Use case · Site selection

Choose sites by when they come alive

Hourly foot traffic for every venue around a candidate site. See each area’s rhythm, venue mix and busy hours, and load the JSON into your own models.

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1:26 · Example: New York City: Grand Central, Union Square, SoHo, Williamsburg. Products in the video are fictional; the data is real BestTime data.

The problem

Demographics stop at who

They tell you who lives nearby, not when the street comes alive.

Enterprise contracts

Foot traffic data often means annual contracts and enterprise prices.

Weeks of manual research

Checking venues one by one doesn’t scale past a few sites.

How BestTime helps

Hourly foot traffic forecasts for public venues, from anonymized mobile device signals from a sample of visitors, combined with weather, events and holidays. Busyness is a percentage of each venue’s own busiest hour.

Venue Filter

GET /api/v1/venues/filter

Every venue in an area or collection, filtered by type, day, hours and busyness, each with its hourly curve. One call, sorted the way you need.

New forecast

POST /api/v1/forecasts

Create the weekly forecast for any public venue by its name and address.

Collections

collection_ids=col_…

Save your own venues or a shortlist once, then filter and refresh only those.

Weekly forecast

GET /api/v1/forecasts/week

Every hour of the week for one venue, as a percentage of its own busiest hour.

What it brings

Typical teams

Site selection and expansion

Rank candidate sites by when they come alive.

Commercial real estate

Show tenants the rhythm around a property.

Franchise developers

Match each concept to an area’s busy hours.

Location data teams

An hourly demand layer in your own models.

Questions

Can I compare areas with each other?

Compare their timing: when nearby venues reach their busy hours, the venue mix and the opening hours. Each venue’s busyness is relative to its own peak, so it shows rhythm, not visitor counts.

How big can an area be?

Use a center and radius, or a bounding box. Larger areas return more venues and use more credits; round coordinates to two decimals so repeated calls come from the cache.

What if a venue isn’t in BestTime?

Create its forecast by name and address with the New forecast endpoint, then add it to a collection.

Video transcript

Choosing a new location? Demographics tell you who lives nearby. They don't tell you when the street comes alive.

Foot traffic data usually means annual contracts and enterprise prices. Or weeks of checking venues one by one.

BestTime has venues in cities worldwide, each with an hourly foot traffic forecast for every day of the week, plus its type, rating and reviews.

The Venue Filter pulls every venue around a candidate site, by radius or bounding box, in one call. Here: four areas in New York City, sixty venues each.

Count how many venues around each site are in their busy hours, hour by hour, and each area's rhythm appears. Grand Central fills up from eight and peaks at lunch. Williamsburg starts later, and stays active until eleven.

Add the venue mix, ratings and opening hours, and you can compare sites on timing, not just on location.

Need more? Create forecasts for any address, save shortlists as collections, and load the JSON straight into your own models.

Typical users are site selection and expansion teams, commercial real estate, franchise developers and location data teams.

Prices are published, and you can start free. Try it on your own candidate sites in Radar.

Learn the tools

Tutorial 2: Radar: filter any cityTutorial 3: Venue Filter API

Other use cases

Nightlife appsCity guides & travelDOOH advertisingRetail & hospitality opsTourism & crowds

Try it on your own city

Sign up for free to see predicted live data for the current hour, based on real-world signals such as weather, events and holidays.

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