Weekly Patterns data provides the same foot traffic data insights from Patterns, updated weekly.
Anonymized and aggregated foot traffic and mobility data to census block groups (CBG) to help you understand consumer behavior, open or close new locations, and more.
We rigorously test for bias by comparing our panel to the true proportions reported in the US Census.
Patterns data can be used to estimate foot traffic to locations and tell a story about the economic health of a set of locations.
Complex engineering to clean, cluster, and combine visit data allows us to precisely attribute a device ping to a specific POI or census block group.
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We partner with mobile applications that obtain opt-in consent from its users to collect anonymous location data. This data is not associated with any name or email address. This data includes the latitude and longitude of a device at a given point in time. We take this latitude/longitude information and determine visits to points of interest. We then aggregate these anonymous visits to create our Patterns product.
Yes! We have Patterns data going back to January 1st, 2018 for the US and back to January 1st, 2019 for Canada. In order to successfully compare the data over time, we encourage normalizing based on our panel size over time. Each monthly and weekly delivery of Patterns includes the Patterns to enable this normalization. Please see our Data Science Resources for guidance on how to go about doing this.
Please note that the underlying Places (i.e., Core + Geometry) data used to create Patterns changes over time. Ongoing releases will always be using the latest version of Places: for example, all Patterns data from January 2021 onward will encompass the new POIs added to Core in January 2021 (i.e., industrial POIs). This also means that historical Patterns data will not contain new POIs until historical Patterns are re-generated with new versions of Places, which is generally done no more than twice a year. See Backfill Key Concept in Patterns for more details.
Small geographic bias exists in our panel based on our understanding of the home locations of the devices in the panel. SafeGraph tested for geographic bias by comparing its determination of the state-by-state numbers of home location of the devices in the panel to the true proportions reported by the 2016 US Census. Based on that analysis, SafeGraph panel density closely mirrors true population density. The overall average percentage point difference is < 1% with a maximum of +/-3% per state. For a deep dive on geographic bias in the panel, see Quantifying Sampling Bias in SafeGraph Patterns.