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Leeds clusters report


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Insights on over 3,500 digital and technology businesses in Leeds, the geographic, industry and community structures we can find through innivative use of data.

Published in: Data & Analytics

Leeds clusters report

  1. 1. IS THERE A WAY TO… • Map the economic structures of a city? • Audit the communities present in a city and how well connected they are? • Identify strengths and weakness of a city? • Understand the areas which need civic support? • Analyse the impact of policy decisions in real time?
  2. 2. METHOD OVERVIEW Database Sector datafile Companies house Business rates data 1. Identifies all organisations registered or holding offices in a location 2. An iterative process of automated google searches and use of web crawlers, finds information on the organisations such as their website addresses, social profiles, how they describe what they do on their websites etc. 3. As organisations are identified, further information is returned from other data sources on them 4. A definition of a sector such as ‘digital and technology’ is then run as a query against the master database
  3. 3. HEADLINE FIGURES This analysis is based on: • 3,339 businesses identified as “digital & technology” businesses • 22.7 million tweets, from 350,000 people, collected in October 2014.
  4. 4. INDUSTRY BREAKDOWN 45 392 63 627 1376 296 540 Telecommunications Software Publishing Other IT Audio & Visual Agency
  5. 5. LEEDS The 3,339 businesses are located across the Leeds area. The majority are located in the city centre but there are clusters of businesses in Ilkley, Wetherby/Boston Spa and Garforth.
  6. 6. 3 GEOGRAPHIC CLUSTERS Using a clustering algorithm the first level of useful clustering splits the city into three groups. The algorithm tells us how many clusters make “sense” so as to group the businesses into useful groups. The three clusters partition the businesses into East / West groups but there isn’t enough definition in the centre of the City to distinguish between those in the “city centre” compared with those situated towards the Ring Roads.
  7. 7. 7 GEOGRAPHIC CLUSTERS If we partition the businesses into 7 clusters we start to see the towns of the Leeds area becoming visible. However, with only 7 clusters we don’t get enough distinction between the centre of Leeds City Centre and the wedge from the City towards Roundhay and Chapel Allerton. We still need some additional clusters.
  8. 8. 12 GEOGRAPHIC CLUSTERS 12 clusters is the optimal number of clusters to help us see the different geographic groups we have in the city area. Each cluster has a “useful” center that maps onto Leeds City geography and helps us tell the story of how and where these businesses are located.
  9. 9. Ilkley Wetherby / Boston Spa Guiseley Garforth Rothwell Morley Seacroft / NE Leeds Leeds City Centre Pudsey Horsforth Roundhay Headingley
  10. 10. GEOGRAPHIC CLUSTERS Cluster Center Businesses 1 Headingley 321 2 Ilkley 53 3 Seacroft 204 4 Boston Spa 137 5 Roundhay 448 6 Leeds City Centre 858 7 Morley 261 8 Pudsey 203 9 Guiseley 256 10 Rothwell 71 11 Garforth 250 12 Horsforth 198
  11. 11. INDUSTRY & GEOGRAPHY 0 200 400 600 800 1000 1: Headingley 2: Ilkley 3: Seacroft 4: Boston Spa 5: Roundhay 6: Leeds City 7: Morley 8: Pudsey 9: Guiseley 10: Rothwell 11: Garforth 12: Horsforth Unknown Agency Audio & Visual IT Publishing Software Development Telecommunications Other
  12. 12. TEXT INFO This word cloud is generated from the text descriptions we’ve collected for each business. It is not filtered and so could be a little misleading. What does “none” refer to for example. To learn something useful about the businesses in our area we need to do some filtering on the words and remove some and up weight others.
  13. 13. TEXT INFO These are the words that Leeds digital businesses use to describe themselves. Whilst this is interesting because we can see useful descriptions we are lacking some additional context to help us understand how these businesses are organised across the city.
  15. 15. CITY STRUCTURES The previous diagram shows us how the different disciplines are related. Each dot is a Leeds business and the businesses are connected together by the things that they do. The distance on this diagram is significant, so disciplines that are closer together are more closely related. This technique could be used to map the city’s businesses each year and the patterns we see will be different. Each year we could see how the disciplines are merging or separating and what is the most important part of the landscape. Data is at the heart of the digital structure and this tells us that this is a very important part of the landscape. Publishing is a fragmented community and it will be interesting to see whether gets more or less important next year. Social stands out as connecting creative/brand, development and consultancy groups and it is interesting to see that “design” is seen as the linkage between the creative/advertising/brand communities and the internet/software development groups.
  16. 16. SOCIAL DATA SET • 22.7 million tweets from 350,000 individuals from October 2015 • The individuals were included because their Twitter biographies tell us they live in one of 10 cities across the UK • 864,981 (3.8%) tweets from people who live in Leeds from 23,274 (6.6%) people.
  17. 17. WORD CLOUDS Leeds Bristol Using the social data we can uncover the types of community that exist in each city. Whilst they look quite similar, there are some significant diffierences and these need analysing to understand what causes the difference in each city.
  18. 18. COMMUNITY STRUCTURES We see communities emerge around: • The University (the student population) • Sports clubs (Leeds United, Bristol City) • Music • Night life Is it possible to build up a matrix of “things” that a city has and in what proportion? Can we link a “successful” city to the presence of absence of these “things”. Does this lead us to having an always on, real time view of success in a city that we can use in our decision making process?
  19. 19. LEEDS VS BRISTOL Leeds Bristol Total 23,274 14,311 “University” 33.1% 10.0% “Student” 6.3% 4.0% “Fan” 5.3% 2.6% “Love” 5.9% 4.2% We could go on…
  21. 21. If we analyse mentions of “Leeds” on two separate days, we find similar trends but there is a significant difference in volume on different days. If we are to find a stable metric that is capable of analysing a whole city, we need a metric which doesn’t change too much day by day. Our method must remove noise. We do this through the application of novel mathematics. NOISE
  22. 22. If we remove the noise from our network visualisations, we find the “stable” structures which don’t change too much over time. This helps us see the communities which matter and need further study. Our method is capable of producing a “barcode” for a city, capable of expressing fine detail in a unique way which helps us understand how communities in a city fit together. NOISE
  23. 23. The new method allows us to clearly see the communities which exist in a city. This helps us understand those communities which make the city “tick” and those communities who are isolated from the main group of people. In Leeds, we find that the student community is isolated from the day to day communities we see focused on digital & tech, health and sport. What does this mean for the long term economic growth plans for our city? This can help decision makers understand which communities to focus on when considering policy changes. It can help us plot a course to build a city which has been optimised for growth. NOISE
  24. 24. These diagrams show the significant communities of these 6 cities once we remove noise from the data we’ve collected.