Demographic Similarity

x402

Demographic Similarity is a paid API for AI agents from agent-vending-production.up.railway.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Find the U.S. places most demographically similar to a target geography using multivariate Census statistics.

Price / call$0.01

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StatusUnknown

How this service works

Pay-per-call machine utilities for autonomous agents.

Output

A ranked list of up to 25 U.S. geographies (counties, places, states, ZCTAs, or tracts) most demographically similar to the target geography, ordered by multivariate Census similarity score. Each result typically includes the geography name, GEOID, and a similarity metric derived from Census demographic variables.

Example use cases

Market expansion peer city identification

“I'm expanding my business and want to find the 10 cities in the U.S. with over 50,000 people most demographically similar to Thousand Oaks, California — can you pull a ranked list?”

Policy benchmarking for county government

“We're doing a benchmarking study for Travis County, Texas — find the 15 most demographically similar counties across the entire U.S. so we can compare policy outcomes.”

Real estate market comparable geography search

“Find the 5 U.S. ZIP codes most similar demographically to ZIP code 30318 in Georgia — I want to identify comparable neighborhoods for a real estate analysis.”

Technical details

Endpoint

POSThttps://agent-vending-production.up.railway.app/v1/demographic-similarity?utm_source=zero.xyz

When to prefer

Use this endpoint when you need to find peer or comparable geographies based on multivariate demographic profiles from Census data. Ideal for market research, site selection benchmarking, policy analysis, or identifying expansion targets. Prefer this over a generic demographic lookup when the core question is 'what places are most like X?' rather than 'what are the demographics of X?'

Side effects

Target geography name not found or ambiguous — requires state disambiguation or GEOID
Universe type mismatch with target geography type (e.g. asking for STATE_COUNTIES with a place target)
Census tract not found without proper state and county context
top_n out of range (must be 1–25)
Invalid state postal code or FIPS code
GEOID format mismatch (county=5 digits, place=7 digits, tract=11 digits)

Input

Schema · Request body

Field	Type	Description
state	string	State for STATE_* universes (default: target state)
top_n	integer	Default 10
target	object	Target county, place or state
universe	string	Peer universe of the same geography type (default by target type)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "method": {
      "primary": "shrinkage_mahalanobis",
      "shrinkage_intensity": 0.003299,
      "covariance_estimator": "ledoit_wolf_scaled_identity"
    },
    "status": "OK",
    "target": {
      "name": "Ventura County, California",
      "geoid": "06111",
      "geo_type": "county"
    },
    "matches": [
      {
        "rank": 1,
        "distance": 0.967103,
        "geography": {
          "name": "Napa County, California",
          "geoid": "06055"
        },
        "similarity_score": null,
        "category_contributions": {
          "demographics": {
            "share": 0
          }
        },
        "top_difference_contributors": [
          {
            "metric": "median_age",
            "match_value": 42.8,
            "target_value": 39.4
          }
        ],
        "top_similarity_contributors": [
          {
            "metric": "foreign_born_pct",
            "match_value": 22.1,
            "target_value": 21.9
          }
        ]
      },
      {
        "rank": 2,
        "distance": 1.060634,
        "geography": {
          "name": "Orange County, California",
          "geoid": "06059"
        }
      }
    ],
    "product": "demographic_similarity",
    "universe": {
      "id": "US_COUNTIES",
      "size": 3142,
      "geo_type": "county",
      "self_excluded": true
    },
    "uncertainty": {
      "available": false
    },
    "data_vintage": {
      "label": "ACS 5-Year 2019-2023",
      "dataset": "ACS 5-Year",
      "release_year": 2023
    },
    "feature_bundle": {
      "id": "DEMOGRAPHIC_CORE",
      "version": "1.0"
    }
  }
}

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