Data has traditionally been used as a tool of management and control. Nowhere is this more visible than in the measurement of deprivation.
The Index of Multiple Deprivation (IMD) is one of the most consequential datasets in England. It shapes billions of pounds of public and private investment. It informs decisions in education, healthcare, insurance and commercial development. It sits behind funding allocations, regeneration strategies and policy interventions at every level of government. As our Digging into the Index of Multiple Deprivation event made clear, it is also one of the most misread.
We brought together the civil servant who led the 2025 publication, researchers from the University of Manchester’s Spatial Policy and Analysis Lab, analysts from GMCA and Liverpool City Region, and a room full of practitioners using this data in their daily work. The conversations that followed were an insight into some of the challenges and opportunities of working with the IMD and what it says about the communities in which we live.
Cities do not behave like spreadsheets.
As Ash Amin and Nigel Thrift argue, the modern city cannot be fully conditioned. It is complex, messy and constantly evolving. The IMD knows this, even if it cannot always say so. It is not a picture of a place. It is a picture of specific, measurable dimensions of deprivation at a neighbourhood level, carefully constructed, independently produced, and deliberately limited in scope.
Bowie Penney, who led the 2025 release at the Ministry of Housing Communities and Local Government, MHCLG, was clear about what the index is and isn’t. It measures unmet needs and lack of access to resources across seven domains — income, employment, education, health, crime, barriers to housing and services, and living environment, as well as the income deprivation of older people and children. It does not measure affluence. It does not capture everything that makes a neighbourhood difficult to live in. It is not a time series. And not everyone who lives in a deprived area is deprived.
These are not caveats limiting the IMD’s use. They are the honest conditions of any attempt to make complexity legible.
What the 2025 release revealed, across Greater Manchester at least, is a picture of persistent and uneven deprivation. There are areas in Manchester and Oldham that saw their relative rank positions worsen between the 2019 and 2025 iterations, while Trafford’s improved. The distance between them, in some dimensions, is growing. And yet, as GMCA’s analysis showed, the most and least deprived areas in Stockport sit just three miles apart — a proximity that should give pause to anyone who thinks deprivation maps onto geography in simple ways.
Liverpool City Region presented a different lens: health deprivation as a structural problem, not a local anomaly. Around 44% of neighbourhoods in the city region fall within the most deprived decile nationally on the health domain — more than four times the national rate. When the analysts looked at GP access alongside deprivation, expecting a straightforward correlation, they found something more complicated. 95.6% of Liverpool City Region residents live within a 20-minute walk of a surgery. The health deprivation is real, but its drivers are not simply about proximity to services.
There was discussion around artificial intelligence — whether machine learning could supplement or accelerate the production of area-based indicators, fill gaps in datasets like energy performance certificates, or eventually allow something closer to real-time monitoring of deprivation.
The responses were cautious, rightly so. Garbage in, garbage out, as Bowie put it. The IMD’s authority rests partly on its transparency and independence. A synthetic proxy, however sophisticated, is a different kind of claim. Trust has to be built before it can be borrowed.
But the conversation pointed somewhere important. Some of the most interesting potential lies not in replacing the index, but in addressing what it cannot reach — the public green space that Defra’s data was too incomplete to include, the mobility patterns that assume car ownership, the lived environment indicators that currently require someone to stand on a street corner and look.