PowerPoint Presentation

Is there a way toMap 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?

Method overviewDatabaseSector datafileCompanies houseBusiness rates data1. 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 them4. A definition of a sector such as digital and technology is then run as a query against the master database

Headline figuresThis analysis is based on:3,339 businesses identified as digital & technology businesses22.7 million tweets, from 350,000 people, collected in October 2014.

Industry Breakdown

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.

No clustering

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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 isnt enough definition in the centre of the City to distinguish between those in the city centre compared with those situated towards the Ring Roads.

We could cluster the city into three distinct regions.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 dont 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.

If we use 7 clusters we start to see the different towns across the city becoming the focus of the clusters.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.

12 clusters proves to be the ideal number of clusters to represent the geographic spread of businesses around the area.9

IlkleyWetherby / Boston SpaGuiseleyGarforthRothwellMorleySeacroft / NE LeedsLeeds City CentrePudseyHorsforthRoundhayHeadingley

Geographic ClustersClusterCenterBusinesses1Headingley3212Ilkley533Seacroft2044Boston Spa1375Roundhay4486Leeds City Centre8587Morley2618Pudsey2039Guiseley25610Rothwell7111Garforth25012Horsforth198

So, we know where these businesses are centered across the city. What else can we find out?11

Industry & Geography

Text info

This word cloud is generated from the text descriptions weve 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.

This is unfiltered; its just whats taken out of the data straightwaway13

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.

This removes some stop words to do with companies etc. The bag of words analysis doesnt yield anyting obvious lots of words used quite infrequently across the sample.14

Industry Make Up

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City StructuresThe 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 citys 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.

Social Data Set22.7 million tweets from 350,000 individuals from October 2015The individuals were included because their Twitter biographies tell us they live in one of 10 cities across the UK864,981 (3.8%) tweets from people who live in Leeds from 23,274 (6.6%) people.

Word Clouds

LeedsBristolUsing 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.

Community StructuresWe 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?

Leeds vs Bristol

LeedsBristolTotal23,27414,311University33.1%10.0%Student6.3%4.0%Fan5.3%2.6%Love5.9%4.2%We could go on

Community Structures

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 doesnt change too much day by day.

Our method must remove noise. We do this through the application of novel mathematics.Noise

If we remove the noise from our network visualisations, we find the stable structures which dont 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

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

These diagrams show the significant communities of these 6 cities once we remove noise from the data weve collected.