Planet Fitness
U.S. · 2,748 locations
Planet Fitness has 2,748 locations across 49 US states. This dataset contains verified Planet Fitness club locations with street address, phone, and GPS coordinates — deduplicated by 50-meter spheroid distance from multi-source public POI data.
More about this list
Used by competitive retailers measuring market overlap, commercial real estate analysts benchmarking footprints, and distribution planners mapping channel coverage.
The file has all 2,748 locations across 49 states in one download. Building the same list by searching one area at a time tends to miss places, and a missed place looks like a gap in coverage that is not really there.
Need just some of them? Ask in your own words.
●Where the rows are
≡Sample Data Preview
| name | address | city | state | zip | phone | url | geo precision | county | county fips | address quality | county note | zip4 | country | latitude | longitude |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Planet Fitness | 5850 US Highway 431 | Albertville | AL | 35950 | (256) 660-0410 | https://www.planetfitness.com/gyms/albertville-al | source | Marshall County | 01095 | complete | 2048 | US | 34.262627 | -86.187279 | |
| Planet Fitness | Acworth | GA | http://www.planetfitness.com/ | source | Cobb County | 13067 | missing | US | 34.075016 | -84.679267 | |||||
| Planet Fitness | 730 Center St | Auburn | ME | 04210 | (207) 241-8156 | https://www.planetfitness.com/gyms/auburn-me | source | Androscoggin County | 23001 | complete | 6316 | US | 44.125608 | -70.227383 | |
| Planet Fitness | 770 Lighthouse Dr | Barnegat | NJ | 08005 | (609) 549-6474 | https://www.planetfitness.com/gyms/barnegat-nj | source | Ocean County | 34029 | complete | 2302 | US | 39.75854 | -74.255977 | |
| Planet Fitness | 1845 Haines Ave | Rapid City | SD | 57701 | (605) 343-2255 | https://www.planetfitness.com/gyms/rapid-city-sd | source | Pennington County | 46103 | complete | ZIP 57701 spans 2 counties; assigned to the county holding 64% of its land area | 0663 | US | 44.103055 | -103.224285 |
{}What's in each row
| Field | What it is | Rows filled |
|---|---|---|
| name | Store name and number | 100% |
| address | Street address | 100% |
| city | City name | 100% |
| state | Two-letter state code | 100% |
| zip | 5-digit ZIP code | 100% |
| country | Country code (US) | 100% |
| phone | Store phone number | 99% |
| latitude | GPS latitude coordinate | 100% |
| longitude | GPS longitude coordinate | 100% |
| url | Url | 100% |
| geo_ | How exact the map point is: the source's own point, the street address, or the ZIP code's centre | 100% |
| county | County the location is in | 100% |
| county_ | Five-digit federal code of that county | 100% |
| address_ | Whether the street address is complete, missing a house number, or a PO box | 100% |
| county_ | How the county was found, where it was not given by the source | 22% |
| zip4 | Zip4 | 91% |
Rows filled is measured on every row of the delivered file.
✓Where this data comes from
This dataset is compiled from public geographic place data covering Planet Fitness's store network across the US, deduplicated by 50-meter distance to one record per physical store.
September 20, 2026
File built
16 fields
Columns in the file
Fill measured
On every row of the delivered file
How we build and date every file: methodology.
Location data aggregated from public geographic sources including © Overture Maps Foundation contributors.
Using ChatGPT, Claude or an API?
No setup needed. Ask ChatGPT or Claude your question about the Planet Fitness Locations and give it this page: it can open https://locationlists.com/find?dataset=planet-fitness-stores with the filters you need and show you the count and price.
Using ChatGPT or Claude? Tell it to answer from locationlists.com/find — no account, no connector.
How assistants ask
No connector needed. Any AI assistant can answer from this site with a web link, no connector, no account: open https://locationlists.com/find with the question as query parameters.
If a question needs filtering, distance, drive time, combining lists or a price, open a /find link: either put the question itself in q=, or build the structured link. The answer does not need an API, a connector or an account.
Simplest, the question itself: https://locationlists.com/find?q=<the question in plain words>
Or set the parts yourself: https://locationlists.com/find?dataset=<slug>&state=<ST>&city=<city>&county=<county>&zip=<zip>&near=<place or zip>&radius=<miles>&drive=<minutes>&where=<column>:<op>:<value>&total=<rows>
Leave out what the question does not need. Use radius for straight-line miles or drive for minutes of driving (5 to 60), not both; near can name several places separated by | to reach from any of them. To ask about several lists at once, use datasets with two or more names, or category with a kind of business. To compare two lists, add relate=nearest and the other list with b. in front of its parameters. To count by area, use areas=county (or zip, state, metro). To keep only places whose county, ZIP code, metro area or state meets a Census figure, add area_where (for example county:population>1000000); with areas, per=100000 gives counts per 100,000 residents.
Links an assistant can open, each showing how many rows match, a few preview rows and the price for just that part:
- Only California, the state with the most: https://locationlists.com/find?dataset=planet-fitness-stores&state=CA
- Within 25 miles of New York, NY, the busiest area: https://locationlists.com/find?dataset=planet-fitness-stores&near=New+York+NY&radius=25
- Within a 30-minute drive of New York, NY: https://locationlists.com/find?dataset=planet-fitness-stores&near=New+York+NY&drive=30
- Every Consumer Services list combined, within 25 miles of New York, NY: https://locationlists.com/find?category=consumer-services&near=New+York+NY&radius=25
- Each one's nearest Xponential Fitness Studio Locations, in California, with miles: https://locationlists.com/find?dataset=planet-fitness-stores&state=CA&relate=nearest&k=1&b.dataset=xponential-fitness-studio-locations&b.state=CA
- The whole list: add a city, state, ZIP code or distance:
https://locationlists.com/find?dataset=planet-fitness-stores - In plain words: "Planet Fitness Locations in California": https://locationlists.com/find?q=Planet+Fitness+Locations+in+California
- In plain words: "Planet Fitness Locations within 25 miles of New York, NY": https://locationlists.com/find?q=Planet+Fitness+Locations+within+25+miles+of+New+York%2C+NY
Optional: connect for repeat use or wallet payments:
Read https://locationlists.com/skill.md and follow the instructions to connect LocationLists. Then show me the Planet Fitness Locations list.Your assistant will walk you through setup and show you the price before you pay.
For developers
At 2,748 records this dataset is not sold by the row — buy the file. Without a connector, open https://locationlists.com/find?dataset=planet-fitness-stores. An agent connected to https://locationlists.com/mcp can still search it, read a free sample file of up to 10 rows and buy the whole file itself. How agent purchasing works
?Frequently Asked Questions
How many Planet Fitness locations are in this dataset?
2,748 deduplicated club locations across 49 US states.
What fields are in the CSV?
Store name, street address, city, state, ZIP code, phone number, latitude/longitude GPS coordinates, and website URL. Standard CSV ready for any CRM, GIS tool, or spreadsheet.
How is the Planet Fitness data compiled?
Planet Fitness's own locator is protected by bot defenses that block traditional scraping. We aggregate from multiple public POI sources and apply a 50-meter spheroid-distance dedup pass to produce one record per physical location.
How often is the dataset refreshed?
File built on September 20, 2026. It is not on an automatic refresh schedule, so check the build date above before you buy.
We're planning regional distribution — how is this useful?
Mapping Planet Fitness's 2,748-location footprint by state lets you benchmark your existing coverage and identify white space. The GPS coordinates enable catchment and trade-area analysis against census and demographic data.