How to Map the Startup Ecosystem in Your Region

Regional economic development is becoming increasingly data-led.

In October 2025, the Department for Science, Innovation and Technology (DSIT) launched an updated UK Innovation Clusters Map, designed to identify concentrations of innovative businesses, research institutions, and infrastructure across the country.

Its methodology reflects a broader shift in how regional innovation is understood. Mapping an ecosystem isn’t simply about finding startups on a map. DSIT’s updated approach examines networks of firms, public investment, and enabling infrastructure to build a more detailed picture of what drives innovation.

As combined authorities take on greater responsibility for local growth and universities play a bigger role in regional innovation, that kind of intelligence is becoming increasingly important.

A better approach is to treat startup ecosystem mapping in the UK as an ongoing intelligence exercise, creating a dataset that can be interrogated, benchmarked, and refreshed as the ecosystem evolves. Here’s how you do it.

Why regional startup ecosystem mapping matters

Understanding a regional ecosystem isn’t simply an exercise in counting startups. Done properly, it can help policymakers, universities, and economic development organisations make better decisions about where support and investment are needed.

Devolved policymaking and the regional growth mission

Greater devolution means more decisions about economic development are being made regionally.

That creates a corresponding need for robust regional economic intelligence in the UK. Combined and mayoral authorities need to understand their local strengths, emerging sectors, and gaps in the ecosystem if they are to prioritise funding effectively.

DSIT describes enhancing local innovation ecosystems as important both for economic growth and addressing spatial disparities. Its Innovation Clusters Map is designed partly to give national and local policymakers the evidence needed to make more targeted interventions.

Making the case for public investment and support programmes

Regional bodies are frequently required to demonstrate why a programme, fund, or infrastructure project deserves investment.

Ecosystem data provides the evidence behind that case.

For example, identifying a growing concentration of life sciences companies alongside rising investment and university spinout activity can provide much stronger evidence for further support than company counts alone.

The same approach can be used retrospectively. Tracking the businesses participating in a support programme can show whether they subsequently raise funding, grow, secure grants, or reach other important milestones.

Benchmarking against peer regions

Is a region producing a large number of spinouts, or does it simply feel that way? Is venture investment growing faster than in comparable areas? Are local startups progressing into scaleups?

Regional startup benchmarking in the UK provides context. Regional differences can be significant: the British Business Bank’s Nations and Regions Tracker 2025 found that while UK equity investment declined slightly in 2024 and deal volumes fell 15%, Scotland, the North West, and East Midlands experienced growth in both deal volume and investment value.

Using consistent company-level data makes it possible to understand how your own region compares on company creation, fundraising, sector growth, innovation activity, and exits.

Attracting investment, talent, and inward investment

Good ecosystem intelligence can also become an inward investment tool.

Being able to demonstrate that a region has a genuine cluster of companies, investors, research institutions, and skilled workers in a particular field makes its proposition more tangible to businesses considering where to locate.

DSIT’s work on innovation clusters makes a similar connection. Its methodology notes that concentrations of specialist expertise and innovation can help attract skilled workers, international investment, and government resources.

Evidencing impact to central government and funders

Central government, research funders, and other funding bodies increasingly expect organisations to demonstrate outcomes rather than simply activity.

Tracking the companies within an ecosystem over time helps show whether businesses are raising investment, increasing headcount, securing grants, scaling, or achieving successful exits.

It also makes it easier to distinguish between overall market movements and changes that may be associated with a particular regional intervention.

What mapping a startup ecosystem actually means

Before deciding how to map the startup ecosystem in your region, you need to decide exactly what you are mapping.

Defining the ecosystem boundary (geography, sector, and stage)

Geography is the obvious starting point, but administrative boundaries do not always reflect economic reality.

A combined authority may want to use its formal boundary because its funding and reporting responsibilities are tied to it. A university might instead be interested in companies within a certain radius of its campuses.

Others may benefit from a hybrid approach that captures the formal administrative region alongside its wider functional economic area.

You also need to define what counts as a startup. Depending on the purpose of the analysis, this could involve company age, size, growth stage, funding history, or a combination of criteria.

The difference between a point-in-time snapshot and continuous monitoring

A traditional ecosystem report provides a snapshot of a region at a particular point in time. That’s useful for public reports and funding submissions, but ecosystems rarely stand still.

Continuous monitoring turns the same exercise into an intelligence function. Rather than rebuilding the dataset every year, teams can monitor new companies, funding rounds, grants, exits, and other changes as they happen. For organisations responsible for combined authority ecosystem intelligence or mayoral authority startup data, that makes regional reporting significantly more useful.

Public reports versus internal intelligence

Not every piece of ecosystem intelligence needs to appear in a published report.

A public-facing report might communicate headline growth, investment trends, and successful clusters. Internally, teams may need considerably more granular information to identify companies for programmes, monitor funding recipients, or investigate emerging sectors.

Starting with the use case helps determine how detailed the underlying dataset needs to be.

The three lenses: companies, capital, enablers

A useful ecosystem map usually combines three lenses, each of which corresponds to one or more of the data layers covered in the next section:

Companies: Which startups and scaleups operate in the region? What sectors are they in? How quickly are they growing?

Capital: Where is funding coming from? Which companies and sectors attract it? Who are the most active investors?

Enablers: Which universities, accelerators, incubators, science parks, corporate partners, and other organisations support the ecosystem?

Together, these create a much richer picture than plotting startup headquarters on a map.

The data layers you need

The three lenses above translate into six practical layers of company and ecosystem data.

Firmographic (company count, size, sector, headquarters versus operating location)

Start with the fundamentals: company location, age, size, and industry.

Headquarters data should be treated carefully. A registered office may be an accountant’s address or located outside the region even when substantial operations take place locally. Understanding both registered and operating locations helps reduce the risk of missing relevant businesses.

Financial (funding rounds, valuations, filed accounts, growth trajectories)

Company counts tell you the size of an ecosystem, but not its momentum.

Funding rounds, valuations, filed accounts, and growth indicators help reveal which companies and sectors are attracting capital and progressing.

They also demonstrate why multiple metrics matter. Beauhurst’s The Deal 2026 found that the amount raised by UK companies increased 3.3% in 2025, while the total number of deals fell 7.9%. At the same time, first-time deals increased 24%. Looking at investment value alone would therefore tell a very different story from looking at deal numbers, stages, and first-time fundraising.

Innovation (patents, Research and Development tax credits, Innovate UK grants, university spinout status)

For regional innovation ecosystem mapping in the UK, conventional company information should be supplemented with indicators of innovation. These might include patents, Research and Development Tax Credits, Innovate UK grants, and university spinout status.

Spinouts are particularly important. The British Business Bank’s Small Business Equity Tracker 2025 found that university spinouts raised £1.9b in equity investment in 2024, representing 17% of UK investment. They also accounted for a record 12% of deals.

These signals can help identify innovative businesses that may not yet have attracted significant external investment or media attention.

Ownership and governance (founders, Persons with Significant Control, investors)

Founders, directors, Persons with Significant Control, and investors reveal who sits behind regional businesses.

This becomes particularly valuable when exploring connections within the ecosystem, such as serial founders creating multiple companies or investors backing several businesses within the same cluster.

Ecosystem infrastructure (accelerators, incubators, universities, science parks, coworking spaces)

Companies do not develop in isolation. Universities, accelerators, incubators, science parks, coworking spaces, investors, and corporate partners all contribute to the environment in which startups form and grow.

For university innovation ecosystem mapping, this layer is particularly important because it can demonstrate the institution’s role beyond its own spinout portfolio.

Connections (investor networks, university links, corporate partnerships)

The final layer is the relationships between these different actors. Which investors repeatedly back local companies? Which startups emerged from universities? Which accelerators appear in the histories of successful scaleups? Where are companies collaborating with universities or established businesses?

Mapping these relationships helps move from an inventory of organisations towards a picture of how the ecosystem actually functions.

A framework for mapping a regional startup ecosystem

So, how do you turn these different datasets into something useful?

Step 1: Define the boundary (administrative geography, functional economic geography, or hybrid)

Start with the question the mapping exercise needs to answer.

If it supports statutory reporting or funding allocation, an administrative boundary may make most sense. If the aim is understanding a technology cluster, a functional economic geography could be more useful.

Document this definition so that the methodology remains consistent when the exercise is repeated.

Step 2: Build the company universe using company data

Next, identify the companies that meet your geographic and startup criteria.

This is the foundation of the map. Rather than starting with a list of well-known businesses and adding companies manually, work from a comprehensive private company dataset and filter down. That reduces selection bias and makes it easier to discover companies that haven’t yet attracted significant press or investment.

Step 3: Layer in funding, growth, and outcome data

Add company performance to your initial universe. Useful indicators might include fundraising, headcount growth, turnover, grants, acquisitions, initial public offerings (IPOs), and company status.

Negative outcomes matter too. Dissolutions and unsuccessful companies provide important context when assessing the health of an ecosystem.

Step 4: Map the enablers (accelerators, universities, investors, corporate partners)

Identify the organisations surrounding those companies. Depending on the region, this could include universities, accelerators, incubators, investors, local authorities, science parks, research institutes, and major corporate partners.

Look beyond their presence to their involvement with companies in your dataset.

Step 5: Identify clusters and specialisations using sector classification and buzzwords

Standard industry classifications alone rarely capture emerging sectors particularly well.

DSIT’s own Innovation Clusters Map illustrates this challenge. Its methodology combines Standard Industrial Classification data with sector lists and more dynamic Real-Time Industrial Classifications to identify activity in emerging areas such as quantum technology, advanced materials, and robotics.

Combining sector classifications with more granular company descriptions and buzzwords can similarly reveal concentrations in areas such as clean energy, artificial intelligence, or advanced materials.

Look at several indicators together. A genuine cluster might show not only a high number of relevant companies, but also investment, research activity, spinouts, and specialist infrastructure.

Step 6: Benchmark against peer regions

Numbers without context can be misleading. The British Business Bank’s Nations and Regions Tracker 2025 also found that London’s share of UK equity investment fell from 73% in 2020 to 61% in 2024, while several regions recorded growth. But even apparently impressive regional investment figures need interrogating.

Beauhurst’s analysis of the regional investment gap provides a useful example. In Q3 2025, Edinburgh-based Fidra Energy raised £445m. That single deal represented almost 80% of Scotland’s total investment value for the quarter, while the actual number of Scottish deals fell 13%. A headline total could therefore suggest a very different trajectory from the underlying ecosystem.

Select comparable regions and apply the same methodology to each. Comparisons could include startups per capita, deal numbers and value, spinout formation, high-growth company density, sector concentration, or the proportion of businesses progressing to later funding stages.

Using the same underlying dataset and definitions is essential for meaningful regional startup benchmarking in the UK.

Step 7: Publish, refresh, and rebuild the picture on a regular cadence

Finally, decide how the map will be maintained. Core metrics might be reviewed quarterly, with a more comprehensive annual report used to examine longer-term trends.

The important thing is to avoid rebuilding the entire exercise from scratch each time. Maintain the underlying company universe and update it as companies form, grow, move, fundraise, exit, or dissolve.

What to include in a regional startup ecosystem report

The exact metrics will depend on the purpose of your report, but there are several areas worth covering.

Headline company count and year-on-year change

Show the overall number of startups and scaleups, but put that number in context. How has the population changed from the previous year? How many companies are new to the dataset, and how many have left it?

Sector distribution and cluster identification

Break the company population down by industry and identify areas of concentration. Where possible, look beyond broad classifications to identify emerging specialisms that might otherwise be hidden inside larger sectors.

Capital raised (over time, by stage, by sector)

Show both the value and number of funding rounds, preferably over several years.

Breaking this down by stage and sector helps distinguish between broad-based investment growth and totals driven by a handful of exceptionally large deals.

Top investors and investor concentration

Identify which investors are most active in the region and where they are based.

Investor concentration can also reveal whether an ecosystem depends heavily on a small group of funders or has access to a more diverse capital base.

University spinout activity and knowledge-exchange indicators

Track the number of spinouts, their sectors, investment activity, and growth.

Universities may also want to examine connections between their institution and the wider business ecosystem, including partnerships and other knowledge-exchange activity.

Accelerator and incubator involvement

Identify which programmes local companies have participated in and whether particular accelerators appear repeatedly among successful businesses.

This can help regional bodies understand which pieces of support infrastructure are most embedded in the ecosystem.

Exit outcomes and survivorship

Don’t only measure success stories. Acquisitions, IPOs, and other exits are important, but so are company dissolutions and failures. Including both helps avoid survivorship bias and creates a more realistic picture of company progression.

Notable companies and case studies

Finally, bring the data to life. Case studies can demonstrate what broader trends look like at company level, whether that is a university spinout raising its first institutional round or a startup progressing through the region’s support infrastructure into a scaleup.

Common pitfalls in regional ecosystem mapping

Even a data-rich ecosystem map can give a misleading picture if the methodology is inconsistent.

Boundary that’s too narrow (missing companies that operate in the region but are registered elsewhere)

Relying solely on registered headquarters can exclude companies with meaningful operations in the region.

Consider operating locations alongside registered addresses and document how each is treated within the methodology.

Boundary that’s too broad

The opposite problem can occur when boundaries extend so far that meaningful local characteristics disappear.

Choose a geography that reflects the purpose of the analysis rather than simply maximising the number of companies included.

Relying on stale data or one-off snapshots

Company ecosystems change quickly. Funding rounds close, startups relocate, new companies emerge, and existing businesses change direction. A static dataset gradually becomes less representative of what is happening on the ground.

Missing dissolutions and exits

Tracking only companies that continue to operate creates an artificially positive picture.

Include dissolved companies, acquisitions, and other outcomes so that the analysis reflects the full company journey.

No benchmark or peer comparison

A number rarely tells you whether performance is good or bad in isolation.

Benchmarking against comparable areas makes it possible to distinguish genuinely unusual regional performance from wider UK trends.

Publishing once, then not refreshing

A regional ecosystem report should ideally be an output of the intelligence process rather than the intelligence process itself.

Maintain the underlying dataset so that the next report updates the existing picture rather than starting again.

How Beauhurst powers regional startup ecosystem mapping

Creating this picture manually can require combining information from dozens of sources. Beauhurst brings data on the UK’s private company ecosystem together in one place, providing a common foundation for regional analysis.

Every UK private company in one dataset: the full ecosystem universe in view

Rather than beginning with a hand-built list of prominent startups, teams can start from the wider private company population and narrow it using criteria relevant to their region and objectives.

This helps reduce selection bias and surface companies that haven’t yet drawn significant media or investor attention.

Regional filters and geographic search

Geographic search enables users to identify businesses within specific areas and build company populations aligned with the region they are studying. It supports everything from Local Enterprise Partnership analysis to combined authority and university mapping.

Live industry classification and buzzwords for cluster identification

Detailed industry classifications and buzzwords help teams look beyond broad sectors and identify emerging areas of specialisation. That’s particularly useful when mapping technologies and markets that are poorly represented by traditional industry classifications.

Funding, growth, and outcome data at company level

Company-level fundraising, financial, and growth information makes it possible to move beyond startup counts and investigate how businesses within the ecosystem are developing.

Tracking exits and other company outcomes also provides a more balanced view of ecosystem performance.

University spinout, Innovate UK grant, and Research and Development tax credit tracking

Innovation indicators can help identify companies connected to universities, government support, and Research and Development activity.

For universities and public-sector organisations, these provide another way of understanding where innovation is taking place and how companies progress afterwards.

Custom Collections and refresh workflows for continuous monitoring

Once the relevant company universe has been identified, custom Collections can be used to maintain groups of businesses and revisit them as new information becomes available.

This shifts the exercise away from one-off mapping and towards continuous ecosystem intelligence.

BeauhurstImpact (the product line built for government, universities, and regional bodies)

BeauhurstImpact regional mapping gives universities, government organisations, and regional bodies a data foundation for understanding the companies, investment, and innovation activity within their ecosystems.

Rather than piecing together Companies House records, funding announcements, grant databases, and individual company research every time a report is required, teams can build and maintain a repeatable picture of their region.

That can support combined authority ecosystem intelligence, university strategy, programme evaluation, inward investment, and reporting to funders and central government.

Where to begin

A startup ecosystem map works best as a living dataset, not a one-off report. Start by defining your boundary and building the company universe. Once that’s in place, layer in funding, growth, and innovation data, map the enablers around it, and benchmark against peer regions.

With a consistent methodology and reliable company data, regional bodies can move away from expensive one-off snapshots and towards an intelligence function that develops alongside the ecosystem itself, spotting emerging clusters earlier and providing stronger evidence of what’s actually driving regional growth.