Digital economy in the UK: an evolutionary story

Emmanouil Tranos

University of Bristol, Alan Turing Institute
, @EmmanouilTranos,




  • Map the active engagement with the digital
  • Over time, early stages of the internet
  • Granular and multi-scale spatial perspective


  • Understand how the adoption of new technologies evolves

  • Guide policies for deployment of new technologies

  • Predictions of introduction times for future technologies (Meade and Islam 2021):

    • Network operators

    • Suppliers of network equipment

    • Regulatory authorities

Technological diffusion

Spatial diffusion processes

  • As in temporal diffusion models, an S-shaped pattern in the cumulative level of adoption

  • A hierarchy effect: from main centers to secondary ones – central places

  • A neighborhood effect: diffusion proceeds outwards from innovation centers, first “hitting” nearby rather than far-away locations (Grubler 1990)

Hägerstrand (1965): from innovative centers (core) through a hierarchy of subcenters, to the periphery

Internet geographies

  • Map internet infrastructure

    • backbone (Malecki 2002; Tranos 2013)

    • last mile (Riddlesden and Singleton 2014; Budnitz and Tranos 2022)

  • Social media and user generated content

    • Here and now effect (Crampton et al. 2013)

Because new digital activities are rarely—if ever—captured in official state data, researchers must rely on information gathered from alternative sources (Zook and McCanless 2022).

Web data

Web data: The Internet Archive

  • The largest archive of webpages in the world
  • 273 billion webpages from over 361 million websites, 15 petabytes of storage (1996 -)
  • A web crawler starts with a list of URLs (a seed list) to crawl and downloads a copy of their content
  • Using the hyperlinks included in the crawled URLs, new URLs are identified and crawled (snowball sampling)
  • Time-stamp

Web data: The Internet Archive