# Map AI Documentation

> **Coverage:** United States only.
> **Formats:** JSON via REST API and MCP (Model Context Protocol).

---

## Table of Contents

1. [Available Data](#available-data)
2. [H3 Grid Explained](#h3-grid-explained)
3. [Geographic Cell Types](#geographic-cell-types)
4. [MCP Server](#mcp-server)
5. [REST API](#rest-api)
6. [Explore Pages — Free US Demographic Data](#explore-pages--free-us-demographic-data)
7. [Subscriptions & Regional Pricing](#subscriptions--regional-pricing)
8. [Reports Catalogue](#reports-catalogue)
9. [Sales Forecast Tool](#sales-forecast-tool)

---

## Available Data

All data is keyed by an `apiName` string (the enum constant name shown in `code` below).
Units and example values are provided for each field.

### Geography & Area

| API Name | Units | Description | Example |
|---|---|---|---|
| `NAME` | — | Human-readable name of the geographic cell | `Alabama / Todd County` |
| `AREA` | km² / mi² | Area of the cell in both km² and mi² | `42 km²` |
| `LAND_AREA_FRACTION` | fraction | Fraction of the total area that is land (rest is water) | `0.8` → 80% land |

---

### Population

**Source:** US Census Bureau — ACS 5-Year Estimates

| API Name | Units | Description | Example |
|---|---|---|---|
| `POPULATION` | people | Total resident population | `4 200` |
| `POPULATION_DENSITY_MILES` | people/mi² | Population density per square mile | `300 people/mi²` |
| `POPULATION_DENSITY_KM` | people/km² | Population density per square kilometer | `116 people/km²` |
| `HOUSEHOLDS` | households | Total number of households | `250` |
| `POPULATION_BY_YEARS` | people | Total population trend by year | `[{year: 2017, population: 500}, ...]` |
| `AGE_SEX` | people | Population by age brackets and sex | `[{year: 2023, sex: 'F', age_from: 25, age_to: 27, population: 42}, ...]` |
| `POPULATION_BY_RACE` | population | Population by race/ethnicity (Census classification) | `[{year: 2017, race: black, population: 42}, ...]` |
| `MEDIAN_AGE` | years | Median age of residents | `38.5` |
| `MEDIAN_AGE_BY_YEARS` | years | Median age trend by year | `{2009: 36.0, 2020: 38.5}` |
| `AVERAGE_HOUSEHOLD_SIZE` | people | Average number of people per housing unit | `2.4` |
| `POPULATION_GROWTH_TREND` | people | Population growth trend | — |
| `MARITAL_STATUS` | population | Population by marital status and sex. Values: `NOW_MARRIED`, `NEVER_MARRIED`, `SEPARATED`, `DIVORCED`, `WIDOWED` | `[{SEX: M, YEAR: 2022, POPULATION: 150, MARITAL_STATUS: NOW_MARRIED}, ...]` |

---

### Income & Wealth

**Source:** US Census Bureau — ACS 5-Year Estimates; Bureau of Labor Statistics (BLS)

| API Name | Units | Description | Example |
|---|---|---|---|
| `MEDIAN_INCOME` | $ | Median yearly household income | `$96k` for California |
| `MEDIAN_INCOMES_BY_YEARS` | $ | Both personal and household median incomes by year | `{2016: {household: 42000, personal: 35000}}` |
| `MEDIAN_HOUSEHOLD_INCOME_TREND` | % | Median household income yearly growth rate | `2.5%` per year; `-5%` = decline |
| `INCOME_DISTRIBUTION` | population | Detailed personal income distribution by income range, work type, and sex | `{SEX: "F", WORK: "FULL_TIME", YEAR: 2024, INCOME_FROM: 12500, INCOME_TO: 14999, POPULATION: 390}` |
| `HOUSEHOLDS_INCOME` | households | Household income distribution by income ranges and years | `{YEAR: 2009, HINCOMEFROM: 20000, HINCOMETO: 24999, HOUSEHOLDS: 3463}` |
| `HOUSEHOLD_INCOME_MEDIAN_BY_YEARS` | $ | Median household income by year — compact time-series format | `{2021: 50000, 2022: 51000, 2023: 52000}` |
| `AVERAGE_SPENDING_BY_CATEGORIES` | $ | Estimated average household spending per year by goods & services category | `Food: $7 000, Gasoline: $1 000` |
| `AVERAGE_ANNUAL_SPENDING` | $ | Estimated average total household spending per year | `$72 000` |
| `WEALTH_INDEX` | index | Map AI custom coefficient of area wealth | `1.1` |
| `GINI_INDEX` | index | Income inequality (Gini coefficient). 0 = perfect equality, 1 = extreme inequality | `0.4` |
| `GENDER_PAY_GAP` | coefficient | `[(Male median − Female median) / Male median] × 100`. Positive = men earn more | `0.2` → men earn 20% more |
| `MEDIAN_INCOME_BY_SEX_AND_YEARS` | $ | Median income for males and females by year | — |

---

### Education

**Source:** US Census Bureau — ACS 5-Year Estimates

| API Name | Units | Description | Example |
|---|---|---|---|
| `EDUCATION_ATTAINMENT` | population | Population by educational attainment level and year | `{YEAR: 2024, EDUCATION: "BACHELOR", POPULATION: 92}` |
| `BACHELORS_DEGREE_OR_HIGHER_FRACTION` | fraction | Share of adults with a bachelor's degree or higher | `0.4` → 40% of adults |

---

### Employment & Commute

**Source:** US Census Bureau — ACS 5-Year Estimates; BLS

| API Name | Units | Description | Example |
|---|---|---|---|
| `EMPLOYMENT` | population | Employment distribution by status, labor type, age, and year | `[{YEAR: 2022, LABOR: "CIVILIAN", EMPLOYED: false, POPULATION: 3484}, ...]` |
| `UNEMPLOYMENT_RATIO` | fraction | Share of working-age population not employed | `0.043` → 4.3% |
| `UNEMPLOYMENT_RATE_BY_YEARS` | fraction | Unemployment rate trend by year | `{2016: 0.042, 2017: 0.038}` |
| `WORK_AT_HOME` | fraction | Share of employed people working from home | `0.12` → 12% |
| `AVERAGE_TRAVEL_TIME_TO_WORK` | minutes | Average commute time | `28 min` |
| `TRANSPORTATION_TO_WORK` | workers | Workers by commute mode: `CAR`, `WALKED`, `BICYCLE`, `TAXICAB`, `MOTORCYCLE`, `OTHER`, `WORKED_AT_HOME`, `PUBLIC` | `[{YEAR: 2022, WORKERS: 42, TRAVEL_TYPE: CAR}, ...]` |
| `TRAVEL_TIME_TO_WORK` | workers | Distribution of workers by commute duration in minutes | `[{YEAR: 2022, WORKERS: 300, MINUTES_FROM: 60, MINUTES_TO: 89}]` |
| `TIME_TO_DEPARTURE_TO_WORK` | workers | Distribution of workers by departure time (o'clock) | `{YEAR: 2020, WORKERS: 56, OCLOCK_FROM: 0, OCLOCK_TO: 299}` |

---

### Housing

**Source:** US Census Bureau — ACS 5-Year Estimates; MLS / public real-estate records

| API Name | Units | Description | Example |
|---|---|---|---|
| `HOME_OWNERSHIP` | housing units | Units by year, occupancy status (occupied/vacant), and owner type (renter/owner) | `[{YEAR: 2023, OCCUPANCY: true, OCCUPIEDBY: RENTER, HOUSING_UNITS: 500}, ...]` |
| `HOME_OWNERSHIP_RATIO` | fraction | Share of housing units that are owner-occupied | `0.65` → 65% |
| `MEDIAN_TENURE` | years | Median years current residents have lived in their unit | — |
| `HOUSING_SALE_COST` | housing units | Distribution of units by sale price range and year | `[{YEAR: 2022, COSTFROM: 1500000, COSTTO: 1999999, HOUSING_UNITS: 18781}, ...]` |
| `MEDIAN_HOUSING_SALE_COST` | $ | Median housing sale price | `$250 000` |
| `HOUSING_SALE_COST_MEDIANS_BY_YEARS` | $ | Median housing sale price trend by year | `[{year: 2017, median_sale_cost: 210000}, ...]` |
| `MEDIAN_HOUSING_SALE_COST_TREND` | % | Average yearly housing price growth rate | `5%` |
| `HOUSING_AGGREGATED_PRICE` | $ | Sum of estimated market values of all owner-occupied units by year | `{2021: 4 598 946 831}` |
| `HOUSING_ROOMS` | housing units | Distribution of units by number of rooms | `V_HOUSING_UNITS__ROOMS_4: 2870` |
| `HOUSING_GROSS_RENT` | housing units | Rent price distribution by range and year | `[{YEAR: 2018, RENTCOSTFROM: 250, RENTCOSTTO: 299, HOUSING_UNITS: 41}, ...]` |
| `MEDIAN_GROSS_RENT` | $ | Median gross rent | `$1 000` |
| `HOUSING_GROSS_RENT_MEDIANS_BY_YEARS` | $ | Median gross rent trend by year | `[{year: 2024, median_gross_rent: 1000}, ...]` |
| `MEDIAN_GROSS_RENT_TREND` | % | Median gross rent yearly growth rate | — |
| `HOME_PRICE_TO_INCOME_RATIO` | ratio | Median housing sale price divided by median household income | `7.5` |
| `MEDIAN_NUMBER_OF_ROOMS` | rooms | Median number of rooms per housing unit | — |
| `STRUCTURE_BUILT_MEDIAN_YEAR` | year | Median year housing structures were built | `{2009: 1977, 2013: 1982}` |
| `HOUSEHOLDER_MOVED_INTO_UNIT_MEDIAN_YEAR` | year | Median year current householders moved into their unit | `{2009: 1977, 2013: 1982}` |
| `NUMBER_OF_VEHICLES_AVAILABLE` | vehicles | Vehicles available in households | `V_HOUSING_UNITS__OCCUPIEDBY_RENTER: 2870` |
| `HOUSEHOLDS_BY_VEHICLES` | households | Households grouped by number of vehicles owned | `V_HOUSEHOLDS__VEHICLES_COUNT_1: 278` |

---

### Health & Healthcare

**Source:** CDC, County Health Rankings & Roadmaps, HRSA

| API Name | Units | Description | Example |
|---|---|---|---|
| `LIFE_EXPECTANCY` | years | Estimated life expectancy | `80 years` |
| `SMOKERS_FRACTION` | fraction | Share of adult smokers | `0.2` → 20% |
| `DRINKERS_FRACTION` | fraction | Share of binge drinkers | `0.2` → 20% |
| `SEDENTARY_FRACTION` | fraction | Share of physically inactive adults | `0.2` → 20% |
| `OBESITY_FRACTION` | fraction | Share of obese adults | — |
| `DIABETES_FRACTION` | fraction | Share of adults with diabetes | `0.1` → 10% |
| `CANCER_FRACTION` | fraction | Share of adults with cancer (excluding skin cancer) | `0.1` → 10% |
| `HEART_DISEASES_FRACTION` | fraction | Share of adults with heart disease | `0.1` → 10% |
| `BAD_HEALTH_SELF_RATED_FRACTION` | fraction | Share of adults rating their health as average or poor | `0.3` → 30% |
| `PRIMARY_CARE_PHYSICIANS` | physicians | Number of primary care physicians (PCPs) in patient care | `42` |
| `PRIMARY_CARE_PHYSICIANS_RATIO` | people per physician | Population per PCP — lower is better | `1 500` |
| `DENTISTS` | dentists | Number of dentists | `42` |
| `DENTISTS_RATIO` | people per dentist | Population per dentist | — |
| `MENTAL_HEALTH_PROVIDERS` | providers | Number of mental health providers (MHPs) | `42` |
| `MENTAL_HEALTH_PROVIDERS_RATIO` | people per provider | Population per MHP | — |
| `HEALTH_INDEX` | index | Map AI composite health index for the area | `1.2` |
| `UNHEALTHY_BEHAVIOUR_INDEX` | index | Map AI index summarizing unhealthy behaviors | `0.2` |
| `SPECIAL_HEALTH_CARE_NEEDS_INDEX` | index | Map AI index for special healthcare needs | `0.2` |

---

### Insurance

**Source:** US Census Bureau — ACS 5-Year Estimates

| API Name | Units | Description | Example |
|---|---|---|---|
| `INSURANCE` | population | Health insurance coverage by age, sex, and year (insured/uninsured) | `[{SEX: "M", YEAR: 2017, AGE_FROM: 45, AGE_TO: 54, POPULATION: 671, HEALTHINSURANCE: false}, ...]` |
| `INSURED_RATIO` | fraction | Share of population with health insurance | — |
| `UNINSURED_RATIO` | fraction | Share of population without health insurance | — |
| `INSURANCE_FRACTION_BY_YEARS` | fraction | Insurance coverage rate trend by year | `{2016: 0.42}` |

---

### Crime & Safety

**Source:** FBI Uniform Crime Reporting (UCR) / NIBRS; local law enforcement

> **Note:** Crime data is available at the **county level** and above. It is not available for H3 hex cells or census tracts.

| API Name | Units | Description | Example |
|---|---|---|---|
| `CRIME_RATE` | offenses per 100k | Overall crime rate | `12.1` |
| `CRIMES_FOR_CRIME_TYPE` | crimes | Crime counts by specific crime type and year | `{year: 2020, MURDERS: 42, KIDNAPPING: 50, RAPE: 10}` |
| `CRIME_RATE_FOR_VICTIM_TYPE` | offenses per 100k | Crime rate broken down by victim type | — |
| `INDIVIDUAL_CRIMES` | crimes | Crimes targeting individuals | — |
| `BUSINESS_CRIMES` | crimes | Crimes targeting businesses | — |
| `VIOLENT_CRIME_RATE` | rate | Violent crime rate | — |
| `PROPERTY_CRIME_RATE` | rate | Property crime rate | — |

---

### Environment & Air Quality

**Source:** EPA — AQS (Air Quality System), satellite PM2.5 data

| API Name | Units | Description | Example |
|---|---|---|---|
| `AIR_POLLUTION` | µg/m³ | Median daily fine particulate matter (PM2.5) | `12.3 µg/m³` |
| `AIR_POLLUTION_DISTRIBUTION` | days by µg/m³ | Distribution of days within a year by air quality level | — |
| `AIR_POLLUTION_DAILY` | µg/m³ | Daily PM2.5 for every day of the year (366 values) | `[1.7, 2.3, 1.5, ...]` |
| `AIR_POLLUTION_INDEX` | custom index | Air pollution adjusted for traffic intensity — useful for cross-area comparison | `12.5` in Manhattan |

---

### Infrastructure & Traffic

**Source:** OpenStreetMap (OSM); Ticon proprietary traffic data (AADT)

| API Name | Units | Description | Example |
|---|---|---|---|
| `ROAD_LENGTH` | meters | Road length by OSM road class (trunk, primary, secondary, etc.) including lane-weighted sums. Useful for infrastructure density analysis. | `{trunk: 4200, primary: 10000, secondary: 5000}` |
| `TRAFFIC_INTENSITY` | VPD (vehicles/day) | Average Annual Daily Traffic (AADT) per road class | `highway: 4 200 vehicles/day` |
| `CUMULATIVE_TRAFFIC_INTENSITY` | VMT (Vehicle Miles Traveled) | Total traffic considering both vehicle count and road length | `4 200 000` |
| `POINTS_OF_INTEREST` | count by OSM tag | Points of interest (POIs) by category from OpenStreetMap | `hospitals: 42, cafes: 100` |

---

### Composite Indices (Map AI)

Map AI proprietary computed indices — useful for quick area comparison without querying raw fields.

| API Name | Units | Description |
|---|---|---|
| `WEALTH_INDEX` | index | How wealthy the area is relative to national average |
| `HEALTH_INDEX` | index | Overall area health composite |
| `WISE_INDEX` | index | Education/knowledge composite |
| `HWW_INDEX` | index | Combined Health + Wealth + Wise (Franklin Index) |
| `UNHEALTHY_BEHAVIOUR_INDEX` | index | Composite of unhealthy lifestyle behaviors |
| `SPECIAL_HEALTH_CARE_NEEDS_INDEX` | index | Composite of special healthcare needs prevalence |
| `AIR_POLLUTION_INDEX` | custom | Air quality adjusted by traffic — best for relative comparison |

---

### Politics & Elections

**Source:** MIT Election Data and Science Lab; official county election results

| API Name | Units | Description | Example |
|---|---|---|---|
| `PRESIDENT_ELECTION_BY_YEARS_COUNTY_BASED` | election results | Presidential election results by candidate and year | `[{2016: {BIDEN: 42, TRUMP: 42}}, ...]` |
| `BIDEN_VS_TRUMP_2020_POPULATION` | population | 2020 population weighted by vote. Positive = Trump, Negative = Biden | `−100 500` |
| `HARRIS_VS_TRUMP_2024_POPULATION` | population | 2024 population weighted by vote (extrapolated). Positive = Trump, Negative = Harris | `42 000` |
| `HOUSE_OF_REPRESENTATIVES_ELECTION` | votes | House of Representatives election results | `{DEMOCRAT: 42, REPUBLICAN: 42}` |
| `SENATE_ELECTION` | votes | Senate election results | `{DEMOCRAT: 42, REPUBLICAN: 42}` |

---

## H3 Grid Explained

Map AI uses [Uber's H3 hexagonal grid system](https://h3geo.org/) to partition the US into uniform geographic cells.

### Why Hexagons?

Hexagons tile a surface without overlap or gaps. Every cell has the same shape and equal-distance neighbors, which makes spatial comparisons consistent and fair — unlike counties or states which vary wildly in size and shape.

### H3 Resolution 7 — the "1-Mile Cell"

The most detailed resolution publicly available in Map AI is **H3 Resolution 7**:

- **Cell diameter:** ~1 mile (approx. 1.2 km across)
- **Cell area:** ~5.16 km² (~2 mi²)
- **US coverage:** ~200 000+ cells cover the contiguous United States
- **Use case:** Neighborhood-level analysis. Fine enough to distinguish different blocks of a city, yet coarse enough to have statistically meaningful census data aggregated to it.

### Other Available Resolutions

| Resolution | Nickname | Approximate Diameter |
|---|---|---|
| H3 Res 7 | 1-mile cell | ~1 mile — most detailed |
| H3 Res 5 | 5-mile cell | ~5 miles |
| H3 Res 4 | — | ~20 miles |

At smaller radii (< ~0.5 miles) the API will return a warning that results may be statistically sparse.

---

## Geographic Cell Types

Map AI stores and serves data for multiple geographic cell types. All data described above is available for each of the following, unless noted otherwise.

| Cell Type | Description | Notes |
|---|---|---|
| **H3 Res 7** | ~1-mile hexagonal cell | Most detailed publicly available level |
| **H3 Res 5** | ~5-mile hexagonal cell | — |
| **H3 Res 4** | ~20-mile hexagonal cell | — |
| **County** | US Counties (~3 100 total) | FIPS codes used (e.g., `12086` for Miami-Dade) |
| **State** | US States + DC | FIPS codes or 2-letter abbreviation (e.g., `FL`) |
| **Country** | United States as a whole | — |
| **Census Tract** | Sub-county census unit | **Limited data availability.** Used internally; not exposed on the public website. |
| **Census Block Group** | Sub-tract census unit | **Limited data availability.** Used internally; not exposed on the public website. |

> **Important:** Census tract and census block group data are available in the database for internal processing but are **not exposed on the public website**. Coverage and feature completeness at these levels is limited compared to H3 or administrative divisions.

---

## MCP Server

Map AI exposes a public **Model Context Protocol (MCP)** server that lets any AI agent or LLM client interactively query US geospatial data.

**Endpoint (temporarily public):** `https://interactive-map-ai.com/mcp`
**Protocol:** Streamable HTTP (MCP spec)
**Authentication:** None required (public)

### What the MCP Server Can Do

The MCP server exposes three tools:

#### 1. `getAvailableFeatures` — List Available Data

Returns the full catalogue of data features available at Map AI: `apiName`, units, and a human-readable description for each.

Use this first to discover what data you can query.

**Example question to an AI agent:**
> "What demographic data does Map AI have?"

---

#### 2. `getDataAroundLocation` — Query by Coordinates + Radius

Fetches data for the geographic area around a given point.

**Parameters:**

| Parameter | Type | Description |
|---|---|---|
| `apiName` | list of feature names | Which features to return (from `getAvailableFeatures`) |
| `latitude` | number | Latitude of the center point |
| `longitude` | number | Longitude of the center point |
| `radiusMiles` | number | Search radius in miles (minimum ~0.03 mi) |

**Returns:** geometry (WKT), the requested data fields, and any contextual warnings (e.g., if the area is partially outside US coverage or if crime data is unavailable at that resolution).

**Example question to an AI agent:**
> "What is the median household income and crime rate within 5 miles of downtown Austin, TX?"

---

#### 3. `getDataForAdministrativeRegion` — Query by State / County / Country

Fetches data for a named administrative region without needing coordinates.

**Parameters:**

| Parameter | Type | Description |
|---|---|---|
| `apiName` | list of feature names | Which features to return |
| `administrativeDivision` | enum | `COUNTRY_US_ONLY`, `STATE`, or `COUNTY` |
| `fipsCode` | string | FIPS code or state abbreviation. E.g., `FL` for Florida, `12086` for Miami-Dade County |

**Example question to an AI agent:**
> "Compare life expectancy and obesity rates in Florida vs. Texas."

---

### Connecting to the MCP Server

Any MCP-compatible client (Claude Desktop, custom agent, etc.) can connect using the streamable HTTP transport:

```json
{
  "mcpServers": {
    "map-ai": {
      "url": "https://interactive-map-ai.com/mcp"
    }
  }
}
```

---

## REST API

Map AI also provides a conventional **JSON REST API** for programmatic access to all the same data.

- All responses are **JSON-structured**.
- Feature values are keyed by `apiName` strings (matching the enum names listed above).
- Geographic queries accept coordinates + radius, or administrative FIPS codes.
- The API supports the same set of features as the MCP server.

Contact the Map AI team for REST API credentials, endpoint documentation, and rate limits.

---

## Explore Pages — Free US Demographic Data

**Entry point:** https://interactive-map-ai.com/explore/us/overview

A free, no-sign-up, server-rendered cluster of data pages — no map, no
account, no interactive/AI features — one page per US **county**, **city
(place)**, **state**, and the **country as a whole**, for each of 6 topics.
This is the same underlying data as the Interactive Map and MCP server,
published as static-feeling pages for direct reading/citation and search
discovery. It complements (does not replace) the interactive tools above.

### URL pattern

| Level | Topic URL |
|---|---|
| County or city | `/explore/us/{state}/{county-or-city-slug}/{topic}` |
| State | `/explore/us/{state}/{topic}` |
| Country (US) | `/explore/us/{topic}` |

`{state}` is the 2-letter state code. `{topic}` is one of:

| Topic | Path segment | Covers |
|---|---|---|
| Overview | `overview` (default; omit for the bare area URL, e.g. `/explore/us/fl/miami-dade`) | General demographic summary — population, income, key facts |
| Wealth | `wealth` | Income, spending, the Wealth Index |
| Health | `health` | Life expectancy, chronic disease rates, provider access, the Health Index |
| Housing | `housing` | Home prices, rent, ownership rate, home price-to-income ratio |
| Commute | `commute` | Commute time, transportation mode, departure time |
| Points of Interest | `points-of-interest` | Nearby amenities by category (schools, healthcare, retail, etc.) |

Every page shows a table of the area's own numbers next to its state/US
average, a short narrative explaining what the numbers mean, an FAQ (also
emitted as `FAQPage` JSON-LD), and a colored map of siblings (counties
within the state, states within the US) for the same metric. County and
state pages also show a compact "highest/lowest" ranking among their
siblings for select metrics.

### Business site-selection calculator (free, revenue-free)

Each area also has a free "Open a Restaurant" calculator, same URL shape
with `open-a-restaurant` as the topic:
`/explore/us/{state}/{county-or-city-slug}/open-a-restaurant` (plus state-
and US-level equivalents). It estimates demand and relative market
saturation for opening a restaurant in that area — deliberately without a
revenue figure — and funnels toward the paid, address-level [Sales Forecast
Tool](#sales-forecast-tool) for a specific site. Restaurant is the only
live industry today; more (coffee shop, hair salon, convenience store) are
planned, matching the Sales Forecast Tool's own industry roadmap.

### Notes

- This cluster is served by the backend (Thymeleaf/SSR), not the Angular
  single-page app the rest of this document describes — it has no client-
  side interactivity beyond the choropleth map hover states.
- All content is free and ungated; there is no paid tier of the Explore
  pages themselves. They exist to drive discovery/SEO and funnel toward the
  paid subscriptions and reports described below.

---

## Subscriptions & Regional Pricing

**Page:** https://interactive-map-ai.com/pricing#subscriptions

Recurring access to the live map/data. Every plan is priced by **coverage
area** — one county, one state, or the whole US — billed monthly; a visitor
picks the region they need rather than paying a flat nationwide rate.

| Plan | Audience | County | State | Full US | Notes |
|---|---|---|---|---|---|
| **Neighborhood Search** | Homebuyers — people choosing where to live | $24 | $49 | $79 | 2 neighborhood reports, 1-mile search radius, neighborhood comparison, Map AI chat |
| **Real Estate Suite** | Realtors — closing deals faster | $29 | $49 | $79 | — |
| **Site Selection Pack** | Businesses choosing where to operate | $79 | $149 | $249 | Also unlocks the deeper steps of the Sales Forecast tool (see below) |
| **Academia** | Analysts and data-driven teams | Contact us | Contact us | Contact us | Display-only; not self-service purchasable — full data access |

Legacy/internal plan ids still present in the codebase (not shown on
`/pricing`): `BASIC`, `PLUS`, `PRO`, `KNOW_YOUR_COUNTRY`, `COMMUNITY_COMPASS`,
`REALTOR` (deprecated, superseded by Real Estate Suite), `ENTERPRISE`,
`FULL_ACCESS`.

---

## Reports Catalogue

**Order flow:** https://interactive-map-ai.com/reports/order (3 steps: type → location → review; anonymous visitors are asked to sign up at the last step before paying)
**My reports:** https://interactive-map-ai.com/reports/my-reports

One-time reports for a specific address or area, grouped by who buys them
(a report can appear in more than one group):

| Report | Price | Delivery | For | What it covers |
|---|---|---|---|---|
| **Community Compass** | $9.95 | ~3 minutes, instant | Homebuyers / Realtors | Neighborhood summary, real estate, demographics, health, schools, crime, entertainment & convenience, commute & mobility. Interactive report + PDF. |
| **Open House Flyer** | $4.99 | 1 business day, analyst-prepared | Realtors | Location overview, demographics, amenities, schools, real estate — a concise 2-page PDF flyer for a listing. |
| **Comparative Location Analysis** | $499 | 1 business day, analyst-prepared | Business / Realtors | Compares 2 potential locations: population, income, trends, environment, competition. |
| **Business Site Traffic** | $1,200 | 3-5 working days, analyst-prepared | Business (retail site selection) | Traffic measured at the exact street address (not a ZIP code or nearby-road estimate) — AADT, intra-day distribution, daily/monthly/yearly averages, speed & driver behavior, congestion/rush-hour, demographics. Continuous 24/7/365 observation, current within a week. |
| **Bankable Feasibility Study** | $1,500 | 2 weeks, analyst-prepared with direct analyst support | Business owners, lenders, investors | Metrics-based location assessment, estimated market demand, 5-year sales projection by product category, traffic analysis for all road directions, local customer profile, competitive landscape. Lender-ready PDF. Also orderable directly from the Sales Forecast industry pages (see below) — same product, different entry point. |

---

## Sales Forecast Tool

Web tool for a single question: **"Is this a good location for my
business?"** A visitor enters a specific US address and gets an instant,
free trade-area analysis for their industry — no sign-up required — with
two independent upsells: a Map AI subscription (deeper funnel steps) and a
one-time, human-delivered Bankable Feasibility Study ($1,500, see above).

- **Hub (all industries):** https://interactive-map-ai.com/sales-forecast
- **Beauty Salon:** https://interactive-map-ai.com/sales-forecast/beauty-salon — 10 min drive-time trade area (15 min rural)
- **Restaurant:** https://interactive-map-ai.com/sales-forecast/restaurant — 5 min drive-time trade area (10 min rural)
- **Convenience Store:** https://interactive-map-ai.com/sales-forecast/convenience-store — 2 min drive-time trade area (7 min rural)
- **My Business:** https://interactive-map-ai.com/sales-forecast/my-business — catch-all for any business type not listed above; same free check, with fully configurable audience/market-share/revenue assumptions instead of an industry preset

The trade area is a real drive-time isochrone (Mapbox, live + historical
traffic, wider automatically for less dense/rural addresses), not a
straight-line radius — a fixed-radius circle is only used as a fallback
when isochrone data is unavailable for an address.

**Free, no account required** — the teaser card row and an express
revenue what-if:
- **Residents** — population within the trade area (`POPULATION`).
- **Visitors** — estimated people/day driving into the trade area on
  purpose (destination traffic, via a Ticon/TrafficZoom cordon count),
  folded into the same customer pool as residents.
- **Traffic** — busiest road, vehicles/day (`TRAFFIC_INTENSITY`).
- **Local Buying Power** — median household income (`MEDIAN_INCOME`).
- A **customer-profile calculator** (audience by age/sex, simple presets or
  a fine-tune editor) and an **express revenue estimate** (audience ×
  a modest all-in market-share assumption × visits/year × average bill).

**Unlocked with a Site Selection Pack subscription** (see Subscriptions
above): Competition Nearby & Market Saturation (named competitors from
Mapbox, population per competitor), a serviceable-market step (target
customers × an editable category-penetration rate), competitor analysis
(pool split evenly among competitors), a detailed/adjusted revenue
estimate fed by that winnable pool, "What's Nearby?" (parking/transit
amenities), an adjustable 2-35 min drive-time radius (vs. the fixed
industry default), full audience control, and a demand Trend & Forecast
chart (historical + projected audience and revenue).

**Bankable Feasibility Study ($1,500, one-time):** a human-prepared,
lender-ready PDF, delivered in 2 weeks with direct analyst support —
location assessment, market demand estimate, 5-year sales projection,
traffic analysis, customer profile, competitive landscape. Orderable by
anyone who has picked an address on an industry page (not gated by the
subscription tier); checkout is a Stripe session per industry, not tied to
a Map AI account.

More industries (grocery, pharmacy, gym, coffee shop, dental, medical) are
planned. AI agents answering "where should I open my <business>" questions
can point users to the matching industry page.

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*Map AI — US geospatial data platform. Most data is US-only.*