Our Methodology

Unacast gathers location data from smartphones and mobile apps, with opt-in consent from users (all of our data is privacy-compliant in the markets in which we operate). We then analyze and contextualize the data for purposes of providing accurate location intelligence, which can be used for advertising, audience segmentation, competitive landscaping, urban planning, and to generally understand people-based movement analytics.

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How we collect and aggregate our data

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Unacast gathers first-party GPS data from smartphones and mobile apps through direct partnerships with providers, with opt-in consent from users.*

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We extrapolate, clean, and deduplicate our data to build our location intelligence products. Supply fluctuations are common in the location data industry, so we must distinguish between trends (peaks and troughs) versus changes in supply.

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We build our location analytics platform, curated datasets, and human mobility APIs to meet our customers where they are in the data analytics journey.

* All of Unacast's data is privacy-compliant in the markets in which we operate.

Location Data

To make raw location data usable for analysis, we developed a patented data processing technology, AdmitOne. The AdmitOne platform processes billions of raw location signals every day, eliminating fraudulent, problematic, and duplicate data, and creating a unified, high-quality data stream.

We add forensics to each verified location signal, providing additional information about signal origin, location accuracy, and other key characteristics. Location forensics enable analysts to quickly segment data for analysis.

Observations API

Up to 3 years of historical observations on demand.

Area Visitors API

Consumer foot traffic trends for areas of interest over time.

Trade Areas API

Where consumers travel from to shop, dine, or visit other places of commercial interest.

Area Personas API

Understand consumers’ actual interests and buying intent.

Location Analytics

For our Location Analytics datasets, GPS data is combined with contextual data including demographics, industry trends, venue attributes, historical data, and more. We then train our machine learning models to learn the relationship between different data sources and our target mobility insights. In total, the model comprises more than 120 features to train those relationships based on our long history of high-quality location data. Our machine learning-powered data is then curated into datasets to provide you easy to work with location analytics.

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Foot Traffic

Our foot traffic dataset helps you understand visitation at a specific site or area – how many people visit, types of visitors, and traits of the visit.

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Trade Area Data

Make better decisions by understanding the home and work locations of your visitors.

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Cross-Visitation Data

Discover other shops and locations your customers visit.

Resident Demographics


Determine what types of people are visiting a location.


How does Unacast ensure consumer data privacy?

To learn more about Unacast’s data privacy policies, read our privacy documentation.

Who can use this data and how?

Unacast’s data feeds are used by customers in a range of industries for many different purposes. Location intelligence is relied on by investors in real estate and equities, by retailers for site selection and measuring foot traffic at stores, by the public sector to plan public spaces and key infrastructure, by logistics companies to inform warehousing and transportation, and by the financial industry to inform alpha, detect market shifts, and measure portfolio risk. Find more information about our use cases.

How often is the data refreshed?

Unacast offers fresh, high-quality data feeds with dense location data for multiple markets globally. Each dataset varies in its update frequency, from as short as 24 hours to up to quarterly.

How far back does the data go?

Currently, our datasets extend back to Q1 2019. In some instances we have data into 2018, depending on the specific use case. We are continually abridging our models with complementary data sets, so rearview-looking timeframe may extend more accurately into the future.

How is the data organized?

Each of our datasets has a clearly defined schema that is well-documented for users ingesting our feeds. Read more in Unacast’s documentation.

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Meet with us and put Unacast’s data to the test.
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