Local Deprivation Explorer
In this interview, we speak to Rob Fry (Head of Enhanced Digital and Data Insights Projects, Digital Directorate, Ministry of Housing, Communities and Local Government).
We discuss the work behind the Local Deprivation Explorer, which explains and maps deprivation patterns across England, sourcing from the 2025 English Indices of Deprivation (IoD25).
The Indices of Deprivation rank over 33,000 small areas across the country by levels of deprivation. It’s one of the most important and widely used datasets and is used to shape policy, guide funding and inform decisions from a wide range of users.
Why was it created?
The Local Deprivation Explorer was developed to provide a clear and accessible visual overview of deprivation across England, bringing together a wide range of datasets in one place.
The Explorer allows users to explore and understand the data at the Lower Super Output Area (LSOA) level (areas with an average population of 1,600 residents), showing how deprivation varies across small areas, and highlights differences across multiple indicators.
Using the 2025 English Indices of Deprivation dataset, it demonstrates spatially how deprivation varies across England, allowing users to compare regions and explore patterns across different domains, using the interactive map.
It was designed to help people quickly access and understand this information, through the accompanying area profiles – which were designed to help guide informed interpretations of the data.
Who was it created for?
We developed the Explorer with a wide range of users in mind, providing access to the depth of IMD data, so it was important to create a joined-up experience that could meet different needs – an exploratory map, area profiles and data downloads.
For example, local government users can use it to understand deprivation patterns in their areas and support policy decisions.
Charities and campaign groups can use the tool to support their work, helping to identify where funding is most needed and strengthening their case when advocating for funding.
Members of the public might use the Explorer for various reasons, from simply exploring their local area to informing decisions such as relocating.
Tell us about the data
The data consists of the updated English Indices of Deprivation (IoD25), which was released in October 2025.
The IoD25 uses LSOAs from the 2021 Census to measure deprivation at a small area or neighbourhood level. Seven domains of deprivation are used within this data to measure deprivation: income, employment, education, health, crime, barriers to housing and services and living environment, as well as two additional domains on income deprivation affecting children and old people. You can find out more about the methodology within the technical report, published alongside the IoD25.
What steps did you take to ensure the data was accessible to a wide range of viewers?
Because we started working on this project before the release of the IoD25, we began by exploring previous iterations of IoD data using tools like QGIS and Excel to identify how deprivation varies across areas. This helped us decide how these patterns should be visualised and explained.
For the map we considered how users would interact with the map (such as zooming, hovering, and clicking) and used this to develop a map where patterns are quickly visible, while more detailed information (such as the domains driving deprivation) can be revealed through interactive features.
We also tested different mapping methods, combining choropleth and dasymetric maps to better reflect population distribution. We tested the whole experience with a range of users , taking onboard feedback to ensure the website was intuitive and effective as a tool for exploring and understanding the data.
To create the online map, we used a combination of SvelteKit and MapLibre. We also used Tippecanoe to process the tiles in the map as well as Ordnance Survey open zoomstack tiled data for the background mapping layer.
Why did you choose to present the data in this way over other approaches?
We made several design choices to ensure the Explorer was clear and easy to understand.
To avoid overemphasising large land areas, we used a dasymetric style approach, shading buildings more heavily than open land to better reflect where people actually live.
We also included a colour scale, using darker shades to indicate higher levels of deprivation and lighter shades for lower levels. To add even more detail, we ensured colour wasn’t the only way to interpret the data by providing written descriptions on deprivation data when specific areas are selected.
We also included a side panel that breaks down the different domains of deprivation, if selected, helping users understand what factors may be driving deprivation in a particular area.
To keep the profiles as accessible as possible, we used simple, plain language throughout and simple maps and charts to aide interpretation and included clear explanations of key terms, such as what an LSOA is.
What impact has the visualisation had in research, policy or other contexts?
Since the launch of the Explorer, it has recorded approximately 400,000 sessions in the first seven months, showing strong engagement with the tool.
We’ve seen it being used in a range of practical contexts, particularly to support funding decisions. For example, charities have used the Explorer to help target funding at an LSOA level, and schools have drawn on the tool when applying for grants.
Local government has also made use of the map to better understand deprivation patterns in their areas and inform decisions about where to invest resources and support.
How else might this approach or data be used?
There are opportunities to combine the map with other types of geographical data, helping to build a deeper understanding of what drives deprivation and provide richer insights.
What steps can others take to try this visualisation technique?
We are developing a publicly available component library in SvelteKit that anyone can use. It enables users to apply similar techniques developed within our government department to create interactive content - including maps and data visualisations.
To finish with, what’s your one top tip for geographers looking to visualise data in this way?
Don’t be put off by technical tools and methods used. If you’re interested in data visualisation, follow what excites you and be willing to experiment. There’s a huge amount of information and support available, so keep exploring, and don’t be afraid to try new things.