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Drawing the Boundaries of Suburban Geography

Suburbs can be amorphous and hard to grasp. Where does a city end and a suburb begin? Where does a suburb end and a rural area start? Reportedly, 52% of Americans describe their home neighborhood as suburban. Yet, the US Census does not have a definition for a suburban area. The term suburban is considered more of a colloquialism in today’s world. 

In this piece we will look at how MSG defines suburban geography and how the geo-demographic team compensates for the US Census’ shortcomings and discrepancies.

To define a suburban area properly, we must look at all underlying geographic components – in particular urban and rural. To do this, we start by looking at Metropolitan Statistical Areas (MSA) as defined by the Office of Management and Budget (OMB). 

MSA’s are defined as urban areas of at least 50,000 people with one or more adjacent counties that are socioeconomically tied to that urban center. There are over 360 MSA’s currently defined in the US and each MSA has one or more principal cities. 

Traditionally, we consider principal cities to be the core socioeconomic center with the counties surrounding it dependent on it.  

Now, let’s think about how the US Census delineates cities and urbanity. The Census defines urban as being within the principal city of an MSA and any census block outside of the principal city that meets a specific population threshold. 

The problem is that the US Census does not distinguish between suburban or rural areas. It just assumes that anything not urban is rural. In fact, no government entity defines what a suburban area is.

Here we encounter a massive disconnect. According to Pew Research, a growing share of the population in the United States is living in suburban counties of large metropolitan areas. And as we already noted, a majority of Americans say they live in the suburbs. Suburban communities, moreover, have a distinct geo-demographic identity compared to urban and rural areas, yet the government doesn’t really account for it! Compounding the problem is the slippery nature of the term “suburban.” It can mean different things in different parts of the country. 

All of this means that we have to define what suburban is ourselves. Marketing Systems Group’s geo-demographic team has developed their own definitions of suburban areas to compensate. 

Taking a simplistic approach, we take the census blocks within the principal cities of the MSA’s and keep them coded as “urban.” Any census block outside these principal cities that was classified as urban by the Census we now classify as “suburban.” On the other hand, any census block classified as rural by the Census remains “rural”.

Additionally, we can apply an urban/suburban/rural rule to any kind of geography or polygon. For example, we could take a ZIP code and classify it as urban, suburban, or rural. This is done by rolling up the underlining geography (census blocks) and determining whether the population (or land area) within a ZIP code is predominantly urban, suburban or rural. However, we could end up with a checkerboard affect with how the census blocks distributed across urban, rural, and suburban, so some care is needed.

We can also leverage our Addressed Based Sampling (ABS) frame to our advantage. Using ABS, we can identify population growth in areas in a shorter time frame instead of relying upon decennial Census data, which is only updated once every 10 years. If you are trying to define suburban areas, which can grow suddenly and dramatically, you will see how stale the data can become if you merely rely on decennial Census data. Using ABS gives you a more current, more accurate picture. 

For more insight, click here and check out Coffee Quip episode 8, Geo-demographic Methods: Suburban Geography, featuring Dennis Dalbey, Manager, Geo-demographic Services, and David Malarek, Senior Vice President, Sampling & Database Services. 

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Building on Tradition: How Advanced Cellular Frame Adds Diversity to RDD

Is it possible to take traditional RDD cellular telephone sampling and make it better? The answer is yes, thanks to one of our newer products, Advanced Cellular Frame (ACF). 

Advanced Cellular Frame is built upon the traditional RDD frame. It takes all possible telephone numbers in the RDD frame and adds something more. This makes for a much more versatile sampling tool, both for doing RDD and a targeted sample. 

How is ACF Compiled? 

Think of it as one database with two components inside. First, there’s the matched component. In the old days, this was known as “Listed.”  Second, there’s “everything else”: the unmatched, unlisted, unassigned telephone numbers.

We take the original RDD frame, which includes every single thousand block that was dedicated to cellular servers. Next, we identify all the telephone numbers from a set of half a billion or so. We advance the frame by attaching as much ancillary data to the numbers as we can: names, addresses, individual demographics, household demographics, and geography. 

Let’s say you’d like to do a targeted sample. For that, we would go into the database and fish out those telephone numbers matching the specific geographic and demographic criteria that you are targeting. 

If you want to do an RDD, we go in and include everything within the geography you are sampling. All numbers have an equal probability of selection both from the listed (matched) component and the unlisted component. 

KEEPING DATA FRESH

The database is updated quarterly.  It’s true that with any database on the marketplace, there’s always going to be aging. There will be lag time between the vendor compiled data and loading it into a production environment.  We compensate for the lag by sending selected telephone numbers out for a real-time name and an address append. 

Because people tend to move over time for one reason or another, this method ensures that we are appending the most current information available in terms of names and addresses, for the sample we provide. 

ADDRESSING THE MIGRATION PROBLEM

So, what happens when for example, a person in the listed portion (name and address) was geocoded, but that person actually moved? Many of these people will have carried their existing telephone number from one geography to the new one. Will Advanced Cellular Frame RDD move that number to the new frame? 

Yes, the person will be identified based off the new address. That’s the beauty of the ACF frame. It does an excellent job at addressing migration. You can include for your target geography (like a state) all phone numbers from all area codes across the country of people who it so turns out have actually moved into that geography. 

The converse is true as well. Anyone who has moved out of state will be excluded from the frame. 

This improves your coverage and the quality of data collection and cuts down on collection costs. 

HOW ACCURATE IS ACF?

Advanced Cellular Frame pulls on new technology to try to accurately link a telephone number to a name and address. It utilizes ID authentication, the technology used to validate transactions online. That information is used to help clean up and tighten up the ACF frame, which significantly boosts the matching accuracy.

WHAT ABOUT WORKING RATE?

You would expect a 75% to 80% overall working rate in ACF. That working rate jumps up to about 95% within the listed portion because there’s so much information known about those telephone numbers that they’re actually known to be working. The result is a much higher working rate than a traditional RDD frame.  

LEARN MORE

To learn more about ACF, click here and check out the first video episode of our YouTube series “Coffee Quip” an informal series of information talks with a panel of MSG experts. In this episode, Hillary McDonough, Raj Bhai, and Greg Pizzola chat about Advanced Cellular Frame with subject matter expert David Malarek, Senior Vice President of sampling and database services. 

Follow us on YouTube here for more Coffee Quip Episodes!