Census data for the decisions that matter.
Public health vulnerability scores. Lending compliance checks. Demographic maps. AI-powered data retrieval. PrairieCloud gives you the variables, geographies, and boundaries to build what your industry needs — without a data engineering team.
Compute community vulnerability indices from tract-level data.
Social Vulnerability Index. Area Deprivation Index. EJScreen. CEJST. CDC's Social Determinants of Health. These tools drive resource allocation, emergency response planning, and health equity research — and they all require specific Census variables at the tract or block group level.
PrairieCloud's 392 variables cover the demographic, economic, housing, disability, and language data that feed into these indices. The variables you need for SVI, ADI, and EJScreen are in our catalog, at the geography levels where indices are actually computed. No more assembling data from multiple Census tables, downloading separate boundary files, or writing FIPS-code join logic.
What you can build:
- Social Vulnerability Index (SVI) scoring for every tract in a county
- Area Deprivation Index (ADI) computation at block group level
- EJScreen environmental justice screening
- CEJST climate and economic justice tool inputs
- Health equity dashboards with demographic overlays
Tier you need: Any tier can query a specific tract or block group directly. Pro ($49/mo) adds tract wildcard and boundary workflows; Business ($299/mo) adds block group wildcard and boundary workflows.
Get Started with Pro →import requests
import pandas as pd
API_KEY = "pck_live_your_key_here"
# Fetch SVI input variables for all tracts in Harris County, TX
response = requests.get(
"https://api.prairiecloud.io/v1/data",
params={
"variables": ",".join([
"pop_total",
"poverty_below",
"employ_unemployed",
"income_per_capita",
"edu_place_less_than_hs",
"health_ins_total_civilian",
"elderly_alone_65_plus_living_alone",
"child_pop_total_under_18",
"disability_total",
"vehicles_owner_no_vehicle",
"crowding_total_occupied",
"structure_type_mobile_home",
"language_api_limited_english"
]),
"geo": "tract:Harris County, Texas",
"vintage": 2024
},
headers={"X-API-Key": API_KEY}
)
data = response.json()["data"]
# Build a DataFrame — one row per tract, ready for SVI computation
rows = []
for geo_key, geo_data in data.items():
row = {"tract": geo_key, "name": geo_data["name"]}
for var_name, var_data in geo_data["variables"].items():
row[var_name] = var_data["estimate"]
row[f"{var_name}_moe"] = var_data["margin_of_error"]
rows.append(row)
df = pd.DataFrame(rows).set_index("tract")
print(f"Tracts loaded: {len(df)}")
print(f"Variables per tract: {len([c for c in df.columns if not c.endswith('_moe') and c != 'name'])}")CRA eligibility, Opportunity Zones, and QCT designation — in one API call.
Community Reinvestment Act compliance. Opportunity Zone qualification. Qualified Census Tract designation. CDFI target market analysis. New Markets Tax Credit eligibility. These regulatory and investment programs all share one thing: they require Census tract-level income and poverty data matched to specific geographic boundaries.
PrairieCloud gives you the exact variables these programs use — median household income, poverty rates, population counts — at the tract level, with GeoJSON boundaries included. No FFIEC data downloads. No FIPS-code matching against separate shapefiles. Query a tract, get the data and the boundary, check eligibility.
What you can build:
- CRA assessment area analysis with demographic overlays
- Opportunity Zone screening and monitoring dashboards
- QCT eligibility checks for LIHTC applications
- CDFI target market identification
- NMTC investment area qualification
Tier you need: Any tier can query specific tracts directly. Pro ($49/mo) adds tract boundaries and inline geometry for map-ready lending workflows.
Get Started with Pro →import requests
API_KEY = "pck_live_your_key_here"
# Get median household income + boundaries for tracts in a county
response = requests.get(
"https://api.prairiecloud.io/v1/data",
params={
"variables": "income_median_household,poverty_below,pop_total",
"geo": "tract:Dallas County, Texas",
"vintage": 2024,
"include_geometry": "true"
},
headers={"X-API-Key": API_KEY}
)
data = response.json()["data"]
# Each tract includes data + GeoJSON boundary
for geo_key, tract in data.items():
income = tract["variables"]["income_median_household"]["estimate"]
poverty = tract["variables"]["poverty_below"]["estimate"]
pop = tract["variables"]["pop_total"]["estimate"]
geometry = tract["geometry"] # GeoJSON MultiPolygon
# Check against area median income thresholds
# Feed geometry directly to your mapping layer
# No separate boundary file download neededDemographic maps and data tools — with one API call per layer.
Building a neighborhood explorer? A school district comparison tool? A community resource dashboard? An equity analysis platform? These applications need demographic data and map boundaries together — and traditionally that means downloading shapefiles, matching FIPS codes, wrangling data into GeoJSON, and rebuilding the pipeline every time Census releases new data.
PrairieCloud collapses that pipeline. One request returns demographic variables and GeoJSON boundaries in the same response. 35 vintages for time series. Seven geography levels for drill-down. Your mapping library gets a ready-to-render feature collection.
What you can build:
- Neighborhood demographic explorers
- School district and community comparison tools
- Equity and disparity analysis dashboards
- Population change visualizations (35 vintages)
- Interactive choropleth maps at any geography level
Tier you need: Pioneer ($19/mo) for county-level maps. Pro ($49/mo) for tract-level choropleths.
Get Your Free API Key →import requests
import json
API_KEY = "pck_live_your_key_here"
# Get income data + boundaries for all counties in Colorado
response = requests.get(
"https://api.prairiecloud.io/v1/data",
params={
"variables": "income_median_household",
"geo": "county:Colorado",
"vintage": 2024,
"include_geometry": "true"
},
headers={"X-API-Key": API_KEY}
)
data = response.json()["data"]
# Convert to GeoJSON FeatureCollection — ready for Mapbox, Leaflet, D3
features = []
for geo_key, county in data.items():
features.append({
"type": "Feature",
"geometry": county["geometry"],
"properties": {
"name": county["name"],
"median_income": county["variables"]["income_median_household"]["estimate"]
}
})
geojson = {"type": "FeatureCollection", "features": features}
# Write to file or serve directly to your frontend
with open("colorado_income.geojson", "w") as f:
json.dump(geojson, f)
# → 64 counties with income data + map-ready boundaries
# → Feed to map.addSource() in Mapbox GL JSimport requests
API_KEY = "pck_live_your_key_here"
# Track how Travis County income changed over a decade
response = requests.get(
"https://api.prairiecloud.io/v1/timeseries",
params={
"variable": "income_median_household",
"geo": "county:Travis County, Texas",
"start_vintage": 2014,
"end_vintage": 2024
},
headers={"X-API-Key": API_KEY}
)
# Returns income_median_household for each vintage, 2014–2024Give your AI agent access to U.S. Census data.
Building an AI application that needs to answer questions about demographics, income, housing, or population? Your LLM doesn't need to call a REST API and parse JSON — it can access Census data natively through our MCP server.
MCP (Model Context Protocol) lets AI models use tools directly. Our MCP server gives your agent the ability to search variables, resolve geographies, and retrieve demographic data as structured tool responses. The model asks for data, it gets data — no prompt engineering around API calls required.
What you can build:
- AI assistants that answer demographic questions
- Automated research agents that pull Census data for reports
- Location intelligence bots for real estate and lending
- Data analysis copilots that query and visualize Census data on demand
Tier you need: Pioneer ($19/mo) for MCP access with county-level data. Pro ($49/mo) for tract-level MCP queries.
MCP Setup Guide →# Configuring PrairieCloud MCP server for Claude Desktop
# Add to claude_desktop_config.json:
{
"mcpServers": {
"prairiecloud": {
"url": "https://mcp.prairiecloud.io",
"headers": {
"Authorization": "Bearer pck_live_your_key_here"
}
}
}
}
# 8 tools available: get_census_data, compare_geographies,
# get_timeseries, search_variables, search_geographies,
# list_topics, list_datasets, geocodeUser: What's the poverty rate in Harris County, Texas?
Agent: [calls get_census_data tool]
→ variables: poverty_below, pop_total
→ geography: Harris County, Texas
→ vintage: 2024
Agent: Based on the 2024 ACS 5-Year estimates, approximately 16.2% of
Harris County's population of 4.7 million lives below the poverty line,
with a margin of error of ±0.4 percentage points.Built for anyone who works with Census data.
The examples above are where PrairieCloud's data product is most differentiated. But the clean API design serves every Census data workflow.
Backend Developers
Embed demographic data in your application with a single REST call. Human-readable variable names, structured JSON responses, header-based auth. No FIPS codes, no array-of-arrays parsing.
Quickstart guide →Data Analysts & Scientists
Census data in pandas without the wrangling. One API call returns a clean payload ready for DataFrame conversion. 392 named variables, margins of error included, 35 vintages for longitudinal analysis.
Python examples →Academic Researchers
Reproducible Census data access for research. Pin your queries to a specific vintage. Get consistent results across runs. Export to CSV for Stata, R, or Excel workflows. The free tier covers most exploratory research needs.
Read the docs →Start building. Free tier. No credit card.
Get Your Free API Key1,000 requests per month with direct lookups at any geography level. Upgrade for wildcard expansion, boundary access, and higher limits.