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Category to Tag Cheat-Sheet

Everyday concepts, and the key/value that indexes them. Starting points, not ground truth — a value here may not exist in a given area. Confirm with tag_values(area, key) before filtering on it, and tag_keys(area) when you are not sure the key is there at all. Both return the real, frequency-ranked vocabulary for that area, which beats guessing a spelling.

Two vocabularies live here. The first table is OpenStreetMap's, which is what "tags" usually means. The second — climate, flood, wildfire, radon, air quality, noise, fire history — is not in OpenStreetMap at all; this build stamps it on from government datasets. If you know OSM well you will assume those questions cannot be answered from map tags. In this database they can.

Which key holds which concept

Pick the key first, then the value.

Key Covers Frequent values
amenity Public / commercial point services restaurant, cafe, fast_food, bar, pub, fuel, pharmacy, hospital, clinic, school, bank, atm, parking, toilets, place_of_worship, library, police, fire_station, post_office, cinema, theatre
shop Retail supermarket, convenience, wholesale, bakery, butcher, books, clothes, hardware, department_store, mall, alcohol, car, car_repair, hairdresser, optician
leisure Recreation and sport grounds park, playground, pitch, sports_centre, fitness_centre, stadium, golf_course, swimming_pool, dog_park, garden, nature_reserve
tourism Travel, lodging, sights hotel, motel, guest_house, hostel, museum, attraction, viewpoint, information, artwork, camp_site
highway Roads and paths, plus some point features motorway, trunk, primary, secondary, residential, service, footway, path, cycleway, trailhead, bus_stop, crossing, traffic_signals
natural Physical geography water, wood, tree, peak, beach, cliff, wetland, spring, hot_spring
building Building footprints (the structure itself) yes, house, apartments, commercial, retail, industrial, school, hospital, church
office White-collar / org premises company, government, insurance, lawyer, estate_agent, it, coworking
healthcare Health services (complements amenity) doctor, dentist, pharmacy, hospital, clinic, physiotherapist
place Populated places and admin units (nodes) city, town, village, hamlet, suburb, neighbourhood, locality
boundary Administrative and other boundaries administrative, national_park, protected_area
landuse How land is used (areas) residential, commercial, industrial, retail, farmland, forest, grass, winter_sports
railway Rail infrastructure station, halt, tram_stop, subway_entrance, rail
man_made Built structures that are not buildings tower, water_tower, pier, lighthouse, bridge, surveillance

Keys that are not OpenStreetMap

Stamped at build time from external sources onto whatever entry sits at that point — a house, a town, a trailhead — so they are read off an ordinary search result rather than fetched separately. Coverage differs per source, and the rules below say where each one stops. Where a value is there, prefer it over your own recollection of a region's climate: it is measured, cited, and the same for every caller.

Key Answers Units / values
climate:high_jul how hot summers are whole °F, average July high
climate:low_jan how cold winters are whole °F, average January low
climate:temp_annual one-number summary whole °F
climate:precip_annual how wet whole inches per year
climate:humidity_jul how muggy whole °F, July mean dew point — what separates Houston from Phoenix at the same 100 °F
climate:sunshine how gray percent clear-sky transmittance; higher is sunnier
climate:hardiness_zone what will grow USDA half-zone, 1a…13b — text, so = or an OR list, not >
fema:flood_zone FEMA flood zone X, AE, A, VE, …
fema:sfha in the regulatory floodplain (insurance) yes / no
fema:zone_subtype what a zone-X actually means minimal, 0.2pct, floodway, levee, …
usfs:wildfire_hazard wildfire potential at 270 m very_low … very_high
nri:<hazard> 18 FEMA hazards per census tract high / very_high only — nri:wildfire, nri:hail, nri:tornado, nri:drought, nri:earthquake, nri:heat_wave, nri:landslide, nri:riverine_flood, …
fire:last_burn_year when a mapped wildfire last covered this point year, e.g. 2020
fire:burn_count how many mapped wildfires have count
usgs:radon_potential geologic radon potential low / moderate / high
epa:aqi_unhealthy_days bad-air days per year (county) days
epa:aqi_sensitive_days unhealthy-for-sensitive-groups days days
epa:aqi_median typical day median AQI
epa:aqi_years what the three-year mean averaged e.g. 2022-2024
bts:noise modelled road/rail/aviation noise at 30 m dBA

Sources and vintages: climate is PRISM 1991–2020 normals on an 800 m grid (CONUS only — no Alaska, Hawaii, or Canada), except climate:hardiness_zone, which is the 2023 USDA map and also covers AK, HI and PR. Flood is FEMA NFHL, hazards are FEMA National Risk Index, wildfire potential is USFS WHP, fire history is NIFC perimeters (wildfire only, prescribed burns excluded), radon is USGS, air quality is a three-year mean of EPA AirData, noise is the BTS national noise map. knowhere://attribution records the exact vintage this artifact was built from.

Reading them without being wrong

  • An absent tag is not a low value. nri:* stamps only "Relatively High" and "Very High", so no nri:wildfire means "not elevated or no rating". epa:aqi_* exists only for the ~1,000 of 3,143 counties with a monitor. fire:* is absent where nobody mapped a perimeter, and federal land is mapped far better than private. Non-US areas have none of these at all.
  • These are area values, not the property. 800 m for climate, a census tract for nri:*, a county for epa:*, a geologic province for radon. They screen a location; they do not describe a lot. bts:noise and usfs:wildfire_hazard are models, good relatively and poor absolutely.
  • Numbers compare, text does not. > >= < <= read the leading number, so [climate:precip_annual<15] works and [climate:hardiness_zone>6a] does not mean what it looks like.
  • Combine them. Dry, mild-wintered, sunny: nw[place=town][climate:precip_annual<16][climate:low_jan>25][climate:sunshine>65](area=arizona).

Quick lookups

You want… Query with
Restaurants [amenity=restaurant]
Coffee shops [amenity=cafe]
Gas / petrol stations [amenity=fuel] (not shop=fuel)
Grocery stores / supermarkets [shop=supermarket]
Warehouse clubs (Costco, Sam's) [shop=wholesale]
Bookstores [shop=books]
Colleges / universities [amenity=university] (not amenity=college for higher ed)
Schools (K-12) [amenity=school]
Hospitals [amenity=hospital]
Pharmacies [amenity=pharmacy]
Parks [leisure=park]
Playgrounds [leisure=playground] (not amenity)
Dog parks [leisure=dog_park] (not amenity)
Trailheads [highway=trailhead] (not leisure or tourism)
Hotels [tourism=hotel] (building=hotel is only the structure)
Museums [tourism=museum]
Cities / towns [place=city,town]
Parking [amenity=parking]
ATMs [amenity=atm]
Places of worship [amenity=place_of_worship] (refine with religion=*)
Dry climate [climate:precip_annual<16] (inches/year)
Mild winters [climate:low_jan>30] (°F, average January low)
Sunny [climate:sunshine>65] (percent clear-sky)
Growing zone [climate:hardiness_zone=6a,6b]
Out of the floodplain [fema:sfha=no]
Low wildfire risk [usfs:wildfire_hazard=very_low,low]
Has it burned [fire:last_burn_year] (absent = no mapped fire)
Clean air [epa:aqi_unhealthy_days<3]
Quiet [bts:noise<55] (dBA; absent = nothing modelled)

Gotchas

  • Feature vs. building. The establishment lives on amenity/tourism/ shop; building=<same word> is only the physical structure.
  • amenity=college ≠ university. US higher ed is amenity=university; amenity=college is further education and vocational.
  • Values are singular. "bookstores" → shop=books, "restaurants" → amenity=restaurant.
  • Chains carry sub-features. A Costco is one shop=wholesale node, but the same name is also on its gas station, food court, pharmacy, tyre centre, and per-department nodes. Filter by the primary tag — nw[shop=wholesale][name=~Costco](area=colorado) — never name=~ alone.