Visit patterns for U.S. and Canada points of interest

What is Advan Research?

Advan Research is a location intelligence company that turns anonymized mobile device signals into foot traffic data for the financial, real estate, and retail industries. Its Patterns Plus datasets trace back to the SafeGraph Patterns methodology, rebuilt with proprietary geofences and full population normalization so visit counts reflect actual foot traffic rather than just the devices in a mobile panel.

‍

What academic researchers should know about Advan Research foot traffic data

On Dewey, Advan Research publishes Foot Traffic / Weekly Patterns Plus and Foot Traffic / Neighborhood Patterns Plus. Weekly Patterns Plus reports visit and visitor counts, dwell time, and home census block group for individual points of interest across the U.S. and Canada, with history back to January 2017. Neighborhood Patterns Plus aggregates that same footfall to the census block group and dissemination area level, breaking out weekday, weekend, breakfast, lunch, and dinner visit patterns from January 2019 forward. Every field is normalized against panel size and country population, and Advan Research draws its own hand drawn geofences rather than relying on shared polygons from another provider.

‍

Why academic researchers choose Advan Research on Dewey

Advan Research picked up the SafeGraph Patterns business in 2023 and kept building on it, most recently with a Plus methodology that scales the raw panel up to the full population instead of leaving researchers to normalize a shrinking, self selected sample of devices. That saves academic teams the work of reconciling several discontinued mobility panels into one time series. Foot traffic data like this is priced for financial and real estate industry buyers, and Dewey is where academic researchers get the same signal without that price tag attached. It pairs naturally with other Dewey partners for multidisciplinary work: SafeGraph for point of interest attributes, PassBy for an independent mobility panel to check patterns against, ClimateCheck for physical climate risk at the same locations, the U.S. Census Bureau for local demographics, and RentHub for residential rental context around the same points of interest.

‍

Advan Research academic research ideas and use cases

Foot traffic data is a natural fit for studying how regulation shapes shopping behavior. Javier Donna at the University of Miami Herbert Business School, together with Marit Hinnosaar and Toomas Hinnosaar at the University of Nottingham and Andre Trindade at Nova School of Business and Economics, used Advan Research location data to study North Dakota's 2019 repeal of its Sunday closing law, tracking how shoppers shifted their visits once stores could open Sunday mornings.

Neighborhood Patterns Plus is built for site selection and local market research. Researchers can pull weekday versus weekend visit patterns and breakfast, lunch, and dinner demand for any census block group to study how commercial corridors perform against nearby residential density, or to test whether new retail development changes foot traffic in the surrounding blocks.

Timothy Leslie at George Mason University, with Hossein Amiri and Andreas Zufle at Emory University, built a sector level view of the economy on top of Dewey's Advan Weekly mobility dataset, tracking visits, travel distance, and time spent at points of interest across NAICS categories from 2019 to 2023 to study how different parts of the economy recovered at different speeds.

Mobility signatures are also proving useful in criminology. Ariadna Albors Zumel, Michele Tizzoni, and Gian Maria Campedelli at the University of Trento used Advan Research mobility data alongside crime records and Census demographics to forecast crime at a twelve hour, block level resolution across Baltimore, Chicago, Los Angeles, and Philadelphia, publishing their results in the Journal of Quantitative Criminology.

Roya Shomali and colleagues at the University of Alabama's Culverhouse College of Business used Advan's Weekly Patterns to flag the visit timing, dwell time, and clientele patterns associated with illicit massage businesses, an approach that could extend to other place based detection problems in urban economics and public policy.

‍