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Analyzing Foot Traffic at the Census Block Group Level

December 23, 2020
Briana Brown

SafeGraph’s newest dashboard is here! The Neighborhood Patterns dashboard allows users to visualize and analyze foot traffic data by census block group (CBG). While any industry that interacts with people can benefit from Neighborhood Patterns data, we’ve chosen to highlight some of the most popular uses in retail, real estate, and the public sector.

This dashboard is powered by makepath, an Austin-based spatial data science company that specializes in visualizations and working with open source GIS libraries.


Retail is all about consumers. Brands strive to know as much as they can about their prospective and existing customer base so they can rise above the competition and win market share. This is no small task, and to tackle it, retailers are increasingly turning to data to level-up their strategies.

While there are many types of data a retailer can use to better understand their business, foot traffic is particularly useful for consumer insight. Neighborhood Patterns data provides detailed foot traffic information at the CBG-level so retailers can understand movement trends by geography. 

With CBG-level movement information, retailers can answer critical questions about consumer behavior. Knowing what times of day a CBG is most populated can help retailers in their site selection process. A QSR, for example, might look to open a new location in a CBG with high foot traffic at lunchtime. Similarly, Neighborhood Patterns can provide information about who those people are. By understanding which CBG foot traffic originated in, retailers can append census data to analyze characteristics of the population and develop products, services, and offers that resonate with specific demographics.

Measuring population movement at lunch time can inform a QSR's site selection process.

Real Estate

As the saying goes, real estate is all about location, location, location. Of course, this phrase is usually said in relation to a property. However, the location of people is also important to the real estate industry. Data is rapidly changing real estate, with foot traffic playing a key role.

Property is one of the most expensive investments a company can make. There are many factors that go into making a property investment decision, such as the upfront cost, maintenance costs, and general location. Commercial real estate, in particular, must also analyze how populations will interact with this space. Unlike residential real estate, commercial real estate almost always depends on how well a property is situated in relation to those who will frequent it. That’s where Neighborhood Patterns comes in.

Whether managing an investment portfolio, consulting for a retail brand determining where to open its next location, or identifying a new office building, commercial real estate companies must take foot traffic into account for a complete picture of a property. With CBG-level foot traffic data, real estate analysts can identify how valuable a property investment will be. Measuring foot traffic and where it originates can inform data modeling and projections, so decision-makers can understand the long-term potential of an investment. For example, with Neighborhood Patterns data, real estate analysts and developers can model how many people are likely to visit a location, where they’re from, and who they are. This empowers real estate firms to make data-driven investment decisions and set themselves up for growth.

Visualizing how the population moves on a Monday morning can help real estate analysts decide where to invest in a new corporate property.

Public Sector

Commercial businesses are not the only organizations that benefit from CBG-level foot traffic data. The public sector relies on this information for resource allocation, community planning, and crisis management. As with most industries, the public sector is relying on data more than ever to make strategic decisions. But the level of detail about constituents and their mobility found in Neighborhood Pattens data is a game-changer for governments looking to optimize resources and develop communities. 

As the second-most granular level of geography the U.S. Census Bureau collects data on, CBGs are an ideal resolution for analyzing how people move throughout the day. When combined with census demographics, Neighborhood Patterns data reveals key information for the public sector. Associating demographic characteristics such as age, race, and income profiles with specific geographies allows organizations to develop services tailored to community needs.

Planning public transportation is not as simple as getting people from point A to B. To design the transportation network most valuable for a community, planners need to understand who will be using the system and where they need to go. Large volumes of visits to one CBG from another may indicate a need to increase train or bus frequency between those two locations, while some CBGs may be mostly populated by individuals with cars who don’t rely as much on public transportation. These types of insights also help public sector organizations uncover and remedy socio-economic problems. This study from the University of Wisconsin-Madison Geospatial Data Science Lab used Neighborhood Patterns data to reveal areas of low social mobility and community segregation. With reliable data, the public sector can improve communities and make real progress. 

Analyzing the flow of population to and from different areas throughout the day can uncover needs for new infrastructure or services.

Analyzing how populations move throughout the day is critical for just about every industry, but particularly for retail, real estate, and the public sector. Each of these industries relies heavily on understanding people, their habits, and how they relate to a location. SafeGraph Neighborhood Patterns data provides an accurate view into consumer and constituent mobility, and an efficient way to glean demographic insights from census data. Visit the Neighborhood Patterns dashboard to see how populations move throughout your area, and envision what your organization can do with the data.

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