LocationLists

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['dataset'], 'properties': {'dataset': {'type': 'string', 'description': 'Dataset slug, e.g. generac-dealers. Use search_datasets first.'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['sessionId'], 'properties': {'sessionId': {'type': 'string', 'pattern': '^cs_', 'description': 'Checkout session id from create_checkout'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['a', 'b'], 'properties': {'a': {'type': 'object', 'properties': {'zip': {'type': 'string'}, 'city': {'type': 'string'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/a/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/a/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/a/properties/near/properties/zip'}, 'place': {'$ref': '#/properties/a/properties/near/properties/place'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': 'Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both).', 'additionalProperties': False}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}.'}, 'state': {'type': 'string'}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12}}, 'additionalProperties': False}, 'maxItems': 12}, 'county': {'type': 'string'}, 'dataset': {'type': 'string'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["51760"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).', 'additionalProperties': False}, 'category': {'type': 'string'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1}, 'opendata': {'type': 'object', 'required': ['source'], 'properties': {'zips': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1, 'description': 'Or cut it to 5-digit ZIP codes, e.g. ["78114", "78154"] (/find: opendata_zips=78114,78154)'}, 'state': {'type': 'string', 'description': 'Cut the register to one state, e.g. "NY" (/find: opendata_state=NY)'}, 'where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 12, 'description': 'Conditions on the register\'s own columns, each "column:op:value" with op eq | starts_with | contains | not_blank, e.g. ["license_status:starts_with:Active"] (/find: opendata_where=)'}, 'source': {'type': 'string', 'description': 'The register\'s key, "<domain>/<id>" — search_datasets with kind "register" finds it, e.g. "data.ny.gov/cb42-qumz" (New York\'s licensed child-care programs)'}}, 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.', 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'description': 'First set: dataset, datasets or category, plus filters', 'additionalProperties': False}, 'b': {'type': 'object', 'properties': {'zip': {'$ref': '#/properties/a/properties/zip'}, 'city': {'$ref': '#/properties/a/properties/city'}, 'near': {'$ref': '#/properties/a/properties/near'}, 'metro': {'$ref': '#/properties/a/properties/metro'}, 'state': {'$ref': '#/properties/a/properties/state'}, 'where': {'$ref': '#/properties/a/properties/where'}, 'county': {'$ref': '#/properties/a/properties/county'}, 'dataset': {'$ref': '#/properties/a/properties/dataset'}, 'exclude': {'$ref': '#/properties/a/properties/exclude'}, 'areas_in': {'$ref': '#/properties/a/properties/areas_in'}, 'category': {'$ref': '#/properties/a/properties/category'}, 'datasets': {'$ref': '#/properties/a/properties/datasets'}, 'opendata': {'$ref': '#/properties/a/properties/opendata', 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.'}, 'area_where': {'$ref': '#/properties/a/properties/area_where'}, 'area_columns': {'$ref': '#/properties/a/properties/area_columns'}}, 'description': 'Second set, given the same way', 'additionalProperties': False}, 'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string', 'description': 'Area unit for both/only-a/only-b (default county)'}, 'in_state': {'type': 'string', 'description': 'Only areas in this state'}, 'radius_miles': {'type': 'number', 'maximum': 250, 'description': 'Distance that counts as together (default 1 mile)', 'exclusiveMinimum': 0}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['by', 'sets'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'all': {'type': 'boolean', 'description': 'List every matching area, however many'}, 'has': {'type': 'array', 'items': {'type': 'string'}}, 'per': {'type': 'number', 'minimum': 1, 'description': 'Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field.'}, 'sets': {'type': 'array', 'items': {'type': 'object', 'required': ['label'], 'properties': {'zip': {'type': 'string'}, 'city': {'type': 'string'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/sets/items/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/sets/items/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/sets/items/properties/near/properties/zip'}, 'place': {'$ref': '#/properties/sets/items/properties/near/properties/place'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': 'Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both).', 'additionalProperties': False}, 'label': {'type': 'string', 'description': 'Short name, e.g. "a" or "closed"'}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}.'}, 'state': {'type': 'string'}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12}}, 'additionalProperties': False}, 'maxItems': 12}, 'county': {'type': 'string'}, 'dataset': {'type': 'string'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["51760"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).', 'additionalProperties': False}, 'category': {'type': 'string'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1}, 'opendata': {'type': 'object', 'required': ['source'], 'properties': {'zips': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1, 'description': 'Or cut it to 5-digit ZIP codes, e.g. ["78114", "78154"] (/find: opendata_zips=78114,78154)'}, 'state': {'type': 'string', 'description': 'Cut the register to one state, e.g. "NY" (/find: opendata_state=NY)'}, 'where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 12, 'description': 'Conditions on the register\'s own columns, each "column:op:value" with op eq | starts_with | contains | not_blank, e.g. ["license_status:starts_with:Active"] (/find: opendata_where=)'}, 'source': {'type': 'string', 'description': 'The register\'s key, "<domain>/<id>" — search_datasets with kind "register" finds it, e.g. "data.ny.gov/cb42-qumz" (New York\'s licensed child-care programs)'}}, 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.', 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}, 'maxItems': 4, 'minItems': 1}, 'lacks': {'type': 'array', 'items': {'type': 'string'}}, 'limit': {'type': 'integer', 'maximum': 1000, 'minimum': 1, 'description': 'Areas listed per call (default: every matching area when 500 or fewer match, else the first 100; at most 1000)'}, 'offset': {'type': 'integer', 'maximum': 100000, 'minimum': 0, 'description': "Areas to skip before the first listed: page through every matching area with limit + offset. The answer's page block gives total and the next offset (null on the last page)."}, 'in_state': {'type': 'string', 'description': 'Only areas in this state'}, 'order_by': {'type': 'object', 'required': ['field'], 'properties': {'field': {'type': 'string', 'description': 'count (default), a set label, per (with per), name, or an attribute the rows carry from area_where / per_field / area_columns'}, 'direction': {'enum': ['asc', 'desc'], 'type': 'string', 'description': 'Default desc (name: asc)'}}, 'description': 'Rank the areas: {"field": "median_household_income"} for the richest counties first, {"field": "a", "direction": "asc"} for the fewest of set a first. Unknown figures sort last.', 'additionalProperties': False}, 'per_field': {'type': 'string', 'description': 'With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, every <sector>_establishments / <sector>_employees, and every naics_<code>_establishments / naics_<code>_employees (2- to 6-digit NAICS, e.g. naics_8111_establishments).'}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only areas meeting a Census condition, each as "<kind>:<attribute><op><value>" about the same kind as by (or a state), e.g. "county:population>500000" with lacks for "counties over 500k people with no X". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'top_values': {'type': 'object', 'required': ['column'], 'properties': {'n': {'type': 'integer', 'maximum': 5, 'minimum': 1, 'description': 'Values per area, 1-5 (default 3)'}, 'column': {'type': 'string', 'description': "A CLASS column of the sets' files: category, type, brand, tier, status… (get_dataset lists columns)"}}, 'description': 'The most common values of one class column per area, per set (top on each area row): what kinds of place make up a count — {"column": "category"} shows that Elkhart County\'s 47 trailer dealers are 30 dealers, 12 manufacturers, 5 horse-trailer specialists. Free, bounded, and the same figures a filtered count gives. A name, address or contact column is refused: a free answer names classes, never records — the rows are in the file you buy.', 'additionalProperties': False}, 'with_sample': {'type': 'boolean', 'description': "Free. On the first 10 areas listed, each set's fixed published sample rows that fall inside the area (source_dataset, the list's columns, matches_your_question). The same published rows every question sees, so cells never add up to the file."}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 40, 'description': 'Census and NOAA figures RETURNED on every area row (in attributes), each as "<kind>:<attribute>,<attribute>" about the same kind as by (or "state:…" for the area\'s state, returned as state_<attribute>), e.g. ["county:population,median_household_income,households"]. Free. The vocabulary is the area manifest — the same words area_where takes (population, households, median household income, median age, growth since 2020, home values, <sector>_establishments / <sector>_employees by NAICS sector, and the NOAA weather elements); https://locationlists.com/find?areas=county&dataset=<slug>&area_columns=county:<attribute> refuses an unknown attribute by name and lists the nearest ones. Rank the areas by one with order_by.'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'zip': {'type': 'string', 'description': 'Shortcut for where zip eq <value>'}, 'city': {'type': 'string', 'description': 'Shortcut for where city eq <value>'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which.'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).", 'additionalProperties': False}, 'limit': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': "Price a slice instead of every match: the first `limit` rows past `offset` in the delivery order — e.g. near a place with limit 10 for the 10 nearest — exactly the rows a purchase with the same limit returns. The answer's `slice` has their exact price, each row source at its own rate (open-data rows vs rows of lists we sell), and the card price."}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase.'}, 'state': {'type': 'string', 'description': 'Shortcut for where state eq <value>. Two-letter code.'}, 'total': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string', 'description': "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."}, 'field': {'type': 'string', 'description': 'Column name as listed by get_dataset (columns[].name), e.g. revenue_amt'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'With op any_of: the conditions, any one of which keeps the row (one level).'}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.'}, 'county': {'type': 'string', 'description': 'Shortcut for where county eq <value>'}, 'offset': {'type': 'integer', 'maximum': 10000, 'minimum': 0, 'description': 'With limit: skip this many matching rows, as a paged purchase does, so the quote is for that page'}, 'dataset': {'type': 'string', 'description': 'Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"}, 'permits': {'type': 'object', 'properties': {'days': {'type': 'integer', 'maximum': 3650, 'minimum': 1, 'description': 'Issued in the last N days (default 365). /find: permits_days=90'}, 'type': {'enum': ['any', 'new_construction', 'commercial', 'residential', 'renovation', 'demolition', 'electrical', 'plumbing', 'mechanical', 'roofing', 'solar', 'sign', 'pool'], 'type': 'string', 'description': 'The kind of permit (default any). /find: permits_type=commercial'}, 'metres': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'How close a permit must be to the row, in metres (default 60). /find: permits_m=60'}, 'min_usd': {'type': 'number', 'minimum': 0, 'description': 'Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000'}}, 'description': 'Only rows with a BUILDING PERMIT nearby, read live from the city\'s own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set\'s city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city\'s register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer\'s link is the page.', 'additionalProperties': False}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.', 'additionalProperties': False}, 'category': {'type': 'string', 'description': 'Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 150, 'minItems': 1, 'description': 'Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)'}, 'order_by': {'type': 'object', 'required': ['field'], 'properties': {'field': {'type': 'string'}, 'direction': {'enum': ['asc', 'desc'], 'type': 'string', 'description': 'Default desc'}}, 'description': 'With limit: the order the slice is taken in, as a purchase takes it. Without it, nearest first with `near`, else file order.', 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['slug'], 'properties': {'slug': {'type': 'string', 'description': 'Dataset slug'}, 'email': {'type': 'string', 'format': 'email', 'description': 'Buyer email, if known — prefills Checkout and is where the download link is sent'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'zip': {'type': 'string', 'description': 'Shortcut for where zip eq <value>'}, 'city': {'type': 'string', 'description': 'Shortcut for where city eq <value>'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which.'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).", 'additionalProperties': False}, 'email': {'type': 'string', 'format': 'email', 'description': 'Buyer email, if known — prefills Checkout and is where the download link is sent'}, 'limit': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': 'Most rows to buy (max 10,000). Default: every match up to the max. Priced on the rows that actually match, never more.'}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase.'}, 'shape': {'enum': ['hubspot', 'salesforce'], 'type': 'string', 'description': 'CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.'}, 'state': {'type': 'string', 'description': 'Shortcut for where state eq <value>. Two-letter code.'}, 'total': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string', 'description': "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."}, 'field': {'type': 'string', 'description': 'Column name as listed by get_dataset (columns[].name), e.g. revenue_amt'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'With op any_of: the conditions, any one of which keeps the row (one level).'}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.'}, 'county': {'type': 'string', 'description': 'Shortcut for where county eq <value>'}, 'relate': {'type': 'object', 'additionalProperties': {}}, 'dataset': {'type': 'string', 'description': 'Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"}, 'permits': {'type': 'object', 'properties': {'days': {'type': 'integer', 'maximum': 3650, 'minimum': 1, 'description': 'Issued in the last N days (default 365). /find: permits_days=90'}, 'type': {'enum': ['any', 'new_construction', 'commercial', 'residential', 'renovation', 'demolition', 'electrical', 'plumbing', 'mechanical', 'roofing', 'solar', 'sign', 'pool'], 'type': 'string', 'description': 'The kind of permit (default any). /find: permits_type=commercial'}, 'metres': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'How close a permit must be to the row, in metres (default 60). /find: permits_m=60'}, 'min_usd': {'type': 'number', 'minimum': 0, 'description': 'Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000'}}, 'description': 'Only rows with a BUILDING PERMIT nearby, read live from the city\'s own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set\'s city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city\'s register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer\'s link is the page.', 'additionalProperties': False}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.', 'additionalProperties': False}, 'category': {'type': 'string', 'description': 'Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 150, 'minItems': 1, 'description': 'Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)'}, 'order_by': {'type': 'object', 'required': ['field'], 'properties': {'field': {'type': 'string'}, 'direction': {'enum': ['asc', 'desc'], 'type': 'string', 'description': 'Default desc'}}, 'description': "Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last.", 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['email'], 'properties': {'zip': {'type': 'string', 'description': 'Shortcut for where zip eq <value>'}, 'city': {'type': 'string', 'description': 'Shortcut for where city eq <value>'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which.'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).", 'additionalProperties': False}, 'email': {'type': 'string', 'description': "The user's email, as they gave it"}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase.'}, 'state': {'type': 'string', 'description': 'Shortcut for where state eq <value>. Two-letter code.'}, 'total': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string', 'description': "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."}, 'field': {'type': 'string', 'description': 'Column name as listed by get_dataset (columns[].name), e.g. revenue_amt'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'With op any_of: the conditions, any one of which keeps the row (one level).'}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.'}, 'county': {'type': 'string', 'description': 'Shortcut for where county eq <value>'}, 'dataset': {'type': 'string', 'description': 'Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"}, 'permits': {'type': 'object', 'properties': {'days': {'type': 'integer', 'maximum': 3650, 'minimum': 1, 'description': 'Issued in the last N days (default 365). /find: permits_days=90'}, 'type': {'enum': ['any', 'new_construction', 'commercial', 'residential', 'renovation', 'demolition', 'electrical', 'plumbing', 'mechanical', 'roofing', 'solar', 'sign', 'pool'], 'type': 'string', 'description': 'The kind of permit (default any). /find: permits_type=commercial'}, 'metres': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'How close a permit must be to the row, in metres (default 60). /find: permits_m=60'}, 'min_usd': {'type': 'number', 'minimum': 0, 'description': 'Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000'}}, 'description': 'Only rows with a BUILDING PERMIT nearby, read live from the city\'s own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set\'s city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city\'s register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer\'s link is the page.', 'additionalProperties': False}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.', 'additionalProperties': False}, 'category': {'type': 'string', 'description': 'Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 150, 'minItems': 1, 'description': 'Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)'}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['slug'], 'properties': {'slug': {'type': 'string', 'description': 'Dataset slug from search_datasets, e.g. bobcat-dealers'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['slugs'], 'properties': {'slugs': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 50, 'minItems': 1, 'description': 'Dataset slugs'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'zip': {'type': 'string', 'description': 'Shortcut for where zip eq <value>'}, 'city': {'type': 'string', 'description': 'Shortcut for where city eq <value>'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which.'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).", 'additionalProperties': False}, 'rows': {'type': 'integer', 'maximum': 10, 'minimum': 1, 'description': "Rows to return (default 10; with filters default 3, up to the published sample's size — samplePublished in the answer)"}, 'slug': {'type': 'string', 'description': 'Dataset slug. Or datasets / category to preview several at once.'}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase.'}, 'shape': {'enum': ['hubspot', 'salesforce'], 'type': 'string', 'description': 'CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.'}, 'state': {'type': 'string', 'description': 'Shortcut for where state eq <value>. Two-letter code.'}, 'total': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string', 'description': "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."}, 'field': {'type': 'string', 'description': 'Column name as listed by get_dataset (columns[].name), e.g. revenue_amt'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'With op any_of: the conditions, any one of which keeps the row (one level).'}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.'}, 'county': {'type': 'string', 'description': 'Shortcut for where county eq <value>'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"}, 'permits': {'type': 'object', 'properties': {'days': {'type': 'integer', 'maximum': 3650, 'minimum': 1, 'description': 'Issued in the last N days (default 365). /find: permits_days=90'}, 'type': {'enum': ['any', 'new_construction', 'commercial', 'residential', 'renovation', 'demolition', 'electrical', 'plumbing', 'mechanical', 'roofing', 'solar', 'sign', 'pool'], 'type': 'string', 'description': 'The kind of permit (default any). /find: permits_type=commercial'}, 'metres': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'How close a permit must be to the row, in metres (default 60). /find: permits_m=60'}, 'min_usd': {'type': 'number', 'minimum': 0, 'description': 'Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000'}}, 'description': 'Only rows with a BUILDING PERMIT nearby, read live from the city\'s own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set\'s city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city\'s register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer\'s link is the page.', 'additionalProperties': False}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.', 'additionalProperties': False}, 'category': {'type': 'string', 'description': 'Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 150, 'minItems': 1, 'description': 'Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)'}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'zip': {'type': 'string', 'description': 'Shortcut for where zip eq <value>'}, 'city': {'type': 'string', 'description': 'Shortcut for where city eq <value>'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which.'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).", 'additionalProperties': False}, 'limit': {'type': 'integer', 'maximum': 1000, 'minimum': 1, 'description': "Rows to return and pay for. Default: every matching row, up to the most one call can return. That maximum depends on how wide the dataset's rows are, from 100 to 1,000; count_locations reports it as maxRowsPerCall, and a larger limit is reduced to it before pricing."}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase.'}, 'shape': {'enum': ['hubspot', 'salesforce'], 'type': 'string', 'description': 'CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.'}, 'state': {'type': 'string', 'description': 'Shortcut for where state eq <value>. Two-letter code.'}, 'total': {'type': 'integer', 'maximum': 1000, 'minimum': 1, 'description': 'With datasets or category: rows to return and pay for across all of them (default every distinct match, up to 1,000), in one payment'}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string', 'description': "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."}, 'field': {'type': 'string', 'description': 'Column name as listed by get_dataset (columns[].name), e.g. revenue_amt'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'With op any_of: the conditions, any one of which keeps the row (one level).'}}, 'additionalProperties': False}, 'maxItems': 12, 'description': 'Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.'}, 'county': {'type': 'string', 'description': 'Shortcut for where county eq <value>'}, 'offset': {'type': 'integer', 'maximum': 10000, 'minimum': 0, 'description': 'Skip this many matching rows, to page past the first call'}, 'relate': {'type': 'object', 'additionalProperties': {}}, 'dataset': {'type': 'string', 'description': 'Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"}, 'permits': {'type': 'object', 'properties': {'days': {'type': 'integer', 'maximum': 3650, 'minimum': 1, 'description': 'Issued in the last N days (default 365). /find: permits_days=90'}, 'type': {'enum': ['any', 'new_construction', 'commercial', 'residential', 'renovation', 'demolition', 'electrical', 'plumbing', 'mechanical', 'roofing', 'solar', 'sign', 'pool'], 'type': 'string', 'description': 'The kind of permit (default any). /find: permits_type=commercial'}, 'metres': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'How close a permit must be to the row, in metres (default 60). /find: permits_m=60'}, 'min_usd': {'type': 'number', 'minimum': 0, 'description': 'Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000'}}, 'description': 'Only rows with a BUILDING PERMIT nearby, read live from the city\'s own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set\'s city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city\'s register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer\'s link is the page.', 'additionalProperties': False}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.', 'additionalProperties': False}, 'category': {'type': 'string', 'description': 'Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 150, 'minItems': 1, 'description': 'Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)'}, 'order_by': {'type': 'object', 'required': ['field'], 'properties': {'field': {'type': 'string'}, 'direction': {'enum': ['asc', 'desc'], 'type': 'string', 'description': 'Default desc'}}, 'description': "Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last.", 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['relate'], 'properties': {'zip': {'type': 'string'}, 'city': {'type': 'string'}, 'near': {'type': 'object', 'properties': {'lat': {'type': 'number'}, 'lng': {'type': 'number'}, 'zip': {'type': 'string', 'description': '5-digit zip'}, 'place': {'type': 'string', 'description': 'City or town with state, e.g. "Topeka, KS"'}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {'lat': {'$ref': '#/properties/near/properties/lat'}, 'lng': {'$ref': '#/properties/near/properties/lng'}, 'zip': {'$ref': '#/properties/near/properties/zip'}, 'place': {'$ref': '#/properties/near/properties/place'}}, 'additionalProperties': False}, 'maxItems': 10, 'minItems': 1, 'description': "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices."}, 'radius_miles': {'type': 'number', 'maximum': 500, 'description': 'Only rows within this straight-line distance of the point (or of any of the points). Free.', 'exclusiveMinimum': 0}, 'drive_minutes': {'type': 'integer', 'maximum': 240, 'minimum': 5, 'description': 'Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': 'Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both).', 'additionalProperties': False}, 'metro': {'type': 'string', 'description': 'Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}.'}, 'state': {'type': 'string'}, 'total': {'type': 'integer', 'maximum': 10000, 'minimum': 1, 'description': 'Base rows wanted, first in answer order'}, 'where': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}, 'any_of': {'type': 'array', 'items': {'type': 'object', 'required': ['field', 'op'], 'properties': {'op': {'enum': ['eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'in', 'contains', 'starts_with', 'is_blank', 'not_blank', 'not_contains', 'not_in', 'any_of'], 'type': 'string'}, 'field': {'type': 'string'}, 'value': {'anyOf': [{'type': 'string'}, {'type': 'number'}, {'type': 'boolean'}, {'type': 'array', 'items': {'type': ['string', 'number']}}]}}, 'additionalProperties': False}, 'maxItems': 12}}, 'additionalProperties': False}, 'maxItems': 12}, 'county': {'type': 'string'}, 'relate': {'type': 'object', 'required': ['mode'], 'properties': {'k': {'type': 'integer', 'maximum': 3, 'minimum': 1, 'description': 'nearest: how many anchors per base row. next_best / typically_near: H3 rings counted as nearby, 1-3 (default 2, about a mile)'}, 'also': {'type': 'array', 'items': {'type': 'string'}, 'description': 'same_place / near only: the sets a base row must be the same place as (near: within near_m of) some row of, by label ("b", "c"); default every set'}, 'mode': {'enum': ['nearest', 'count_within', 'within_any', 'none_within', 'same_place', 'not_same_place', 'near', 'not_near', 'next_best', 'typically_near', 'overlap'], 'type': 'string', 'description': 'nearest | count_within | within_any | none_within | same_place | not_same_place | near | not_near | next_best | typically_near | overlap'}, 'limit': {'type': 'integer', 'maximum': 50, 'minimum': 1, 'description': 'next_best only: candidates ranked (default 50)'}, 'order': {'enum': ['asc', 'desc'], 'type': 'string'}, 'anchor': {'type': 'object', 'properties': {'zip': {'$ref': '#/properties/zip'}, 'city': {'$ref': '#/properties/city'}, 'near': {'$ref': '#/properties/near'}, 'metro': {'$ref': '#/properties/metro'}, 'state': {'$ref': '#/properties/state'}, 'where': {'$ref': '#/properties/where'}, 'county': {'$ref': '#/properties/county'}, 'dataset': {'$ref': '#/properties/dataset'}, 'exclude': {'$ref': '#/properties/exclude'}, 'areas_in': {'$ref': '#/properties/areas_in'}, 'category': {'$ref': '#/properties/category'}, 'datasets': {'$ref': '#/properties/datasets'}, 'opendata': {'$ref': '#/properties/opendata', 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.'}, 'area_where': {'$ref': '#/properties/area_where'}, 'area_columns': {'$ref': '#/properties/area_columns'}}, 'description': 'The other set: dataset, datasets or category, plus filters. Every mode but next_best and typically_near needs it', 'additionalProperties': False}, 'lambda': {'type': 'number', 'maximum': 10, 'description': 'next_best only: the Huff distance-decay exponent (default 2, the traditional value)', 'exclusiveMinimum': 0}, 'near_m': {'type': 'integer', 'maximum': 200, 'minimum': 10, 'description': 'near / not_near only: how close, in metres, a place of the other set must be (default 30, 10-200). Distance only — no name or address is compared. /find: near_m=30.'}, 'anchors': {'type': 'array', 'items': {'type': 'object', 'properties': {'zip': {'$ref': '#/properties/zip'}, 'city': {'$ref': '#/properties/city'}, 'near': {'$ref': '#/properties/near'}, 'metro': {'$ref': '#/properties/metro'}, 'state': {'$ref': '#/properties/state'}, 'where': {'$ref': '#/properties/where'}, 'county': {'$ref': '#/properties/county'}, 'dataset': {'$ref': '#/properties/dataset'}, 'exclude': {'$ref': '#/properties/exclude'}, 'areas_in': {'$ref': '#/properties/areas_in'}, 'category': {'$ref': '#/properties/category'}, 'datasets': {'$ref': '#/properties/datasets'}, 'opendata': {'$ref': '#/properties/opendata', 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.'}, 'area_where': {'$ref': '#/properties/area_where'}, 'area_columns': {'$ref': '#/properties/area_columns'}}, 'additionalProperties': False}, 'maxItems': 2, 'description': 'same_place (and near) only: further sets (c, d) after anchor (b), each given the same way'}, 'not_also': {'type': 'array', 'items': {'type': 'string'}, 'description': 'same_place / near only: the sets a base row must NOT match any row of; not_same_place (not_near) with one set is ["b"]'}, 'anchor_all': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'string'}}, 'maxItems': 3, 'minItems': 2, 'description': 'count_within / within_any: ONE OF EACH instead of one of any. The anchor set\'s dataset slugs grouped as the question named them — [["a"],["b"]] keeps only base rows with a row of BOTH within the distance. Omit for the ordinary any-of reading.'}, 'radius_miles': {'type': 'number', 'maximum': 250, 'exclusiveMinimum': 0}, 'reveal_anchor': {'type': 'boolean'}, 'within_drive_minutes': {'type': 'integer', 'maximum': 60, 'minimum': 5, 'description': 'Drive time instead of radius_miles (5-60 minutes). count_within / within_any / none_within only; at most 60 base rows. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free).'}}, 'description': "Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. near / not_near: the same N-way shape matched by DISTANCE ONLY, within near_m metres (default 30) — the rule for a live open-data register given as a set's opendata. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. typically_near: the PROFILE of the same analysis, no anchor, state optional — what the base list's locations typically have nearby (the signature: each kind of place or brand near at least 15% of them and at least 1.5x as often as near a typical commercial spot), with a link to the ranked sites; without a state it is computed in the list's top state and the answer says which. Under 10 locations the pattern is shown with a thin-sample note and nothing is sold. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate.", 'additionalProperties': False}, 'dataset': {'type': 'string'}, 'exclude': {'type': 'array', 'items': {'type': 'string'}, 'description': "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."}, 'areas_in': {'type': 'object', 'required': ['by', 'ids'], 'properties': {'by': {'enum': ['county', 'zip', 'state', 'metro'], 'type': 'string'}, 'ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 200, 'minItems': 1, 'description': 'County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row'}}, 'description': 'Only rows in these areas, e.g. {"by": "county", "ids": ["51760"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).', 'additionalProperties': False}, 'category': {'type': 'string'}, 'datasets': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1}, 'opendata': {'type': 'object', 'required': ['source'], 'properties': {'zips': {'type': 'array', 'items': {'type': 'string'}, 'minItems': 1, 'description': 'Or cut it to 5-digit ZIP codes, e.g. ["78114", "78154"] (/find: opendata_zips=78114,78154)'}, 'state': {'type': 'string', 'description': 'Cut the register to one state, e.g. "NY" (/find: opendata_state=NY)'}, 'where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 12, 'description': 'Conditions on the register\'s own columns, each "column:op:value" with op eq | starts_with | contains | not_blank, e.g. ["license_status:starts_with:Active"] (/find: opendata_where=)'}, 'source': {'type': 'string', 'description': 'The register\'s key, "<domain>/<id>" — search_datasets with kind "register" finds it, e.g. "data.ny.gov/cb42-qumz" (New York\'s licensed child-care programs)'}}, 'description': 'INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state\'s licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher\'s own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.', 'additionalProperties': False}, 'area_where': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area\'s, not the row\'s, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor\'s degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census\'s titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), ….'}, 'area_columns': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'description': 'Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there).'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['topic'], 'properties': {'name': {'type': 'string', 'description': "The user's name, if they gave it"}, 'email': {'type': 'string', 'description': "The user's email, only if they gave it, so we can tell them when the list is ready"}, 'topic': {'type': 'string', 'description': 'The list the user wants, in their words (2-200 characters)'}, 'company': {'type': 'string', 'description': "The user's company, if they gave it"}, 'details': {'type': 'string', 'description': 'Anything else they specified: places, fields needed (phone, website, email), timing'}, 'use_case': {'type': 'string', 'description': 'What they will use the list for, if they said (e.g. sales outreach, territory planning)'}, 'email_declined': {'type': 'boolean', 'description': 'True only when you asked the user for their email and they chose not to leave one'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'kind': {'enum': ['list', 'register'], 'type': 'string', 'description': 'list (default): the lists we sell. register: live public registers read from their publisher, e.g. {"query": "child care", "kind": "register", "state": "NY"} — each result\'s source goes in relate_locations as {"opendata": {"source": "<source>", "state": "NY"}}.'}, 'limit': {'type': 'integer', 'maximum': 1000, 'minimum': 1, 'description': 'Max results (default: every match)'}, 'query': {'type': 'string', 'description': 'Free text: brand, kind of business, industry or product'}, 'state': {'type': 'string', 'description': 'With kind "register": prefer registers covering this state, e.g. "NY"'}, 'category': {'type': 'string', 'description': 'Restrict to one catalog category, industry or subcategory'}}, 'additionalProperties': False}

{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['message'], 'properties': {'email': {'type': 'string', 'description': "The user's email, only if they gave it"}, 'dataset': {'type': 'string', 'description': 'Dataset slug it concerns, if any, e.g. generac-dealers'}, 'message': {'type': 'string', 'description': "The feedback, in the user's words (5-4000 characters)"}, 'category': {'enum': ['bug', 'data quality', 'pricing', 'feature', 'other'], 'type': 'string', 'description': 'What it is about (default other)'}, 'email_declined': {'type': 'boolean', 'description': 'True only when you asked the user for their email and they chose not to leave one'}}, 'additionalProperties': False}