As we continue on our mission to democratize access to clean and accurate geospatial data, we’re constantly coming up with new ways to make our data more useful for answering critical business, social, and economic questions. Today, we’re excited to extend this expertise and knowledge by launching SafeGraph’s newest dataset: Neighborhood Patterns.
On a monthly basis, we already package up foot-traffic and visitor insights data for millions of Points of Interest (POI) across the United States. With the official launch of SafeGraph Neighborhood Patterns, we’re now able to apply the same methodology we use for SafeGraph Patterns to every single Census Block Group (CBG)— the most granular area that the US Census reports demographic data on, typically made up of 800 to 1,200 households.
Why Neighborhood Patterns? After a lot of thought and customer demand, we realized that CBGs are essentially like physical places and, thus, can be approached in a similar way to how we approach Points of Interest data.
“Our mission is to be the go-to source of truth about any physical place in the world. But a place doesn't necessarily need to be a building with four walls or even a Point of Interest at all. A Census Block Group is just a different kind of place. SafeGraph Neighborhood Patterns is a natural extension of what we already do best” - Mike Sussman, Director of Enterprise Sales at SafeGraph
The benefit here is simple: using anonymized and aggregated location data to understand how devices move within and around CBGs can help businesses, researchers, and local governments better assess existing foot traffic trends and predict future consumer movement patterns.
More specifically, this information is uniquely critical for informing decisions around retail and commercial real estate site-selection. Understanding how people interact with their local communities is the key for making smarter decisions about where to put up shop, how to define business hours, and what factors can influence a given business’s potential for success. Some of our customers and research partners have already begun using SafeGraph Neighborhood Patterns in beta to do just that and a whole lot more.
At the end of the day, there are a few key questions that we wanted to use this new dataset to answer that we weren’t able to do via SafeGraph's POI foot traffic patterns alone:
You get the point: by applying the same rigor to CBGs as we already do to our POI foot-traffic data, we can begin to answer questions about consumer movement—and its impact on local business—in more nuanced ways than ever before. More importantly, we did this because our customers asked for it; they encouraged us to take our geospatial expertise to “zoom out” and look at an entire neighborhood as a “place.” And that’s exactly what we did.
This has become a true game-changer for retail businesses and commercial real estate development companies alike, as it’s added a new level of depth to their site selection and retail trade area analysis efforts.
For example, before settling on a new location to set up shop, they need to know upfront how busy that area is at any given time as well as who is traveling to that area (and from where). Knowing this information is absolutely critical for making informed assessments about potential business success in any given location. Taking this a step further, what makes SafeGraph Neighborhood Patterns truly unique is that it allows businesses to see movement patterns and foot traffic trends in day-part granularity. Need to know the visitor demographics to a neighborhood during breakfast, lunch, afternoon-tea, dinner, or nightlife hours? Neighborhood Patterns has you covered.
And while there is a clear business case for using this data, it has also made a huge impact in academic research as well. In fact, earlier this year, we published an eye-opening article about how SafeGraph’s Neighborhood Insights data was used by researchers at the University of Wisconsin-Madison to visualize the reality of modern segregation in Milwaukee.
So while our initial focus may have been to use this data to assist with site selection, the reality is that there is huge potential, beyond site selection alone, for SafeGraph’s Neighborhood Patterns to answer important questions at the community level.
Building SafeGraph Neighborhood Patterns didn’t require us to reinvent the wheel; we already had a solid foundation to build upon. It just needed to be adapted to CBGs as “places.” And because this data is aggregated to each CBG, you don’t have to worry about wrangling hundreds of billions of GPS data points yourself. SafeGraph already takes care of analyzing that for you.
For those of you interested in the specifics, we generated this data by first analyzing location insights derived from an anonymized location data panel of 45 million devices and then by:
If this algorithm looks familiar to you, it's because it's the same location data pre-processing, cleaning, and clustering used in SafeGraph’s Visit Attribution algorithm, which we use for determining if a mobile device actually visited a specific POI—with a great deal of precision.
The end result is 220,000 rows of data (i.e. one for each CBG) delivered monthly, giving you best-in-class insights about movement patterns within and around neighborhoods.
This data is so powerful that it has been used—and trusted—by the Centers for Disease Control and Prevention (CDC) in its efforts to understand how well Americans are respecting stay-at-home orders during the COVID-19 pandemic and, as certain states begin to reopen their economies, how they are beginning to move around between communities. These consumer mobility insights are critical for understanding how the gradual return to normal is either mitigating or further fueling the spread of the novel coronavirus. As a part of SafeGraph’s COVID-19 Data Consortium, they are joined by thousands of researchers, academic institutions, local, state, and national government bodies, and non-profit organizations to use the Neighborhood Patterns dataset to change the course of this crisis.
As you can see, having access to strong neighborhood insights can make a positive impact on both the private and public sectors alike. Whether the data is used to inform health and public policy during the COVID-19 crisis, give retailers and real estate developers a leg up in their site selection and retail trade analysis efforts, or answer broader social, cultural, and economic questions at the local, state, and national levels, SafeGraph’s Neighborhood Patterns dataset will transform how we all approach and take action on aggregated consumer movement patterns. It’s time to harness the power of SafeGraph Neighborhood Patterns, now available to everyone starting today!
If you are with a for-profit business, contact our team to learn more.
Academic researchers, non-profits, and government organizations: Join SafeGraph’s COVID-19 Data Consortium to get SafeGraph Neighborhood Patterns data at no-cost.