# DataUSA Census & Economic Data Scraper (`lulzasaur/datausa-scraper`) Actor Scrape US census and economic data from DataUSA. Get population, income, poverty, housing, industries, occupations, and education for any city, county, or state. - **URL**: https://apify.com/lulzasaur/datausa-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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 ## DataUSA Census & Economic Data Scraper Scrape comprehensive US census and economic data from [DataUSA.io](https://datausa.io) for any city, county, state, or metro area. DataUSA aggregates data from the US Census Bureau's American Community Survey (ACS), Bureau of Labor Statistics, and other federal data sources. ### What data does it extract? For each geographic location, the scraper returns: | Field | Description | |-------|-------------| | `name` | Location name (e.g., "New York, NY") | | `geoId` | Census geographic identifier | | `geoType` | Type: Place, State, County, or MSA | | `dataYear` | Most recent data year available | | `population` | Total population | | `medianHouseholdIncome` | Median household income ($) | | `medianAge` | Median age of residents | | `povertyRate` | Poverty rate (%) | | `medianPropertyValue` | Median property value ($) | | `homeownershipRate` | Homeownership rate (%) | | `employmentRate` | Civilian employment rate (%) | | `topIndustries` | Top 5 industries by workforce size | | `topOccupations` | Top 5 occupations by median earnings | | `educationLevels` | Education attainment breakdown (%) | | `sourceUrl` | DataUSA profile page URL | | `scrapedAt` | Timestamp of scrape | ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQueries` | string[] | `["New York"]` | City, county, or state names to search | | `maxListings` | integer | 50 | Maximum profiles to return (1-500) | | `proxyConfiguration` | object | - | Optional proxy settings | ### Example output ```json { "name": "New York, NY", "geoId": "16000US3651000", "geoType": "Place", "dataYear": 2024, "population": 8483844, "medianHouseholdIncome": 3283706, "medianAge": 37.1, "povertyRate": 17.2, "medianPropertyValue": 741500, "homeownershipRate": 32.9, "employmentRate": 54.6, "topIndustries": [ { "name": "Professional, Scientific, & Technical Services", "workforce": 522310 }, { "name": "Educational Services, & Health Care & Social Assistance", "workforce": 499020 } ], "topOccupations": [ { "name": "Management, Business, Science, & Arts Occupations", "medianEarnings": 83944 } ], "educationLevels": { "Bachelors Degree": 17.2, "Graduate Degree": 12.6, "High School or Equivalent": 20.9 }, "sourceUrl": "https://datausa.io/profile/geo/new-york-ny", "scrapedAt": "2026-04-26T12:00:00.000Z" } ```` ### Data sources All data comes from the DataUSA Tesseract OLAP API, which aggregates: - **US Census Bureau** - American Community Survey (ACS) 5-year estimates - **Bureau of Labor Statistics** - Employment and industry data - **Census PUMS** - Education attainment microdata ### Use cases - **Real estate analysis** - Compare property values, homeownership rates, and income across locations - **Market research** - Understand demographic profiles for target markets - **Economic development** - Track employment, poverty, and industry trends - **Academic research** - Access structured census data for analysis - **Site selection** - Evaluate locations for business expansion ### Cost This Actor uses pay-per-event pricing. Each geographic profile returned costs 1 platform usage event. # Actor input Schema ## `searchQueries` (type: `array`): City, county, or state names to search for. Examples: 'New York', 'Los Angeles', 'Cook County', 'Texas'. ## `maxListings` (type: `integer`): Maximum number of geographic profiles to return (across all queries). ## `proxyConfiguration` (type: `object`): Optional proxy to use. ## Actor input object example ```json { "searchQueries": [ "New York", "Los Angeles", "Chicago" ], "maxListings": 50 } ``` # 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 = { "searchQueries": [ "New York", "Los Angeles", "Chicago" ] }; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/datausa-scraper").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 = { "searchQueries": [ "New York", "Los Angeles", "Chicago", ] } # Run the Actor and wait for it to finish run = client.actor("lulzasaur/datausa-scraper").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 '{ "searchQueries": [ "New York", "Los Angeles", "Chicago" ] }' | apify call lulzasaur/datausa-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/datausa-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DataUSA Census & Economic Data Scraper", "description": "Scrape US census and economic data from DataUSA. Get population, income, poverty, housing, industries, occupations, and education for any city, county, or state.", "version": "1.0", "x-build-id": "6a0cd4mX7A8s6awtC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~datausa-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-datausa-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~datausa-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-datausa-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~datausa-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-datausa-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "City, county, or state names to search for. Examples: 'New York', 'Los Angeles', 'Cook County', 'Texas'.", "default": [ "New York" ], "items": { "type": "string" } }, "maxListings": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of geographic profiles to return (across all queries).", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional proxy to use." } } }, "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 } } } } } } } } } } ```