Today, perhaps more than ever before, people are on the move. Transitioning between roles as consumers, parents, employees, tourists, and more means making a lot of stops in a day. As millions of people go about the routines of their lives, patterns inevitably emerge underlying our behaviors. But it is only now, with modern technology, that these patterns can be understood and analyzed.
Looking at these patterns in aggregate can help cities and governments to boost infrastructural efficiency and help companies and brands to more effectively engage with customers. These patterns have implications for investment decision making, public health, environmental efforts, event management and security, and so much more.
It is for these reasons that we are thrilled to announce the inclusion of premium datasets from SafeGraph in our Location Data Stream Catalogue. SafeGraph is a geospatial data company aiming to build the most accurate source of truth for understanding the physical world. They do this by providing not only complete business listing information for over 5 million Points-of-Interest (POI) in the U.S, but also by surfacing the visitor patterns registered at these locations. SafeGraph aims to help urban planners, retailers, marketers, investors, academic researchers, and many more professionals interested in understanding anonymized population movement patterns. Now, you too can start enriching your data with SafeGraph’s Patterns data in CARTO.
Built into SafeGraph’s Patterns dataset is information about when POIs are visited — by time of day and day of the week, length of visit bucketed into intervals, and the median length of a visit. Besides the time dimension, the dataset includes patterns of visits from home and work locations to a venue, creating something similar to an origin-destination matrix aggregated at the census block group level. A final dimension included in the dataset is related brand visits within the same day or same month as a POI visit. In the remainder of this post, we will provide some insights after having explored sample data from SafeGraph from the month of July 2019.
In our efforts to examine the “visit time” dimension of the data, we started out by exploring the patterns for coffee shops and hotels, two of the thousands of POI categories available in the dataset. Going into this analysis, we expected coffee shops to have very low visit numbers in the early hours of the morning with a sudden uptick during common commuting hours of 7-9 AM that sustained past lunchtime, with another surge later in the day.
For our examination, we looked at Starbucks locations in Manhattan because there are a lot of them (~190) and they tend to have predictable store hours. New York is also a notoriously difficult place for GPS data work, due to signal scattering leading to lower precision, and the high density of POIs leading to difficulty assigning visits to specific locations.
Reviewing visits by hour of the day for the locations we selected, we get the following graph:
There are some interesting patterns in this graph:
Pairing the diurnal patterns of one POI with a map, we get:
In terms of visit patterns, a near opposite of coffee shops are hotels. For this section, we chose Hiltons in California.
Digging further into the difference between Starbucks and Hilton customer behaviors, we can look at the distribution of the median dwell time for all locations in the United States. Dwell time is the length of a visit.
Predictably, Starbucks customers tend to have short stays, with the vast majority of visits lasting 10-20 minutes. Hilton, on the other hand, shows more complicated behavior, perhaps as a function of the different types of services hotels perform. Besides being a place to sleep for the night, many hotels also host events and have restaurants, potentially explaining why there are roughly as many visits less than three hours (180 minutes) as there are greater than three hours.
Digging more into the time dimension, we can look at the length of stay ‘fingerprints’ of our two different venue types. We see that the majority of Starbucks visits tend to be 5-20 minutes in length (mostly standing in line, then waiting for your order to be called). The Hilton POIs also match our intuition: longer stays are more likely, but shorter visits are not uncommon because of the aforementioned services hotels provide.
The visitor_work_cbgs and visitor_home_cbgs features of the Patterns dataset allow one to construct something similar to an origin destination matrix, as the census block groups from the visitors home or work locations can be paired with the location of the POI.
Going further, we can even create ‘catchment areas’ of POIs to see how far afield visitors came to visit a POI.
To get started we can look at the visit behavior of a function of distance of home from a POI. In this case, we are looking at two of the most visited Walmarts in the SafeGraph Patterns dataset for July 2019. These two locations have a similar number of visits, but their visit behaviors versus distance are drastically different.
The Walmart in Valley Stream, NY is located close to a large number of single family homes, while the one in Panama City Beach, FL doesn’t seem as close to as many households although a large proportion of visits are from census block groups within a few kilometers of the store. The rest of the visits are 200+ kilometers away, especially a band from 200 to 600 kilometers. This type of visit behavior is consistent with areas that have a high number of out-of-town visitors (e.g., tourists). Looking closer at the area where the Walmart is located, that seems to be true for this location as it is a popular beach destination.
The Valley Stream, NY location on the other hand has a very local visit behavior, with visit numbers quickly decaying with moderate distances (e.g., ~30km). 80% of all visits are within about 7.5 kilometers (~4.5 miles) of the store.
For our purposes, we originally chose a distance that represented 80% of all visits, and calculated the convex hull around those census block groups to define visit regions. While that worked for some areas, others have very different local versus visitor behaviors so it didn’t end up making sense to parse the data that way to create catchment areas. Instead, as we will show later, we turned to geographic clustering to define visitor regions.
Given the complex and interesting visit behavior of the Panama City Beach location, we will focus on that for the rest of this section.
Representing the census block groups as points on a map using CARTOframes, we get the following distribution of visits.
Now that we have an idea of where people are coming from to visit this one POI, we can find natural regions within all of the home census block groups. This can be done by applying a clustering technique called DBSCAN, which works well with spatial data because it can calculate geographic distances or even take pre-calculated ones, like an origin-destination matrix or the results of a routing engine. We also used HDBSCAN and it showed similar results.
To get started with our clustering, we aggregated the visits for all POIs in the city limits of Panama City Beach, so that we have a larger sample.
For the clustering with DBSCAN, we chose a threshold of 30 kilometers (~19 miles) for nearby block groups to be considered as part of the same cluster. At the same time, we set four block groups as the floor for what can define a cluster.
Given that our data is for July, 2019 and Panama City Beach is a popular beach destination in the Panhandle region, some interesting patterns emerge:
Given this last point, we can start to make guesses about demographics, but since we have the census block groups, we can retrieve that data fairly easily.
Using our new picture of home census block groups that have visits to a POI, we can start asking questions about the demographics of visitors to that POI. Depending on the threshold for percentage of visits, we will get different types of visits in different locations. Like in the example above, tourists seemed to leave a large visit imprint at the Panama City Beach Walmart while the Valley Stream, NY location seemed to be dominated by locals.
Going further, we can enrich the underlying block groups with demographics from CARTO’s Data Observatory. To get a sense of the different demographic segments that could be represented, we used AGS’ Consumer Profiles dataset.
Breaking down the dominant demographic segments within each catchment area, we see very different pictures of the types of visitors:
Now that we can identify these areas and make some strong inferences about the demographic segments, geomarketing decisions can be based on our findings. For example, resorts can advertise with some confidence in certain areas in northern Alabama and Georgia to entice people to come to Panama City Beach.
There are additional ways in which we can draw further insights from our data:
“Brands Visits” are a third major dimension in the SafeGraph Patterns dataset. SafeGraph offers two different ways to explore brands: same day visits or same month visits. Here, we’ll focus on same day visits.
While there is a lot more to explore, we’ve simply compared the nationwide profile of top 10 brands visited by Gold’s Gym and SoulCycle visitors. Gold’s Gym is more of a budget gym, while SoulCycle is more expensive (e.g., two classes can cost more than a 1-month membership at Gold’s). While we can use the Home/Work census block group analysis to better understand the demographics of these visitors, we can further try to segment the visitors by their brand relationships.
Comparing Gold’s Gym and SoulCycle, we can see some patterns already:
Comparing the same brand between different states yields some insights as well.
With access to a rich pattern dataset like Safegraph’s, business decision makers can delve further into brand relationships in several ways.
A retail and industry trend analyst could break down brands by their industry classification (NAICS) code and aggregate by that instead of brand names like above. This would allow them to, for example, aggregate all coffee shops, pharmacies, etc. to get a clearer picture of the venues visited.
For a geomarketer for a big-box retailer, looking at the proximity of important POIs to other brands, can provide a better understanding of one company’s impact on and relationship to others, for example exposing how SoulCycle customers tend to interact with Walmart vs. Target vs. Costco.
In this post, we’ve barely scratched the surface of this data – there are countless insights to be drawn and applications for nearly every conceivable business model and industry. By leveraging this data in your analysis, you can create a deeper understanding of your customers and uncover previously unforeseen growth opportunities.
Learn more about CARTO’s location data streams today, and start incorporating data from SafeGraph, and other high-quality curated data sets into your models and spatial analyses today.
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