Tech Map London

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Data-driven ecosystem mapping project documenting London's science and technology sectors through combining business registries, surveys, and advanced analytics to provide evidence-based insight into innovation networks and geographic conce
What are the main aims and objectives?

The primary objectives of Tech Map London are to understand London's science and technology sector scale, structure, and dynamics through rigorous data collection and analysis; to identify where innovation is concentrated geographically within London and what sectors constitute the ecosystem; to map the networks and relationships driving startup growth and ecosystem effectiveness; and to provide evidence-based insight informing London's digital economy strategy and policy priorities. More specifically, the project aims to address identified knowledge gaps in understanding entrepreneurial ecosystems by providing comparative, timely data about London's tech sector; to identify hidden networks and influential actors within London's technology sector that drive startup success; to track network evolution over time enabling understanding of how ecosystems develop; to answer critical policy questions such as "Who are the most influential connectors?", "How have networks developed over time?", and "What distinguishes successful from unsuccessful startups?"; and to provide interactive visualizations enabling policymakers, investors, and entrepreneurs to explore data and answer their own ecosystem questions. The project also aims to contribute to broader innovation policy knowledge by demonstrating new methodologies for ecosystem mapping that could be applied in other cities and contexts globally.

How does the program work?

Tech Map London operates through a multi-stage, data-driven research methodology combining multiple complementary data sources and analytical approaches to create comprehensive ecosystem understanding.

Data Collection Methods:

The project combined multiple data sources including direct surveys and interviews with over 600 tech founders in London and broader Golden Triangle (London, Oxford, Cambridge) region, business registry data from Companies House, online platform data from LinkedIn and other professional networks, social media data (Twitter feeds), company website descriptions, and big data sources documenting company relationships and network patterns.

Advanced Analytical Techniques:

The project employed sophisticated analytical methods including natural language processing (NLP) to analyze company descriptions and identify technology specializations, network science analysis to visualize collaboration networks and identify gaps between communities, company directorships analysis from Companies House data, and machine learning approaches to classify companies and identify patterns in ecosystem structure.

Network Visualization:

The project produced interactive network maps showing web of connections between technology companies, with connections representing mentorship, investments, inspirations, former employee spinouts, and serial entrepreneurship. These interactive visualizations enable users to explore relationships, identify influential actors, and understand ecosystem structure without requiring specialized data analysis skills.

Geographic and Sectoral Mapping:

The mapping exercise identified geographic concentrations of innovation within London and categorized companies by emerging sectors (adtech, fintech, healthtech, deep tech, etc.), enabling understanding of sectoral clusters and geographic distribution beyond traditional indicators.

Policy-Focused Outputs:

Research findings are translated into policy-relevant reports and visualizations providing actionable insight for policymakers. For example, the "Mapping London's Science and Technology Sectors" report documented that London's science and technology sectors comprise 90-95,000 businesses, employ approximately 700,000 people, and represent approximately 15% of London's economy.

Database and Platform Development:

The London Tech Census platform (built on Companies House data) enables science and technology businesses to classify themselves within emerging sectors (adtech, fintech, etc.) and update their own records, creating evolving database rather than static snapshot.

 

What is the overall cost?

No information available. 

How was it implemented?

Tech Map London emerged from growing recognition that London's technology ecosystem was substantial but poorly understood quantitatively. While London was known globally as a leading tech hub (particularly around East London's "Silicon Roundabout"), comprehensive data about sector scale, network structure, and economic contribution remained limited.

The Greater London Authority, in partnership with Nesta (UK innovation research organization) and Endeavor Insight (international entrepreneurship research organization), initiated comprehensive mapping research to understand London's tech ecosystem through data-driven methodologies. This partnership brought together policy expertise (GLA), advanced analytical capacity (Nesta), and international ecosystem research experience (Endeavor Insight).

Nesta and Endeavor Insight, having already successfully developed tech mapping methodology in New York City, adapted and expanded their approach for application in London and the broader Golden Triangle region (London, Oxford, Cambridge). The expanded methodology involved designing survey instruments, developing data collection protocols, and establishing analytical frameworks for ecosystem analysis.

Research teams conducted extensive fieldwork including interviews with over 600 tech founders throughout London and surrounding regions, gathering detailed information about founder backgrounds, company strategies, funding sources, networks, and ecosystem perceptions. This primary research was combined with secondary data collection from Companies House registries, social media platforms, and online business directories.

Following data collection, analytical teams applied sophisticated methods including network science, natural language processing, and machine learning to identify patterns, relationships, and ecosystem structure. Interactive visualization tools were developed enabling intuitive exploration of complex network relationships and ecosystem data.

In October 2015, the Mayor of London released the Tech Map, presenting initial findings about London's science and technology sectors including the key statistic that London's science and technology industry comprises 90-95,000 businesses employing approximately 700,000 people. This launched public awareness and policy integration of the mapping findings.

Findings from Tech Map London were integrated into broader Greater London Authority digital strategy and policy frameworks. By 2018, the Mayor launched "Smarter London Together"—a comprehensive roadmap incorporating mapping insights and establishing framework for ongoing ecosystem support and data-driven policy.

What impact has been measured?

Tech Map London achieved it's goal of providing the first comprehensive, data-driven quantification of London's science and technology sector, establishing that the sector comprises 90-95,000 businesses employing approximately 700,000 people, representing approximately 15% of London's economy.

The broader impact of this endeavour has not been assessed. 

What lessons can be learned?

  • Advanced data science enables unprecedented ecosystem insight: Tech Map London demonstrated that combining traditional surveys with big data sources, natural language processing, network analysis, and machine learning can reveal ecosystem structure and dynamics with unprecedented precision and timeliness, validating investment in sophisticated analytical approaches over conventional survey-only methodologies.

  • Comparative analysis requires standardized methodologies: The Nesta/Endeavor Insight approach of replicating New York methodology in London enabled comparative insights not possible with city-specific approaches, suggesting that standardized ecosystem mapping methodologies across cities enable meaningful benchmarking and learning.

  • Interactive visualizations enable broader engagement: The development of interactive maps allowing policymakers, investors, and entrepreneurs to explore data and answer their own questions increased relevance and utility compared to static reports, demonstrating that data presentation format significantly influences policy impact.

  • Evidence-based policy integration takes time: The lag between initial Tech Map release (October 2015) and integration into major policy framework (Smarter London Together 2018) demonstrates that evidence-based policy integration requires sustained engagement and multi-year effort rather than immediate policy adoption.

  • Network mapping reveals hidden structure: The project revealed hidden networks and influential actors not evident from conventional economic indicators, suggesting that network analysis provides complementary perspective to traditional sectoral and geographic analysis in understanding ecosystem dynamics.

  • Academic-practitioner partnerships enable methodological innovation: The partnership between academic researchers (Nesta, Endeavor Insight) and policy practitioners (GLA) enabled methodological innovation that neither could achieve independently, suggesting value of sustained collaborative research partnerships.

  • Big data sources enable timely analysis: By combining Companies House registries with real-time social media and online platform data, the project demonstrated ability to track rapidly-changing ecosystem dynamics more timely than historical data would enable, suggesting investment in dynamic data infrastructure improves policy responsiveness.

  • Founder interviews provide irreplaceable qualitative insight: While big data enables scale and precision, interviews with 600+ founders provided qualitative understanding of founder motivations, challenges, and network perceptions not extractable from quantitative data alone, suggesting hybrid methodologies combining big data with qualitative research are most valuable.

  • Limited direct policy outcome assessment constrains learning: The absence of published evaluation documenting which specific Tech Map insights drove policy decisions, which policy outcomes resulted from Tech Map recommendations, or whether implementing Map-based recommendations achieved intended effects limits evidence-based learning about ecosystem mapping utility for policy.

  • Sustained funding required for dynamic mapping infrastructure: The challenge of maintaining London Tech Census as dynamic platform (rather than static snapshot) requires ongoing investment and institutional commitment, suggesting that one-time mapping projects deliver less lasting value than institutionalized, evolving mapping infrastructure.

  • Methodological replicability enables global knowledge-building: Nesta's subsequent application of Tech Map methodology to creative industries mapping (Creative Nation) and other sectors demonstrates that validated methodologies can be systematically applied to understand diverse innovation ecosystems, suggesting portfolio approach to ecosystem mapping increases overall knowledge utility.

Notes + Additional Context

Mapping City Resources (excerpt about this type of policy approach in Nesta's Idea Bank for Policymakers):

When it comes to digital startups, maps can be helpful to understand where such firms are physically located – especially if their business activities otherwise leave little obvious sign. Maps can help identify clusters, their structures and their unique assets, allowing policymakers to proceed from a better-informed position. Importantly, however, it should be realised that ‘digital’ startups often do not reveal themselves through conventional business data – many standard industry classification schemes, for instance, have failed to keep pace with digital innovation and often obscure more than they reveal about the nature of local businesses. The most informative maps may therefore need to use novel data-gathering techniques and alternative metrics.

Maps also serve a wider purpose, showcasing an ecosystem and helping different elements locate each other. Presenting city-level ecosystem data in map format is a visually appealing, easy to navigate and relatively cost-effective method of information sharing. Through a combination of visualizations, charts and table data, platforms can showcase the contributions being made in each sectoral cluster. Well-designed platforms allow the data to be dynamically linked, making real time updates possible. As well, filtering mechanisms which allow the user to only extract the data they need, making the map embeddable, and any analysis tools available, are an added bonus. 

 

CURATED BY

Chris Haley

Head of Research
United Kingdom