacy and health/beauty at CVS and Walgreens. The comparison becomes useful when a beverage or snack brand wants to understand total convenience-channel shelf access in a specific DMA: combining our Sheetz, CVS, and Walgreens datasets for, say, the Pittsburgh or Roanoke markets shows the full competitive picture for impulse-purchase placement.
I'm an EV charging network developer. Why is the Sheetz location dataset relevant for site selection?
Sheetz has publicly committed to deploying EV chargers across its network and has already installed Tesla Superchargers and other DCFC units at select locations. The 690 geocoded sites—many positioned at high-traffic interstate interchanges—represent a pre-qualified pipeline of potential host sites for NEVI-formula-funded corridors. Cross-referencing this dataset against AFDC station data and state NEVI plans can surface which Sheetz locations lack chargers but sit on eligible corridors, giving your BD team a prioritized outreach list.