The Innovation Clusters Map was created by the Department for Science, Innovation and Technology in partnership with Cambridge Econometrics, the Data City and the Innovation Caucus. The map is experimental and there are data limitations. However, it provides the most comprehensive picture to date of firm-level innovation activity across the UK and, for the first time, a shared understanding of the innovation clusters that exist across the UK.
Defining and mapping clusters
Clusters are spatially concentrated groups of firms, research capabilities, skills, and support structures in related industries that benefit from spillovers associated with agglomeration.
Our approach sets four criteria to test whether aggregations of firms were innovation clusters. We used the best available data to find evidence that the groups of firms were:
- RD&I-active,
- Spatially co-located,
- Engaged in related activities, for example within the same value chain or producing similar products,
- Actively engaged in collaboration on public funded R&D projects, between organisations in the same group.
Typologies of clusters
For this project, we include only clusters which demonstrate evidence of meeting the criteria “RD&I-active” – thus, we refer to all clusters as innovation clusters. The innovation clusters are then labelled according to the following cluster types:
- “Diverse” innovation clusters – Co-located groups of firms that do not specialise in the same industrial sectors, and we were unable to identify solid evidence of collaboration within the cluster (meet criteria 2 but not 3 or 4).
- “Specialised” innovation clusters – Co-located groups of firms that specialise in the same industrial sectors, but we were unable to identify solid evidence of collaboration within the cluster (meet criteria 2 and 3).
- “R&D Collaborating” innovation clusters – Co-located groups of firms where solid evidence of collaboration within the cluster have been identified (meet criteria 2 and 4).
- “Dispersed” innovation communities – Groups of firms where evidence of collaboration within the cluster have been identified, but are spatially dispersed, i.e., not Co-located in a single place (meet criteria 4). These are best conceived of as collaboration “communities”.
Given the best available data at the time of this report, collaboration is evidenced by joint work on government (research council) funded R&D projects. Supplementing the evidence with other types of collaboration such as patent applications is for future research.
Table 1 Four types of clusters and the criteria they satisfy
| Innovation Clusters | RDI-active | Co-located | Specialised | Internally Collaborative |
|---|---|---|---|---|
| Diverse | ✔ | ✔ | ✘ | ✘ |
| Specialised | ✔ | ✔ | ✔ | ✘ |
| R&D Collaborating | ✔ | ✔ | ✔/✘* | ✔ |
| Dispersed | ✔ | ✘ | ✔/✘* | ✔ |
*While R&D Collaborating clusters and Dispersed communities may or may not meet the Specialised criterion, due to their cross-sector collaborations they warrant further exploration for their unique relationships.
Data and methodology
While taking this challenge on board, the research team acknowledges that data is never comprehensive and it is not possible to identify “all” innovation clusters with the current data. Instead, we took a pragmatic approach and identified as many clusters as possible with the available data. The clusters we present in the tool reflect data available in January 2023. We combined 5 different datasets to classify groups of organisations based on the four criteria described above:
- IDBR – 2018 Inter-Departmental Business Register dataset: 3.1 million business sites classified by 32 broad Standard Industrial Classification (SIC) code sectors.
- RTIC – 2023 Real-Time Industrial Classifications dataset: 5 million firms classified by 46 emerging RTIC sectors identified by The Data City.
- IUK – Innovate UK dataset: 46 thousand collaborators and 111 thousand project funding applications from innovative firms and other organisations from Sep 2016 to Jan 2023.
- UKRI – UK Research and Innovation dataset: 24 thousand funded project from Sep 2016 to Jan 2023.
- MAKG – Microsoft Academic Knowledge Graph dataset: 239 million scientific publications with co-authors.
All business sites are allocated an even share of the business’s total estimated turnover and employee count, weighted by employee count on each site if the data is available. Funding received is allocated equally across all collaborators and then allocated equally across all sites unless specified by the dataset.
To utilise these datasets in a comprehensive manner, we chose to use two complementary approaches. The “Spatial approach” identifies spatial groupings of business sites with similar economic activities around the country using IDBR and RTIC data. The “Network approach” instead identifies network communities of collaborative firms using Innovate UK funding co-applications data.
In total, 3,443 innovation clusters were identified in the UK (of 10 or more firms). There are 429 R&D Collaborating clusters, 2,901 Specialised clusters, 7 Diverse clusters, and 106 Dispersed communities.
Acknowledgments
We are grateful to the many people inside and outside of government who helped to develop the map, including members of the Business Innovation Forum and related ministerial groups.
We would particularly like to thank our steering group of academic and analytical experts for their guidance throughout this process. This included representatives from a range of government departments; UK Research and Innovation; Innovate UK; GO-Science; Intellectual Property Office; and the following researchers of the UK innovation system:
- Prof. Simon Collinson, Director of the West Midlands Regional Economic Development Institute, University of Birmingham
- Prof. Richard Jones, Chair in Materials Physics and Innovation Policy and Vice-President for Regional Innovation and Civic Engagement, University of Manchester
- Dr Kostas Kollydas, Research Fellow, City-REDI
- Dr Max Nathan, Associate Professor in Applied Urban Sciences, University College London
- Dr Jen Nelles, Senior Research Fellow, Innovation Caucus
- Tomas Coates Ulrichsen, Director of the Policy Evidence Unit for University Commercialisation and Innovation, University of Cambridge
- Dr Anna Valero, Distinguished policy fellow and Director of the Growth Programme at the Centre for Economic Performance, London School of Economics.
- Dr Jorge Velez, Principal Economist, National Centre for Universities and Business
For further information, please see the full research report.