5 things to consider when identifying priority neighbourhoods using data

Different datasets tell different stories. Neighbourhoods with similar levels of deprivation can face very different challenges. And focusing too narrowly on one or two indicators can lead to missed opportunities or misplaced effort. Identifying priority neighbourhoods is rarely as simple as finding the “most deprived” areas.

If you’re using data to decide where to focus, here are five things to consider.

(Also, take a look at our complete guide to local data for charities for more resources.) 

1. Identifying priority neighbourhoods requires looking beyond overall deprivation

It’s common to start with deprivation data, and it’s a useful starting point. But areas with similar deprivation levels can have very different drivers of need.

For example:

• One neighbourhood might be driven by poor health outcomes
• Another by unemployment and low skills
• Another by housing pressures or population churn

If you’re only looking at an overall score, you risk treating very different places in the same way. 

What to consider:
Look beneath headline measures such as the Index of Multiple deprivation (IMD) to the individual domains to get a better understanding of what is driving need in each area—not just how much need there is.

Take a look at our resource on explaining IMD deciles as a starting point.

2. Single indicators rarely tell the full story

It’s tempting to focus efforts on one key metric that aligns closely with your mission. But most social issues are interconnected:

• Employment is linked to health
• Housing affects education outcomes
• Income shapes access to services

Focusing on one indicator can unintentionally overlook places where multiple, compounding challenges exist.

What to consider:

• How do different factors combine in each neighbourhood? 
• Are there areas where several risks overlap, even if no single indicator stands out on its own?

3. Relative rankings can hide important differences

Some datasets (like deprivation indices) are based on rankings. That makes it easy to identify the “top 10% most deprived areas” but harder to understand how different those areas actually are.

Two neighbourhoods might sit next to each other in a ranking but have very different real-world conditions. 

What to consider:
Don’t rely on rank alone. Look at the underlying values and other supporting context. Ask whether differences between areas are significant enough to matter for your work.

4. Choosing the right geography

In England, data is often analysed at LSOA level because it’s consistent and widely available. But LSOAs aren’t always meaningful “neighbourhoods” in a real-world sense. They can split communities or group together places that feel very different on the ground.

At the same time, moving to more bespoke geographies (like service footprints) can make it more difficult and more time-consuming to find consistent data.

Just as importantly, it’s not only where you analyse, but what you compare it to. An area might look high-need compared to the national average, but fairly typical within its local authority or vice versa.

What to consider:

• What level of geography best reflects how you deliver services?
• Are you balancing consistency of data (e.g. LSOAs) with real-world meaning?
• What benchmarks are you using and how does that change the story?

5. Data needs context to become insight

Data can help you narrow down where to look but it can’t tell you everything you need to know about a place.

Charities already hold valuable insight that doesn’t sit neatly in a dataset:

• Where demand for services is rising or falling
• Which groups are underrepresented or harder to reach
• How local services, community networks, or informal support actually operate

This kind of knowledge can highlight gaps or nuances that aren’t visible in published data, or that lag behind real-world change.

It can also challenge your initial findings. An area that appears high-need in the data might already have strong provision or established partnerships. Another that looks less severe on paper might be somewhere you’re seeing increasing pressure or unmet need.

What to consider:

• Does the data align with what your teams are seeing on the ground?
• Where are you already working, and what does that tell you about need and demand?
• Are there places where your experience suggests a different priority than the data alone?

Supporting better decisions about priority neighbourhoods

One of the biggest challenges in this process is the time and effort required to bring it together in a way that’s genuinely useful.

Pulling together multiple datasets, aligning geographies and making sense of different indicators can quickly become complicated and difficult to manage. And the more time spent assembling data, the less time there is to reflect on what it actually means for your work.

Tools like On Demand are designed to address that gap. By bringing together a wide range of indicators into detailed, place-based profiles, the reports provide a consistent and ready-to-use view of local need.

With the groundwork already done, you can spend more time applying your organisation’s knowledge, sense-checking findings and making confident decisions about where to focus.

View a sample and build your own report here.

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