Digital economy in the UK: an evolutionary story


Emmanouil Tranos

University of Bristol, Alan Turing Institute
, @EmmanouilTranos, etranos.info

Contents


Introduction

Aims


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

Importance

  • 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

Web data: The Internet Archive

Our web data

  • JISC UK Web Domain Dataset: all archived webpages from the .uk domain 1996-2012

  • Curated by the British Library

  • Tranos, E., and C. Stich. 2020. Individual internet usage and the availability of online content of local interest: A multilevel approach. Computers, Environment and Urban Systems, 79:101371.

  • Tranos, E., T. Kitsos, and R. Ortega-Argilés. 2021. Digital economy in the UK: Regional productivity effects of early adoption. Regional Studies, 55:12, 1924-1938.

  • Stich, C., E. Tranos and M. Nathan. 2022. Modelling clusters from the ground up: a web data approach. Environment and Planning B, in press.

  • Tranos, E., A. C. Incera and G. Willis. 2022. Using the web to predict regional trade flows: data extraction, modelling, and validation, Annals of the AAG, in press.

Our web data

  • All .uk archived webpages which contain a UK postcode in the web text

  • Circa 0.5 billion URLs with valid UK postcodes



20080509162138/http://www.website1.co.uk/contact_us IG8 8HD

Data cleaning

Unique postcodes frequencies, 2000

level freq perc cumfreq cumperc
(0,1] 41,596 0.718 41,596 0.718
(1,2] 6,451 0.111 48,047 0.830
(2,10] 6,163 0.106 54,210 0.936
(10,100] 2,975 0.051 57,185 0.988
(100,1000] 646 0.011 57,831 0.999
(1000,10000] 62 0.001 57,893 1.000
(10000,100000] 4 0.000 57,897 1.000


  • Websites with a large number of postcodes: e.g. directories, real estate websites

  • Focus on websites with one unique postcode per year

Directory website with a lot of postcodes

Website with a unique postcode in London

Analysis

Mapping website density

Spatial attributes


Two scales:

  • Output areas (c. 200,000)

  • Local authorities (c. 400)


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

  • Moran’s I

  • LISA maps

  • Website density regressions

Website density regressions


\[Website\,Density_{i} = a + \beta Distance\,to\,Place_{i} + e_{i}\]


\(Place\):

  • London, or

  • Nearest city, or

  • Nearest retail centre

\(i\): Output Areas or Local Authorities

Website density regressions


\(\beta\) interpretation:

  • The lower the \(\beta\) is (or the larger the \(|\beta|\) is)…

  • … the larger urban gravitation is for web adoption.


Hierarchy effect: from main centers to secondary ones – central places

  • Gini coefficient

Spatial attributes, a summary

  • Spatial dependency relatively small and constant over time / scales

  • At local scale, consistent hotspots over time

  • More granular analysis reveals hotspots

  • Almost perfect polarisation of web adoption in the early stages at a granular level

  • More equally diffused at the Local Authority level

  • Plateau overtime

  • Distance effect: urban gravitation increases over time and then drops

  • Consistent across scales and definitions of urban

S-shaped diffusion curves

Conclusions

What have we learned?

  • Geography matters: spatial dependency, urban gravitation

  • Some indications of a hierarchical diffusion

  • Granular analysis reveals patterns otherwise not visible

  • Well-established theoretical approaches of diffusion survive even at a granular level

What have we yet to learn?

  • Explain the spatial patterns of fast/slow web adoption

    • Applied the same analysis fo OA

    • Survival regressions (Perkins and Neumayer 2005)

  • Expand our definition of web to websites with # of postcodes > 1

References

Budnitz, Hannah, and Emmanouil Tranos. 2022. “Working from Home and Digital Divides: Resilience During the Pandemic.” Annals of the American Association of Geographers 112 (4): 893–913.
Crampton, Jeremy W, Mark Graham, Ate Poorthuis, Taylor Shelton, Monica Stephens, Matthew W Wilson, and Matthew Zook. 2013. “Beyond the Geotag: Situating ‘Big Data’and Leveraging the Potential of the Geoweb.” Cartography and Geographic Information Science 40 (2): 130–39.
Grubler, Arnulf. 1990. The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport. Physica-Verlag.
Hägerstrand, Torsten. 1965. “A Monte Carlo Approach to Diffusion.” European Journal of Sociology/Archives Européennes de Sociologie 6 (1): 43–67.
Malecki, Edward J. 2002. “The Economic Geography of the Internet’s Infrastructure.” Economic Geography 78 (4): 399–424.
Meade, Nigel, and Towhidul Islam. 2021. “Modelling and Forecasting National Introduction Times for Successive Generations of Mobile Telephony.” Telecommunications Policy 45 (3): 102088.
Perkins, Richard, and Eric Neumayer. 2005. “The International Diffusion of New Technologies: A Multitechnology Analysis of Latecomer Advantage and Global Economic Integration.” Annals of the Association of American Geographers 95 (4): 789–808.
Riddlesden, Dean, and Alex D Singleton. 2014. “Broadband Speed Equity: A New Digital Divide?” Applied Geography 52: 25–33.
Tranos, Emmanouil. 2013. The Geography of the Internet: Cities, Regions and Internet Infrastructure in Europe. Edward Elgar Publishing.
Zook, Matthew, and Michael McCanless. 2022. “Mapping the Uneven Geographies of Digital Phenomena: The Case of Blockchain.” The Canadian Geographer/Le Géographe Canadien 66 (1): 23–36.