# Address to Census Tract & Demographics (ACS) (`errandkit/census-tract-demographics`) Actor

Geocode US addresses or coordinates to Census tract and block group GEOIDs with the U.S. Census Geocoder, then append ACS 5-year demographics: population, median income, median age, poverty rate, education, housing and unemployment. Batch CSV or list input, per-row status, built for AI agents.

- **URL**: https://apify.com/errandkit/census-tract-demographics.md
- **Developed by:** [Killis Haraway](https://apify.com/errandkit) (community)
- **Categories:** Business, Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 location matched to a census tracts

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Address to Census Tract & Demographics

Turn US addresses or latitude/longitude points into **Census tract and block group IDs (GEOIDs)**, then append
**American Community Survey (ACS) 5-year** estimates for that area: population, median age, median household income,
per capita income, poverty rate, education, housing and unemployment.

All data comes from free U.S. Census Bureau services: the **Census Geocoder** for matching and the **Census Data API** for ACS tables.
The Actor handles the parts that take time to get right: batch limits, retries, matching the tract year to the data year
(Connecticut changed its county codes in 2022), the ACS "no estimate" codes, and a clear status on every row.

It was built to be called by AI agents as well as people. You get one dataset item per input row, in input order, with a `status` and a `message`.

### What you can do

| Operation | Input | Output per row |
|---|---|---|
| `geocode` | Addresses: one-line strings, structured records, or a CSV file | Matched address, coordinates, state/county/tract/block group codes, GEOIDs, demographics |
| `coordinates` | Latitude/longitude points | Tract/block group and demographics for the area containing each point |
| `demographics` | Tract (11-digit) or block group (12-digit) GEOIDs | Demographics only |

Typical uses: append neighborhood income and poverty data to a customer or donor list, check eligibility rules that are set by census tract,
site selection, grant applications, and research datasets.

### Input examples

**A few addresses:**

```json
{
  "operation": "geocode",
  "addresses": ["125 N Main St, Memphis, TN 38103", "4600 Silver Hill Rd, Washington, DC 20233"]
}
```

**A CSV file** (columns such as `address`, or `street`/`city`/`state`/`zip`, plus an optional `id`, are detected automatically):

```json
{
  "operation": "geocode",
  "csvUrl": "https://example.com/customers.csv",
  "csvColumns": {"street_column": "Addr 1", "zip_column": "Postal", "id_column": "Account"},
  "fields": ["income", "housing"]
}
```

**Structured records from an agent, block group level, with margins of error:**

```json
{
  "operation": "geocode",
  "addressRecords": [{"id": "c1", "street": "165 Capitol Ave", "city": "Hartford", "state": "CT", "zip": "06106"}],
  "geoLevel": "block_group",
  "includeMoe": true
}
```

**Coordinates:**

```json
{"operation": "coordinates", "points": [{"id": "store-12", "latitude": 35.1495, "longitude": -90.049}]}
```

**IDs only (cheaper, no demographics):** add `"demographics": false`.

### Output

One dataset item per input row. Example (demographic values shortened; real values come from the ACS release you choose):

```json
{
  "row": 1,
  "id": "c1",
  "input": "125 N Main St, Memphis, TN, 38103",
  "status": "matched",
  "message": null,
  "match_type": "Exact",
  "matched_address": "125 N MAIN ST, MEMPHIS, TN, 38103",
  "latitude": 35.148562911373,
  "longitude": -90.05155413782,
  "state_fips": "47",
  "county_fips": "157",
  "tract": "004200",
  "block_group": null,
  "tract_geoid": "47157004200",
  "block_group_geoid": null,
  "geo_name": "Census Tract 42; Shelby County; Tennessee",
  "total_population": 2154,
  "median_age": 34.1,
  "median_household_income": 51234,
  "poverty_rate": 21.7,
  "pct_bachelors_or_higher": 38.2,
  "median_home_value": 245000,
  "median_gross_rent": 1180,
  "unemployment_rate": 5.4,
  "demographics_notes": null
}
```

*(The demographic numbers above are illustrative, not real ACS values.)*

Rows that don't match still appear, so you can join results back to your list by `row` or `id`:

| `status` | Meaning | `message` tells you |
|---|---|---|
| `matched` | Found; IDs (and demographics) filled in | Nothing, or a note |
| `no_match` | The Census address database has no match | Check the house number and street; add city + state or ZIP |
| `tie` | Several addresses match equally | Add the ZIP or city and state |
| `invalid` | Not usable as given (empty, PO Box, no house number, over 100 characters) | What's wrong |
| `error` | The geocoder returned nothing for this row | Retry later |

Also saved:

- **`OUTPUT`** (key-value store): totals per status, match rate, ACS release used, warnings, and an error code if the run failed.
- **`results.csv`**: the same rows as a spreadsheet, demographics as columns.

#### Demographic fields

| Field | Unit | ACS variables |
|---|---|---|
| `total_population` | people | B01003_001 |
| `median_age` | years | B01002_001 |
| `median_household_income` | USD (inflation-adjusted to the end year) | B19013_001 |
| `per_capita_income` | USD | B19301_001 |
| `poverty_rate` | % of people below the poverty level | C17002_001–003 |
| `population_25_plus` | people | B15003_001 |
| `pct_high_school_or_higher` | % of people 25+ | B15003_017–025 |
| `pct_bachelors_or_higher` | % of people 25+ | B15003_022–025 |
| `housing_units` | units | B25001_001 |
| `vacancy_rate` | % of housing units | B25002_001, \_003 |
| `owner_occupied_rate`, `renter_occupied_rate` | % of occupied units | B25003_001–003 |
| `median_home_value` | USD | B25077_001 |
| `median_gross_rent` | USD per month | B25064_001 |
| `unemployment_rate` | % of civilian labor force | B23025_003, \_005 |

Use `fields` to pick groups (`population`, `income`, `education`, `housing`, `employment`) or individual field names.
`includeMoe` adds `*_moe` columns (90% margin of error) for the direct estimates.

When the Census publishes no estimate for an area (too few survey responses, or a tract with no residents such as a park or an airport),
the value is `null` and `demographics_notes` explains why. It never contains the Census placeholder codes like `-666666666`.

#### When a run fails

The run ends as **Failed**, the status message starts with an error code, and `OUTPUT.error` has details. You are not charged for failed runs.

| Code | Meaning |
|---|---|
| `NO_VALID_INPUT` | No usable rows (empty input, or all rows invalid) |
| `NO_MATCHES` | Nothing matched; check the per-row messages in the dataset |
| `COLUMN_NOT_FOUND` | The CSV has no address/street column, or a column you named doesn't exist. The message lists the available columns |
| `CSV_PARSE_ERROR` | The CSV couldn't be read |
| `ACS_YEAR_UNAVAILABLE` | That ACS release isn't published yet |
| `CENSUS_KEY_INVALID` | Only if you supplied your own `censusApiKey` and the Census rejected it |
| `UPSTREAM_RATE_LIMITED`, `UPSTREAM_UNAVAILABLE` | The Census service is busy or down. Rows already finished are kept |

### Pricing (pay per event)

| Event | Price |
|---|---|
| Location matched to a Census tract (primary event) | $0.001 ($1.00 per 1,000) |
| ACS demographics appended | $0.0006 ($0.60 per 1,000) |
| Actor start (set by Apify) | $0.00005 |

You pay only for **matched** rows. `no_match`, `tie` and `invalid` rows are free.
1,000 addresses with a 92% match rate and demographics cost **$1.47**. IDs only would cost **$0.92**.

The Actor never spends past your **maximum cost per run**. It processes rows in chunks of 1,000, sizes each chunk to the remaining budget,
and stops cleanly with a status message when the budget runs out.

### Limits and accuracy

- Up to **100,000 rows per run**. CSV files up to 50 MB. Each address part up to 100 characters (a Census Geocoder limit).
- US addresses only (50 states, DC, Puerto Rico and the Island Areas). The Census Geocoder matches by interpolating along address ranges.
  It's good at street addresses and weak at new construction, rural routes and PO Boxes. Typical match rates for clean lists are high, but check `match_rate` in `OUTPUT`.
- Speed: roughly 25 seconds per 1,000 addresses for the geocoding step (one measurement; the Census service speed varies). Block groups add a second pass.
- **Default data year:** ACS 5-year **2020–2024** (`acsYear: 2024`). Tract codes are matched to the same year's boundaries. Older releases back to 2017 are available.
- Tract estimates are more reliable than block group estimates. Block group values can have large margins of error, so use `includeMoe` when the numbers matter.
- These are **area estimates for the tract or block group**, not facts about any address or household.

### Use it from an AI agent (MCP)

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?actors=errandkit/census-tract-demographics" \
  --header "Authorization: Bearer <APIFY_TOKEN>"
```

Agent tips: pass small lists in `addresses` or `addressRecords`; read the dataset items; check `status` per row; read `OUTPUT.summary.match_rate`.

### Use it from the API

```bash
curl -X POST "https://api.apify.com/v2/acts/errandkit~census-tract-demographics/run-sync-get-dataset-items?token=<APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"operation":"geocode","addresses":["125 N Main St, Memphis, TN 38103"],"fields":["income"]}'
```

### Data sources, attribution and acceptable use

- U.S. Census Bureau Geocoder (geocoding.geo.census.gov) and Census Data API, ACS 5-year detailed tables (api.census.gov). Both are free public services.
- **This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.**
- Census terms prohibit using these data, alone or combined with other data, to identify any individual person, household or business. Don't use the output to do that.
- Your addresses are sent to the Census Geocoder to be matched. The Actor stores results only in your own Apify run storage and keeps nothing after the run.

### Support

Open an issue on the **Issues** tab with the run ID. We usually reply within 1–2 business days.

# Actor input Schema

## `operation` (type: `string`):

geocode = US addresses to tract/block group (+ demographics); coordinates = lat/lon points to tract; demographics = known tract/block group GEOIDs to demographics.

## `addresses` (type: `array`):

One-line US addresses, e.g. '125 N Main St, Memphis, TN 38103'. Include city + state or ZIP for best matches.

## `addressRecords` (type: `array`):

Structured addresses: \[{"id": "c1", "street": "125 N Main St", "city": "Memphis", "state": "TN", "zip": "38103"}]. `id` is echoed back. You can also give {"address": "one line"}.

## `csvUrl` (type: `string`):

A CSV with a header row. Columns are detected automatically (address, street, city, state, zip, id); override with CSV columns below. Max 50 MB, 100,000 rows.

## `csvText` (type: `string`):

Alternative to the file: paste CSV text with a header row.

## `csvColumns` (type: `object`):

Only if auto-detection picks the wrong columns, e.g. {"street_column": "Addr 1", "zip_column": "Postal", "id_column": "Account"}. Keys: id_column, address_column, street_column, city_column, state_column, zip_column.

## `points` (type: `array`):

\[{"id": "a", "latitude": 35.1495, "longitude": -90.049}]

## `geoids` (type: `array`):

11-digit tract GEOIDs (e.g. 47157004200) or 12-digit block group GEOIDs.

## `demographics` (type: `boolean`):

Add ACS 5-year demographics to each matched row. Turn off for tract/block group IDs only (cheaper).

## `geoLevel` (type: `string`):

tract (default; more reliable estimates) or block_group (smaller areas, larger margins of error).

## `fields` (type: `array`):

Groups: population, income, education, housing, employment; or field names like median_household_income. Empty = all.

## `acsYear` (type: `integer`):

2024 = the 2020-2024 release (latest as of Oct 2026). Minimum 2017.

## `includeMoe` (type: `boolean`):

Adds \*\_moe columns (90% confidence) for direct estimates.

## `outputFormat` (type: `string`):

csv saves results.csv to the key-value store (demographics flattened into columns).

## `censusApiKey` (type: `string`):

Optional. The Actor has its own key. Use yours if you prefer (free at api.census.gov/data/key_signup.html).

## Actor input object example

```json
{
  "operation": "geocode",
  "addresses": [
    "125 N Main St, Memphis, TN 38103",
    "4600 Silver Hill Rd, Washington, DC 20233"
  ],
  "demographics": true,
  "geoLevel": "tract",
  "acsYear": 2024,
  "includeMoe": false,
  "outputFormat": "csv"
}
```

# Actor output Schema

## `rows` (type: `string`):

No description

## `summary` (type: `string`):

No description

## `csv` (type: `string`):

No description

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "operation": "geocode",
    "addresses": [
        "125 N Main St, Memphis, TN 38103",
        "4600 Silver Hill Rd, Washington, DC 20233"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("errandkit/census-tract-demographics").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "operation": "geocode",
    "addresses": [
        "125 N Main St, Memphis, TN 38103",
        "4600 Silver Hill Rd, Washington, DC 20233",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("errandkit/census-tract-demographics").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "operation": "geocode",
  "addresses": [
    "125 N Main St, Memphis, TN 38103",
    "4600 Silver Hill Rd, Washington, DC 20233"
  ]
}' |
apify call errandkit/census-tract-demographics --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,errandkit/census-tract-demographics"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/1S9mLeisLu0LxnWbp/builds/fyqefgRw6pUGciTqu/openapi.json