# US Census ACS - Demographics & Income | $0.002 (`pink_comic/us-census-acs-search`) Actor Search Census American Community Survey (ACS). Query demographic, economic, housing, and social statistics by geography. For market researchers, urban planners, real estate analysts, and grant writers. Pay per result. - **URL**: https://apify.com/pink\_comic/us-census-acs-search.md - **Developed by:** [Ava Torres](https://apify.com/pink_comic) (community) - **Categories:** Business, Automation, MCP servers - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## U.S. Census ACS - Demographics, Income, Housing & Population Data by Geography Query the U.S. Census Bureau American Community Survey (ACS) for population, income, housing, education, employment, and demographic data — at any geographic level from national down to census tract. Four built-in presets cover the most common use cases, or specify any Census variable codes directly. Covers all 50 states, 3,200+ counties, 33,000+ ZIP codes, 29,000+ cities and places, and 85,000+ census tracts. Data is sourced from the official Census Bureau API. No API key required. --- ### Output Fields | Field | Type | Description | |---|---|---| | `name` | string | Geography name (e.g. `Los Angeles County, California`) | | `geoLevel` | string | Geography level (state, county, zip, place, tract, etc.) | | `stateFIPS` | string | 2-digit state FIPS code | | `countyFIPS` | string | 3-digit county FIPS code | | `data` | object | Key-value map of Census variable codes to values | The `data` object contains the actual Census variable values. When using a preset, fields are labeled with readable names. When using custom variables, keys are the raw Census codes (e.g. `B01001_001E`). --- ### Use Cases - **Market sizing and site selection** — Pull median household income, population density, and age distribution for any ZIP code or county before opening a location or launching a campaign. - **Real estate and mortgage analytics** — Get median home values, rental rates, occupancy, and housing stock data by census tract for underwriting or investment screening. - **Public health and nonprofit grant writing** — Retrieve poverty rates, uninsured rates, and educational attainment by county to support grant applications. - **Political and academic research** — Analyze demographic shifts, income inequality, or language diversity across congressional districts or metro areas. - **Lead generation and territory planning** — Segment geographies by income, age, or household composition to prioritize sales territories or ad targeting. --- ### How to Use Choose a preset (or leave blank for custom variables), select a geography level, and optionally filter by state or county FIPS code. **Example input — demographics for all U.S. states:** ```json { "preset": "demographics", "geoLevel": "state", "year": 2022, "maxResults": 52 } ```` **Example input — economic data for all counties in Texas:** ```json { "preset": "economics", "geoLevel": "county", "stateFIPS": "48", "year": 2022, "maxResults": 300 } ``` **Example input — housing data for ZIP codes in California:** ```json { "preset": "housing", "geoLevel": "zip", "stateFIPS": "06", "year": 2022, "maxResults": 2000 } ``` **Example input — custom variables for a specific city:** ```json { "preset": "", "geoLevel": "place", "stateFIPS": "36", "variables": ["B01001_001E", "B19013_001E", "B25077_001E"], "year": 2022, "maxResults": 100 } ``` #### Data Presets | Preset | Variables Included | |---|---| | `demographics` | Total population, median age, sex, race, Hispanic origin | | `economics` | Median household income, per capita income, poverty rate, employment | | `housing` | Median home value, median gross rent, occupancy status, housing units | | `social` | Educational attainment, language spoken at home, household size | #### Geography Levels | Level | Description | |---|---| | `us` | United States (single national row) | | `state` | All 50 states + DC | | `county` | Counties (requires `stateFIPS` for state filter) | | `place` | Cities and places | | `zip` | ZIP Code Tabulation Areas (ZCTAs) | | `tract` | Census tracts (requires `stateFIPS` and `countyFIPS`) | | `congressional_district` | Congressional districts | | `metro` | Metropolitan statistical areas | Find FIPS codes at [www.census.gov/geographies/reference-files](https://www.census.gov/geographies/reference-files.html) and variable codes at [api.census.gov/data/2022/acs/acs5/variables.html](https://api.census.gov/data/2022/acs/acs5/variables.html). *** ### Cost Approximately **$0.10 per run** (platform start fee) plus minimal compute. Most runs complete in under 30 seconds. *** ### Output Formats Results are available as **JSON**, **CSV**, and **Excel** from the Apify dataset interface. The `data` field in JSON contains the full variable map for each geography. *** ### FAQ **Does this require a Census API key?** No. The actor uses the Census Bureau public API without authentication. **What is ACS 5-Year vs 1-Year data?** The 5-year ACS (default) pools 5 years of survey responses for stable estimates at small geographies like ZIP codes and tracts. The 1-year ACS has more recent data but only covers geographies with 65,000+ population. **What year should I use?** ACS 2022 5-year data (the default) is the most recent widely available release. Use the `year` field to access earlier vintages back to 2009. **What FIPS codes do I need?** State FIPS codes are 2 digits (e.g. `06` for California, `48` for Texas, `36` for New York). County FIPS codes are 3 digits and used within a state (e.g. `037` for Los Angeles County). Leading zeros are required. **How do I find Census variable codes?** Browse the full variable list at api.census.gov for the dataset and year you want. Common examples: `B01001_001E` (total population), `B19013_001E` (median household income), `B25077_001E` (median home value). # Actor input Schema ## `preset` (type: `string`): Choose a preset data package or leave empty to specify custom variables. ## `geoLevel` (type: `string`): Geographic level to query data for. ## `stateFIPS` (type: `string`): 2-digit state FIPS code to filter results (e.g. "06" for California, "36" for New York, "48" for Texas). Required for county, place, and tract queries. ## `countyFIPS` (type: `string`): 3-digit county FIPS code (e.g. "037" for Los Angeles County). Used with tract-level queries. ## `geoFilter` (type: `string`): Specific geography code to look up (e.g. "51000" for New York City place, or "90210" for ZIP code). Use "\*" or leave empty for all. ## `dataset` (type: `string`): Census API dataset. Most users should use the default. Only change for advanced queries. ## `year` (type: `integer`): ACS survey year. Default is 2022 (most recent 5-year data). ## `variables` (type: `array`): Census variable codes to retrieve (e.g. \["B01001\_001E", "B19013\_001E"]). Only used when no preset is selected. Find variables at api.census.gov/data/2022/acs/acs5/variables.html ## `maxResults` (type: `integer`): Maximum number of geography records to return. ## Actor input object example ```json { "preset": "demographics", "geoLevel": "state", "stateFIPS": "", "countyFIPS": "", "geoFilter": "", "dataset": "acs/acs5", "year": 2022, "variables": [], "maxResults": 100 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "preset": "demographics", "geoLevel": "state", "year": 2022, "maxResults": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("pink_comic/us-census-acs-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "preset": "demographics", "geoLevel": "state", "year": 2022, "maxResults": 100, } # Run the Actor and wait for it to finish run = client.actor("pink_comic/us-census-acs-search").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "preset": "demographics", "geoLevel": "state", "year": 2022, "maxResults": 100 }' | apify call pink_comic/us-census-acs-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pink_comic/us-census-acs-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "US Census ACS - Demographics & Income | $0.002", "description": "Search Census American Community Survey (ACS). Query demographic, economic, housing, and social statistics by geography. For market researchers, urban planners, real estate analysts, and grant writers. Pay per result.", "version": "0.1", "x-build-id": "qCcFzoAl8UneyFLcg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pink_comic~us-census-acs-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pink_comic-us-census-acs-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pink_comic~us-census-acs-search/runs": { "post": { "operationId": "runs-sync-pink_comic-us-census-acs-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pink_comic~us-census-acs-search/run-sync": { "post": { "operationId": "run-sync-pink_comic-us-census-acs-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "preset": { "title": "Data Preset", "enum": [ "demographics", "economics", "housing", "social", "" ], "type": "string", "description": "Choose a preset data package or leave empty to specify custom variables.", "default": "demographics" }, "geoLevel": { "title": "Geography Level", "enum": [ "us", "state", "county", "place", "zip", "tract", "congressional_district", "metro" ], "type": "string", "description": "Geographic level to query data for.", "default": "state" }, "stateFIPS": { "title": "State FIPS Code", "type": "string", "description": "2-digit state FIPS code to filter results (e.g. \"06\" for California, \"36\" for New York, \"48\" for Texas). Required for county, place, and tract queries.", "default": "" }, "countyFIPS": { "title": "County FIPS Code", "type": "string", "description": "3-digit county FIPS code (e.g. \"037\" for Los Angeles County). Used with tract-level queries.", "default": "" }, "geoFilter": { "title": "Geography Code Filter", "type": "string", "description": "Specific geography code to look up (e.g. \"51000\" for New York City place, or \"90210\" for ZIP code). Use \"*\" or leave empty for all.", "default": "" }, "dataset": { "title": "Dataset", "enum": [ "acs/acs5", "acs/acs1", "acs/acs5/profile", "acs/acs1/profile" ], "type": "string", "description": "Census API dataset. Most users should use the default. Only change for advanced queries.", "default": "acs/acs5" }, "year": { "title": "Survey Year", "minimum": 2009, "maximum": 2023, "type": "integer", "description": "ACS survey year. Default is 2022 (most recent 5-year data).", "default": 2022 }, "variables": { "title": "Custom Variables", "type": "array", "description": "Census variable codes to retrieve (e.g. [\"B01001_001E\", \"B19013_001E\"]). Only used when no preset is selected. Find variables at api.census.gov/data/2022/acs/acs5/variables.html", "default": [], "items": { "type": "string" } }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum number of geography records to return.", "default": 100 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```