Counting a Neighborhood

Oct 13

4 min read

At Cerca Solutions, we believe a data-based approach is one of the strongest tools a community has for making sure a neighborhood is actually improving for the people living in it. Numbers hold people accountable in a way that systems can't always do. But if you've ever worked with spatial data at the neighborhood level, you've probably run into the same friction. The infrastructure that collects most public data was built to be as broad and far-reaching as possible. It wants to capture as much as it can, on as many people and topics as it can, at a scale that works for the whole country. That's exactly what makes it powerful, and it's also exactly why things start to break down once you get specific about a single neighborhood.

For those of you who are starting to move your organizations toward data-centric strategies, here's what to keep in mind as you begin that work.

How you use and interpret the data matters as much as the data itself

Even once you have good numbers, the way they get used introduces its own set of problems.

Data tends to move in herds is an idea borrowed from the RBA framework: if you have one or two data sets pointing toward the same thing, it's likely that a lot of other data sets will point in the same direction. That means you don't need ten different data sets to demonstrate one outcome. Keeping it simple and succinct frees you up to focus on several different outcomes at once, rather than exhausting your capacity proving just one.

A number by itself doesn't tell you the story behind it. A drop in enrollment could mean families are being priced out, or it could mean a birth rate decline that started years earlier, and those two explanations point toward completely different responses. The data can tell you that something changed. It usually can't tell you why on its own. This is where lived experience in your neighborhood matters. Being able to understand the reason something is happening is a lot different than understanding that it is happening. Each data point is also only telling you one side of the story, since it's standing in for someone's life. Understanding it from that person's perspective gets you closer to the why.

Not every organization is set up to handle a lot of data at once, even when they genuinely want to be data-driven. A dashboard full of fifty new metrics can overwhelm a team that's used to tracking two or three numbers a quarter, and the result is sometimes that everything gets ignored rather than the most important pieces getting prioritized. Starting small and growing it year by year, metric by metric, gives a team room to build not just capacity but the judgment to know what's actually worth tracking.

Estimates rarely match your boundaries

Once you're working with the numbers themselves, geography is often the first wall you hit. Census tracts, block groups, or any other census boundary rarely line up cleanly with the boundaries of the neighborhood you're actually working in. There are ways to handle this, but when you're apportioning a tract's data across a boundary it was never drawn to respect, the figure you land on can look precise on a page while carrying real uncertainty underneath it. The best check is cross-referencing with other data points. If part of the block group feeding your demographic numbers turns out to have no residential buildings and is just a large mall, that changes what the number is actually telling you.

One solution that the City of Atlanta has used is a neighborhood statistical area system, a layer designed specifically to translate census geography into something closer to how residents actually describe where they live. The State of Atlanta's Neighborhoods article uses this system to clearly show what neighborhood areas are scoring in different measures. It's a genuinely useful piece of infrastructure. But building one takes lots of money, a dedicated staff, and years of iteration, and not every city, and certainly not every small organization, has the funding, time, or technical capacity to map that out for their whole city, only to find what is true about your neighborhood.

Not all the data you need is public

Some of the most important data for neighborhood work simply isn't available publicly, and for good reason. Much of what you'd want to know about individual children, families, or households would violate basic privacy protections if published at that level of detail. That's not a flaw in the system so much as a boundary it has to hold.

The bigger challenge is usually specificity, not availability. Say you want to know how many students at a school actually live within your neighborhood boundary. That's not a number you can pull from ACS or any public dataset. It requires going directly to the institution that has it, in this case the school itself, and building a relationship that allows for that kind of data sharing. Or it may be an area no one has measured yet, in which case your organization has to run the survey itself.

This is part of why partnerships matter so much in this work. Not just because most community quarterbacks can't provide every service themselves, but because partners providing different services can show you how their own performance reflects the broader neighborhood results.

If you've worked with community-level data before, none of this is new to you. You've likely run into all of it already. For those of you who are new to neighborhood data, none of this should be a reason to trust data less. If anything, it's the opposite. Understanding where these numbers come from, what they can and can't tell you, and how much translation happens between the raw source and the figure in your report is what lets a neighborhood hold itself accountable with real confidence instead of false precision. This is why building out your team’s data capacity is important, because with the right data, you can truly view your community in a new lens.