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Location intelligence for teams that open doors

Know where your customers come from. And where to open next.

MapDove turns foot traffic into decisions. Track visits and trade areas for any place, score every hexagon of a city for suitability, and forecast a new store’s visits — including what it takes from the stores you already run.

Start free Explore the product

Free plan, no card. Five demo cities included.

3 mi ring
Suitability 65.5/100
Forecast visits / day 359 Candidate site

Live visits Click or use arrow keys to move the site

The demo dataset, live from the API

cities
5
places tracked
1,235
modeled visits
334M
hexagons scored
2,417

Synthetic demo data modeled from neighborhood-level parameters.

Platform

One workspace from “how is it doing?” to “should we open here?”

The descriptive side tells you what is happening at a place. The decision-support side tells you what would happen if you changed the map.

  • Foot-traffic analytics

    Daily visits, unique visitors, frequency and dwell for every place, with hourly heatmaps and category benchmarks.

  • Site-suitability scoring

    Weight nine criteria — from daytime population to rent and demand gaps — and get a ranked list of hexagons with spacing rules.

  • Huff visit forecasts

    A calibrated gravity model predicts daily and annual visits for a candidate site, sized by square footage and brand strength.

  • Cannibalization

    See exactly which existing stores lose visits to a new opening, by how much, and how much of the forecast is net new.

  • Ring & drive-time catchments

    Up to four rings in miles, kilometers or drive minutes, with area-weighted demographics and the business mix inside.

  • Trade areas & cross-shopping

    Visitor-origin hexes, lifestyle segments and the places your visitors also go — the raw material for co-tenancy.

Decision support

How the decision support works

Three models, run in sequence, each one narrowing the map.

  1. Score the city

    Pick a category and weight the criteria that matter. Every hexagon gets a 0–100 suitability score from a min-max normalized weighted overlay; cost criteria like rent and competition are inverted.

  2. Forecast the shortlist

    Drop a candidate on a top-ranked hex. The Huff model splits each hexagon’s demand across every competing store, calibrated against observed visits, to predict yours.

  3. Check the catchment and the cost

    Read ring and drive-time demographics, and the cannibalization table: which of your stores give up visits, and what is truly incremental.

Methodology

Use cases

Built for the people who sign leases and defend them

Plan a network, not just a store

Rank a city for your format, forecast each shortlisted site and see cannibalization against your existing fleet before the real-estate committee meets.

  • Weighted suitability by format
  • Forecast visits with brand strength
  • Net-new vs. transferred visits
397 forecast daily visits, candidate in Mueller (illustrative)

Prove a center’s draw

Show prospective tenants the trade area, visitor segments and cross-shopping of a center, and benchmark it against the city.

  • 85% trade-area hexes
  • Co-tenancy from cross-shopping
  • Percentile vs. category
81st percentile in city for its category (illustrative)

Find gaps, not just traffic

The demand-gap criterion finds hexagons with high demand and weak access to your category — the white space between competitors.

  • Demand-gap layer
  • Hourly and day-of-week peaks
  • Dwell time vs. category
1.5 km minimum spacing between suggested sites

Tell the corridor’s story with numbers

Track visits by neighborhood and category over time, show seasonality and growth, and give site selectors the data they ask for.

  • Neighborhood trends
  • Category mix and movers
  • Exportable reports
12 mo of daily history on paid plans

Coverage

Five demo cities, ready on day one

Four Sunbelt metros and Dubai, each with a full hexagon grid, neighborhoods, 247 places and a year of daily visits.

See coverage

From the field

What teams do with it

  • We used to argue about rings on a whiteboard. Now we argue about weights in a scenario everyone can open — and the cannibalization table ends half the arguments.

    Dana WhitfieldDirector of Real Estate, Juniper & Rye Bakeries

  • The trade-area segment mix is what our leasing deck was missing. Tenants stopped asking “who shops here?” because the first slide answers it.

    Rafael MontoyaVP Leasing, Corridor Retail Partners

  • Having the formulas published matters. My analysts can explain a forecast line by line instead of pointing at a black box.

    Noura Al-MansouriHead of Strategy, Palmline Hospitality Group

Personas and companies are fictional and used for illustration.

Pricing

Start free. Upgrade when the analyses pile up.

Every plan includes all demo cities. Paid plans add a year of history, exports, the API and more seats.

  • Free

    For trying the workflow on the demo cities.

    $0/ month  
    Start free
    • All demo cities
    • 90 days of foot-traffic history
    • 25 site analyses / month
    • Trade areas & cross-shopping
  • Most popular

    Pro

    For analysts and small expansion teams.

    $149/ month billed monthly
    Start with Pro
    • 12 months of history
    • 500 site analyses / month
    • CSV export
    • REST API (10k calls)
    • 5 seats
    • Visit alerts

  • Business

    For real-estate teams running analyses daily.

    $499/ month billed monthly
    Start with Business
    • Unlimited site analyses
    • REST API (100k calls)
    • 25 seats
    • Brand benchmarking
    • Priority support

  • Enterprise

    For custom markets, data feeds and SSO.

    Custom tailored to your markets
    Contact sales
    • Custom markets & data feeds
    • SSO & audit exports
    • Unlimited API
    • Dedicated success manager
    • SLA

Prices in US dollars, excluding applicable taxes.

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FAQ

Questions we hear first

Is the data real?

Not in the demo. Every city ships with a synthetic dataset modeled from neighborhood-level parameters — population, income, density, commercial intensity — so the patterns are realistic but no real person or device is behind any number. Enterprise customers can bring their own data feeds.

What is the Huff model?

A gravity model of store choice: each shopper’s probability of visiting a store rises with the store’s attractiveness and falls with distance. MapDove calibrates it against observed visits for the category, then uses it to forecast a new site and its cannibalization.

How is suitability scored?

Nine criteria are min-max normalized across the city’s hexagons, cost criteria (rent, competition) are inverted, and a weighted sum gives each hexagon a 0–100 score. The weights are yours; the methodology page shows the exact formulas.

Can I use it from my own tools?

Yes. Pro and above include the REST API with API keys: places, visits, trade areas, market data and every SDSS model, returning JSON and GeoJSON.

Which cities are covered?

Austin, Houston, Phoenix and Miami (US dollars, imperial units) and Dubai (UAE dirham, metric units). Enterprise plans can add custom markets.

Do you support Spanish and Arabic?

Yes — the site and the product are available in English, Spanish and Arabic, with full right-to-left layout for Arabic.

Your next location is already on the map.

Open the demo cities, run 25 analyses a month on the Free plan, and bring your team when you are ready.

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