How to identify the most deprived neighbourhoods within a larger region

For charities, the Index of Multiple Deprivation (IMD) is one of the most widely used tools for evidencing need. This guide explains how you can identify which neighbourhoods in your service area are most deprived.

IMD is a neighbourhood measure

The Index of Multiple Deprivation  is calculated at the level of Lower Layer Super Output Areas (LSOAs), small neighbourhoods of around 1,500 residents. Each LSOA is ranked relative to all others in England.

This means the IMD is most powerful when used to understand how individual neighbourhoods within a larger area are performing, rather than to assign a single label to an entire local authority or county.

For charities, this matters because services are rarely delivered evenly across an area. Need is often highly localised, and demonstrating that you understand this strengthens your credibility with funders. 

We have an additional guide on using the IMD within funding bids here.

Understanding the distribution of deprivation

Once you move to a neighbourhood-level perspective, a more important question emerges: how is deprivation distributed across the area?

Two areas with similar overall profiles can look very different when broken down:

  • One may experience moderate deprivation spread widely across many neighbourhoods
  • Another may have acute pockets of deprivation within an otherwise less deprived area

These patterns have very different implications for service design. Widespread deprivation may require broad, accessible provision, while concentrated deprivation may call for targeted, place-based interventions.

Communicating deprivation clearly using deciles

To make distribution easy to understand, deprivation is often grouped into deciles, dividing all neighbourhoods in England into ten equal groups from most to least deprived.

Looking at the proportion of LSOAs in each decile within your area provides a clear and accessible way to communicate need.

For example, you might show that:

  • a significant share of neighbourhoods fall within the most deprived 10% or 20% nationally, or
  • deprivation is spread across the middle deciles rather than concentrated at the extremes

This approach works well in funding applications because it translates complex data into a simple narrative about scale and concentration. It also allows funders to quickly benchmark your area against others.

We have a full resource explaining IMD deciles here. 

A step-by-step approach to identifying the most deprived neighbourhoods

For charities, the most useful application of IMD data is often not comparing every area against every other one, but identifying which neighbourhoods within a place are experiencing the greatest disadvantage.

Start by identifying all the LSOAs within the local authority or your region of interest. You can access IMD data online and filter the data by local authority. “File 2: Domains of Deprivation” includes information on the IMD and each of the individual domains.

The next step is to sort the data by value for each of the measures that you are interested in to highlight the most deprived neighbourhoods in each case.

Then summarise the pattern across the authority. For example, you could report:

  • the proportion of neighbourhoods in decile 1
  • the proportion in deciles 1 and 2
  • how the authority compares with regional or national averages

This is often the most useful way to describe deprivation at local authority level, because it reflects variation within the area rather than reducing it to a single figure.

A more rounded picture of need

While IMD is a powerful starting point, it is most effective when combined with other data to build a fuller understanding of communities.

Looking at the distribution of deprivation within areas, alongside social, economic and demographic data, allows charities to develop a more nuanced picture of need.

This might include:

  • Population characteristics (household composition, age profiles, ethnicity)
  • Economic indicators (unemployment levels, household debt, benefit claimants)
  • Community insight (crime, health, housing conditions)

This is often easier said than done as relevant data is often spread across multiple sources, geographies, and formats. Bringing this together into a clear narrative can be time-consuming and is a common challenge when developing funding bids.

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