Reblogged with permission from Juan Mateos-Garcia, whose team at Nesta studies innovation in creative and digital industries. His team is interested in tracking the emergence of new technologies, and communities of innovators. This blog presents what they found using the data available from our API. The article was originally published on Nesta.
Juan is also a member of the Data Visualisation Brighton Meetup, and Big Data Debate in London.
Tech meet-ups have become an important feature of the digital innovation landscape. In these events, coders, designers, hackers and entrepreneurs (among others) come together to learn from each other and network. Meetups can help participants keep their technology skills fresh in fields that move too fast for universities and training providers, and facilitate collaboration and job mobility, increasing the connectivity and efficiency of local innovation ecosystems.
Websites like Meetup and EventBrite have emerged to make it easier for people to create and manage meet-ups.[i] The data generated by these platforms could help us understand when and where new technology communities emerge and evolve, and how they are connected to each other. It could also tell us something about the rise of new technologies. These are questions of obvious interest for policymakers, entrepreneurs, businesses and investors who want to identify the right communities of innovators to work with, and the right technologies to target.
In this blog, we undertake a preliminary exploration of UK tech meet-ups from Meetup to assess its potential as a source of information about the structure, geography and evolution of digital tech in the UK.
About the data
Meetup was created in 2002 to help people connect with others in their community. It currently has over 20 million users in 192,000 groups in 181 countries. When registering, users express interest on particular topics (e.g. “data science” or “online marketing”), and are shown information about groups near to them that focus on those topics (or similar ones).[ii] Users can join those groups to receive updates about forthcoming events. Meetup charges group organisers a monthly subscription fee.
To get the data, we query the Meetup API for groups in the “Tech” category in UK cities (based on this Wikipedia list). This returns 3,707 groups as of 2nd April 2015. After removing duplicates, we are left with 1391 groups for which we have information on location, membership, starting date, description and topics for the group.[iii]
These 1,391 groups are based in 160 unique locations in the UK, and have a gross total of 434,826 members.[iv] 71% have been created since 2013, consistent with the idea (though not necessarily proof of) of increasing levels of meet-up activity in recent times.
A graph of the tech landscape
In aggregate terms, the groups in our list focus on 2,569 topics. We want to arrange these topics into a smaller set of “tech fields” containing inter-related topics. To do this, we follow a ‘data-driven’ approach based on scientometrics principles (the quantitative analysis of science and technology metrics e.g. academic papers and patents).
The basic idea is that topics in the same tech field will often be mentioned by the same Meetup groups.[v] For example, if the business challenge of creating value from big data requires the combination of database technologies, analytics methods and parallel processing frameworks, these topics are likely to be of interest to the same practitioners. As a consequence, we would expect to find them mentioned by the same groups, in a way that defines a ‘data’ technology field and its community of practitioners.
We visualise these associations in a “topic network” where topics that are often mentioned together are linked and “pulled together” (see graph below).[vi] After constructing that network, we use community detection algorithms to look for densely connected “clusters” of topics inside them.[vii] This results in the identification of six tech fields:
- Application: includes topics representing industries and domains where digital technologies are being applied, such as startups and entrepreneurialism, social media, digital marketing, educational technology, and mobile and web design.
- Data: includes topics related to data and analytics, such as big data, data science, predictive analytics, machine learning, open data or data mining.
- IT systems: The topics here represent IT engineering and systems administration activities.
- Hardware: Its topics relate to technologies and skills with a hardware component, such as 3D printing, Internet of Things or Robotics, as well as Maker communities.
- Python: Interestingly, the community detection algorithm does not allocate the programming language Python to any of the tech fields above. This could be explained by the fact that Python is a general programming language in its own right (it has many links to topics in the Application and Software fields), but at the same time is gaining increasing popularity among data analysts and data scientists (in the Data field).
- Software: includes topics related to general-purpose software programming languages (e.g. JavaScript) and development methodologies (e.g. agile). It also contains some generic terms like “coding” and “computer programming.”
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