PLACE-BASED POLICY Rethinking place-based policy: How uep.org.uk reframes the way we see English towns and cities
 | Matthew is co-Founder of Didobi and Co-Creator of Understanding English Places. He has been involved in data businesses since leaving the army in 1997. He co-founded Didobi in 2018, Income Analytics, a tenant income risk platform, in 2020, and was a founding director of The Local Data Company in 2009. Matthew is a recognised authority and practitioner on the interpretation of data and on the UK’s built environment. Matthew co-authored the 2013 and 2018 Grimsey Reviews and was research partner for Amazon’s ‘Clicks and Mortar’ initiative. He has worked with the Greater London Authority and London Boroughs on evidence bases for night time economies, along with research into night workers. Matthew is Honorary Professor of Practice at University College London and the Chairman of Emu Analytics, a digital twin software company based in London, and is a Board adviser to a new data intelligence start-up, Evaluate Locate, which is also London based. |
Matthew outlines the uses of this dataset tool in developing policies. |
For decades, local government decision makers have wrestled with a familiar dilemma: how to formulate tailored, effective place-based strategies using datasets that were never designed to capture the true identity of a town.
Whether allocating Levelling Up funds, commissioning town centre regeneration schemes, or designing regional transport networks, local authorities often find themselves bound to rigid administrative boundaries—district councils, unitary authorities, or output areas—that fail to reflect how people actually live, work, and move. While much data exists at a strategic or higher authority level, very little exists at a town or place level.
This is why we created Understanding English Places (UEP) at www.uep.org.uk .
Developed through a joint collaboration between data and research specialists Didobi, and demographic analysts Edge Analytics, UEP was created to solve a fundamental challenge in spatial planning: giving local authorities, policy analysts, and regional leaders a granular, standardised, and evidence-led framework to understand every individual town across England.
How the UEP model works
Driven entirely by professionally interrogated open and commercial datasets—including the Census 2021, NHS England, HM Land Registry, and OpenStreetMap—the UEP model operates on a three-stage framework:
Clustering: Places are statistically grouped into 8 distinct typology clusters based on demographic, social, and economic characteristics (e.g., student-heavy university towns, affluent areas with younger families, coastal retirement communities or the London Boroughs)
Size banding: Clusters are disaggregated into 4 size bands, ranging from very small towns (1,000–3,000 residents) to major cities (over 150,000) to account for differing service provisions
The Inter-Relationship Model: Places are scored across 15 indicators to determine their functional relationship with neighbouring areas, classifying them from 'Independent' (hubs that naturally attract jobs and visitors) to 'Dependent'.
Below is an overview of why UEP was built, how its spatial taxonomy breaks down English places, and the novel analytical angles it provides to officers, elected members, communities and businesses.
1. Why UEP was created
Historically, policy frameworks treat towns as static administrative units or bundle them into broad national averages. When central government evaluates economic health, "coastal decline”, or "high street vitality”, local officers are frequently forced to rely on high-level Local Authority District (LAD) metrics.
However, a LAD can contain an affluent commuter hub, an industrial market town, and a struggling coastal resort—all blended together resulting in misleading averages.
UEP was conceived to address three primary market gaps in local government analytics:
Inconsistent town definitions - Local government lacked a single, agreed-upon spatial footprint for what constitutes a "town", distinct from its wider borough or greenbelt
Data fragmentation - Crucial spatial indicators—spanning socio-economics, housing, employment, and demographic trends—were buried in disconnected departmental silos across central bodies and at different geographies
The "One-Size-Fits-None" policy trap: National initiatives often deployed blanket funding criteria across towns that shared a region, but had entirely different structural profiles.
By aggregating, standardising, and visualising multi-layered spatial data (200 datasets to date) at the specific settlement level, UEP acts as a baseline repository for local government. It allows officers to compare apples to apples when benchmarking against peer towns across the nation. A further consideration is that UEP should be easily accessible and understood by all and this is what the user interface has aimed to achieve, as illustrated in the following examples.
UEP user interface examples
Learning from Celtic success
The launch of UEP follows the highly acclaimed success of the Understanding Scottish Places (USP) platform, launched in 2015, and its subsequent Welsh counterpart, UWP.
Welcoming the English model, Professor Leigh Sparks, Chair of Scotland's Towns Partnership, noted:
"In Scotland, local leadership embraced the tool to ground conversations in objective, visual data. England seemed somewhat left behind due to its scale and complexity. UEP steps directly into this space. With 'Pride in Place' currently in vogue, the starting point must be to understand our towns objectively. Without this focus, there is a real danger of misperceptions and misaligned activity. We all need to know ourselves and our places better and UEP provides just the tool to start those conversations in the right place."
2. What places does UEP cover?
UEP covers every place in England with a population of 1,000 or more - which is 3,463 places. This compares to Scotland’s 508 places and Wales’ 307.
The platform maps England’s urban hierarchy by dissecting data across multiple geographic tiers. Rather than forcing users into a single view, UEP structures its place profiles into logical directories that align directly with modern local governance tiers.
While cities have more structured data services, towns often have not, and the importance of addressing towns is critical to the ongoing strapline ‘Save Britain’s High Streets’. This strapline is wrong as it addresses the symptom and not the cause. High streets are a reflection of the wider town, and high street decline is merely a symptom of deeper structural issues in the local economy. The UK Government needs to focus on saving Britain’s towns, and by doing so, will in turn create thriving high streets.
The need to have a better understanding of towns is illustrated by the fact that 63% of the places with a population of 1,000 or more in England reside in these ‘towns’ and not cities.
UEP place breakdown by population
Key geographic categories on UEP:
Places, Towns and Cities A–Z: Granular profiles for individual place across England
Coastal towns directory: A dedicated analytical subset isolating coastal communities to track specific vulnerabilities like seasonal employment, ageing demographics, deprivation and connectivity
Local authority districts and counties: Aggregations that map directly to upper-tier and lower-tier council responsibilities, aiding inter-council co-operation
Combined authorities: Data grouped by Strategic Combined Authority boundaries (e.g., Greater Manchester, West Midlands, West of Yorkshire), enabling mayoral teams to evaluate intra-regional disparities across their footprints
Standard regions: Macro-level regional benchmarking across the nine standard English regions (North West, East Midlands, South East, etc.).
Note. London is the 33 Boroughs only.
3. Understanding towns in ways not available before
What sets UEP apart for municipal planners and economic development teams is not just where it looks, but how it analyses space. The platform introduces several analytical angles that were previously difficult or costly to compile:
Angle A: True functional peers (beyond regional proximity)
Historically, a council in Lancashire evaluating a high street strategy would look at neighbouring Lancashire towns. However, UEP’s data clustering allows planners to identify functional twin towns across the entire country.
For example, a post-industrial town in the North West might share near-identical geo-demographic profiles, health, economic activity status and industry sectors with a former mining town in Cornwall such as St Austell. UEP allows users to benchmark against true structural peers rather than relying solely on geographic neighbours.
Angle B: Unmasking intra-district disparities
District-level data often hides pockets of deprivation or localised economic growth. UEP separates the performance of individual towns from their surrounding district context. This enables policy teams to justify hyper-local interventions—such as targeted spatial regeneration grants or localised public health initiatives—backed by evidence specific to that settlement’s exact boundary.
Angle C: Dynamic typology mapping (e.g., coastal vs. inland hubs)
By grouping settlements into specific functional typologies—such as coastal towns, — UEP gives policy leaders an instant, evidence-backed narrative when applying for national funding streams. Instead of building custom GIS datasets to prove a town’s coastal status or economic isolation, the platform provides pre-packaged spatial intelligence ready for bid inclusion.
Angle D: Strategic alignment for Combined Authorities
With devolution expanding across England, Combined Authorities are tasked with managing complex economic landscapes that include major cities, suburban belts, and rural towns. UEP’s Combined Authority views allow regional strategists to map how smaller towns interact with core urban engines, ensuring that regional transport and housing strategies do not leave peripheral towns behind. For example, in the Cambridgeshire and Peterborough Combined Authority there are 99 UEP places.
4. How local authorities can put UEP into practice
Council department | Practical application of UEP | Key benefit |
Economic development | Benchmarking town centre profiles and performance against national peer clusters | Stronger evidence bases for central government funding bids |
Spatial planning and policy | Evaluating settlement boundaries against local housing needs assessments | More defensive spatial policies in local plan examinations |
Public health and social care | Isolating demographic shifts (e.g., ageing coastal populations) | Targeted service provision in vulnerable communities |
Data stories
Crime
On 27August 2026 on BBC Radio 4 the MP for Middlesbrough, Andy Macdondald, was being interviewed about the levels of crime and antisocial behaviour that had led to the tragic deaths of some young men and two police officers. His reply was that Middlesbrough receives town level police funding, but its crime rate is that of a city. So, to see if this was true, I looked it up on UEP by selecting Middlesbrough (pop 163,000) and choosing the nearby much larger cities of Leeds (pop 566,600), Newcastle upon Tyne (305,800) and Kingston upon Hull (280,400) as benchmarks. As the UEP chart shows, the MP for Middlesbrough was right!
Access to a bank
Over 11 million people live in some 2,657 places without a bank branch or banking hub. Some of the bigger places include Cheshunt in Hertfordshire, and Denton, Tameside. Of these, over 1m people across nearly 500 places are also without a post office. Some of the bigger places include Elworth in Cheshire and Ashley Heath in Dorset.
20% of places in the 15-150,000 size band without a bank/banking hub
80% of places in the 3-15,000 size band
All places 1-3,000 size band.
Access to secondary schools
Some 6.5m people live across 2,657 places without a secondary school. While the majority are small places, 60 places have a population over 10,000, including Shiney Row and Penshaw south of Washington, and Desborough in the East Midlands.
Many of these places have a secondary school nearby, but for nearly 600 places (1.3m people) the nearest secondary school is over 2.5 miles away. In 71 of these places, many of which are across the South West and East of England, children have to travel more than 5 miles, including Edenbridge in Kent and Wellesbourne in Warwickshire.
Evidence-led localism
As local government faces ongoing fiscal pressures and shifting devolution landscapes, the demand for sharp, reliable, and accessible spatial data has never been higher.
Understanding English Places represents a shift away from high-level guesswork and towards true place-based intelligence. By offering a clearer lens on how towns function, how they compare, and where they need support, UEP equips local authorities with the baseline data needed to build resilient, flourishing communities.
To explore your authority's towns or schedule a team demonstration, visit www.uep.org.uk .
Editor note- UEP is not a free service. Matthew and two colleagues have personally funded its creation, but it is not for profit. Costs range from £750 p.a. to £4,750 p.a., depending on the size of organisation and users. UEP is investigating whether the government might fund it.
Matthew states “This is a passion project, as 25 years of working with local authorities has shown me that they do not have access to independent evidence to support their evaluation and monitoring of places, let alone engagement with their communities and businesses. UEP is a starting point on this.”
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