Uniform Store Locations
4,387 US uniform store businesses, from national chains to single independent shops, with address, phone, website and map coordinates.
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Or ask for the part you need: an area, a distance, a condition. Counts and previews are free, and each answer shows its exact price.
Sample data
| name | address | city | state | zip | phone | website | overture id | brand | brand id | wikidata | category | category path | basic category | social | operating status | confidence | source dataset | source record id | source ids | source updated | data license | location type | location type confidence | location type basis | latitude | longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cintas Uniform Services | 194 Park 35 Cv S | Buda | TX | 78610 | (512) 362-5727 | https://www.cintas.com/local/usa/tx/austin-tx/work-uniforms-rental | 8ae7070d-ae52-4824-9fb6-acbea1c22e6a | uniform_store | |shopping|fashion_and_apparel_store|clothing_store|uniform_store| | fashion_and_apparel_store | 0.85 | Microsoft | 1125899909143965 | Microsoft:1125899909143965 | 2018-08-31 | Overture Maps Foundation, CDLA-Permissive-2.0 | store | 0.84 | category: "store" (store) | 30.064295 | -97.833443 | ||||||
| Scrubs Etc | Dallas | TX | 75390 | (214) 634-0001 | http://www.scrubsetc.com | 7e6c3398-017f-4516-9031-cf8667dc8d3e | uniform_store | |shopping|fashion_and_apparel_store|clothing_store|uniform_store| | fashion_and_apparel_store | https://www.facebook.com/203291066353868 | open | 0.92 | meta | 203291066353868 | meta:203291066353868|Overture-signals:cbaeae4a-9012-400b-9e67-8d51e01c1bfd | 2026-08-10 | Overture Maps Foundation, CDLA-Permissive-2.0 | store | 0.84 | category: "store" (store) | 32.808439 | -96.841545 | |||||
| Custom Apparel Solutions | 2617 Edenborn Ave | Metairie | LA | 70002-7038 | (504) 885-3488 | http://www.customapparelnola.com | b6020c58-914b-4835-9b5e-63b50d1de77c | uniform_store | |shopping|fashion_and_apparel_store|clothing_store|uniform_store| | fashion_and_apparel_store | open | 0.92 | BrightQuery | 95889467 | BrightQuery:95889467|Overture-signals:0579205f-8e3a-4559-805c-fe98f70b3c5f | 2026-08-13 | Overture Maps Foundation, CDLA-Permissive-2.0 | store | 0.84 | category: "store" (store) | 29.999498 | -90.161861 |
What's in each row
| Field | What it is |
|---|---|
| overture_ | Map data id for the place, stable across monthly releases |
| name | Business name |
| brand | Brand or chain name, when the place belongs to one |
| brand_ | Our id for that brand (its Wikidata id where one exists) |
| wikidata | Wikidata id of the brand |
| category | Kind of place, from the map data |
| category_ | The category with every broader category above it |
| basic_ | A broader, simpler category |
| address | Street address |
| city | City |
| state | State (two-letter code) |
| zip | ZIP code |
| phone | Phone number |
| website | Website |
| Email address | |
| social | Social media page |
| latitude | Latitude |
| longitude | Longitude |
| operating_ | Whether the place is open, where the source says; closed places are left out of the list |
| confidence | How sure the map data is that the place exists (0 to 1) |
| source_ | Which source contributed the record |
| source_ | The record's id in that source |
| source_ | Every source record merged into this row |
| source_ | When the source last updated the record |
| data_ | The license the row is published under |
| location_ | Kind of premises: store, office, branch, warehouse and so on, read from the name, website and category |
| location_ | How sure the location type is (0 to 1) |
| location_ | What the location type was read from |
Source & license
Open map data from the Overture Maps Foundation, release of August 2026, refreshed monthly. Closed and low-confidence places are removed and duplicates within 50 meters merged. Contains data from Overture Maps Foundation (https://overturemaps.org), Places theme, licensed CDLA-Permissive-2.0 (https://cdla.dev/permissive-2-0/); some sources are Apache-2.0. See https://docs.overturemaps.org/attribution/.
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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.
Open one of these to see how many places match, a few preview rows and the price for just those rows:
- The whole list: add a city, state, ZIP code or distance:
https://locationlists.com/find?dataset=uniform-store-locations
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For developers
Dataset slug uniform-store-locations. Machine-readable record: dataset.json. Without a connector, every one of these answers comes from a link: https://locationlists.com/find?dataset=uniform-store-locations. Works in every MCP tool and the x402 query API like any other dataset (count_locations, get_sample, query_locations, relate_locations, count_by_area, cotenancy).