Device-level mobility data

What is Veraset?

Veraset is a mobility data company that builds device level location signals from privacy protected sources. Their data captures human movement patterns across high interest locations and supports derived views of where devices generally reside and work, down to the ZIP code, census block group, or geohash level. Veraset is widely used in industry as a stable alternative for researchers who need consistent device level mobility data over time.

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What academic researchers should know about Veraset mobility data

Veraset publishes a U.S. focused mobility file on Dewey built from privacy protected device signals across more than 4 million points of interest. The data captures visits at the brand and location level, with home and work inferences that let researchers attach a neighborhood of origin to each device. Because the underlying signal is device level rather than panel inferred, researchers can move flexibly between location level visit research, daily trip patterns, and longer term migration and residential change.

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Why academic researchers choose Veraset on Dewey

Mobility data has become a foundational input for empirical research across economics, public health, urban planning, and real estate, but the underlying market has been turbulent. Several major providers have shuttered or restricted access in recent years. Veraset offers a stable, device level alternative with the granularity researchers need for causal designs around shocks, openings and closings, and policy changes. The home and work inferences let researchers control for or stratify on neighborhood of origin, which is hard to do with foot traffic data alone. On Dewey, Veraset pairs naturally with SafeGraph for points of interest and building footprints, Advan Research for complementary foot traffic methodology, ATTOM for the real estate context underneath the locations being visited, and Consumer Edge for the spending that follows visits. Veraset was built for commercial location intelligence, and Dewey is how academics get their hands on it.

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Veraset academic research ideas and use cases

Urban mobility, transit, and commuting. Veraset's home and work inferences let researchers study commute patterns at high spatial resolution, test how transit investments or congestion pricing reshape mode choice and route timing, and quantify the geography of jobs and housing fit in U.S. metros. Pair with ATTOM for the residential context behind home locations and SafeGraph for the workplace destinations that anchor commute flows.

Retail, hospitality, and consumer behavior. Device level visit signals combined with origin context support research on store visit drivers, the geography of customer catchments, and how brand competition plays out across formats and markets. Researchers studying hospitality can isolate the visitor base for hotels, restaurants, and entertainment venues by neighborhood of origin. Pair with Consumer Edge to connect those visits to the actual spending they generate and PassBy or Advan Research for cross verification of foot traffic.

Public policy and natural experiments. Mobility shocks like minimum wage changes, school district boundary shifts,  and pandemic era restrictions all produce identifying variation that researchers can exploit with device level data. Veraset's stable methodology supports panel designs and event studies where consistency of the underlying signal matters. Pair with ClimateCheck for the climate risk overlays that drive adaptive behavior and Consumer Edge for the spending response that often accompanies a policy shift.

Real estate, housing, and neighborhood dynamics. Researchers studying gentrification, displacement, school quality, and neighborhood change can use Veraset to measure who actually visits and uses neighborhoods, not just who is recorded as residing there. Pair with ATTOM for property records and transactions, RentHub or Dwellsy for rental dynamics, and SafeGraph for the amenities and points of interest that drive demand.

Public health and environmental exposure. Aggregated mobility signals support research on contagion, healthcare access, exposure to environmental hazards, and the daily geography of behavior. Researchers can estimate time spent in specific environments and link that exposure to outcomes. Pair with NatureQuant for nature exposure context and CustomWeather for the daily weather conditions that shape where and how often people move.

Migration, displacement, and post-disaster recovery. Home and work inferences over time let researchers study migration flows in response to climate events, economic shocks, and major policy changes. Researchers studying disaster recovery can quantify how long displaced households remain away and where they end up. Pair with ClimateCheck for projected hazard exposure and ATTOM for the property context.

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