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What can tweets about contact tracing apps tell us about attitudes towards data sharing for public health?

Birds eye view of a crowd of people on street

What can tweets about contact tracing apps tell us about attitudes towards data sharing for public health? (Part 1)

The ongoing COVID-19 pandemic means governments have been looking for technological solutions in order to reduce the spread of the virus. Contact-tracing apps are now being used, from Singapore’s ‘TraceTogether’ to ‘StopKorona!’ in North Macedonia. As restrictions on movement are eased in many countries, these apps aim to identify if an individual has been in contact with an infected person through Bluetooth and/or GPS signals. This provides alerts to users and creates early warnings of new outbreaks.  As these apps have been adopted, a huge amount of online discussion has followed about the benefits and concerns around sharing personal data for the benefit of public health. 

So much of this conversation seems novel. Several months ago, most in the UK would have gawked at the possibility of a government app privy to information about who they come into contact with. Yet, the phrase “we are in unprecedented times” has been difficult to escape in recent weeks.  

For me, the onset of the pandemic has coincided with a new research position with the LifeInfo project, under the supervision of Michelle Morris‘ expertise in Health Analytics. This project focuses on people’s attitudes towards sharing their lifestyle data – from supermarket loyalty card to fitness apps – and linking this to health records to drive research into the risk factors of non-communicable diseases such as diabetes, heart disease and certain cancers. Access to these data could have immense benefit as millions of yearly deaths can be attributed to poor diet and physical inactivity.  

Holly Clarke

Leeds Institute for Data Analytics

Holly Clarke is an Intern at Leeds Institute for Data Analytics, applying data science solutions to solve complex, real-world challenges. She is working for the LifeInfo project with Michelle Morris, researching attitudes towards novel lifestyle and health data linkages and how access to this information could improve public health. 

You can follow Holly on Twitter: @HollyEClarke

At the heart of this project is also the vital recognition that we must understand people’s concerns about such initiatives and adapt research accordingly. Part of my role is analysing free-text survey responses about the circumstances under which people would share different types of lifestyle data for health research and factors that might impact their decision to do so.  While the conversation about contact tracing apps and their place in our lives is certainly novel, many of the words and topics about these apps mimic those that come out of my analysis.  

This made me wonder how I could tap into the conversation about contact tracing apps and the insights this could give about data sharing, privacy, surveillance and public health. For the past two weeks I have been scraping tweets about coronavirus apps and will continue to do so as they are developed, trialed, and used in countries around the world.   

This is the first of a series of short blog posts about attitudes towards contact tracing apps and data-sharing for public health. Using text analysis and Natural Language Processing (NLP), I will be answering questions about the conversation around these apps. What topics are prevalent and how do people feel about sharing their data? How does this look in different countries and what role does context play? How does this relate to more general attitudes about data-sharing for public health benefit and what might the impacts be going forward? Twitter is by no means a direct expression of public opinion, but analysing tweets can give us important insights about people’s attitudes, news stories that shape narratives, and shifts in opinion over time.  

So, are people talking about contact tracing apps?  

The first thing to establish is whether people actually care about contact tracing apps. Here, the answer is an undeniable “yes!”. A total of 12,593 tweets were collected on the topic of COVID-19 apps produced during the two-and-a-half-week period between 24 April 2020 and 12 May 2020 (and limiting collection to those in the English language)1. Governments need around 60% of the population (80% of UK Smartphone users) to enable contact tracing apps for them to be effective, which could influence many people to consider their relationship with data-sharing that haven’t given much thought to it before.  

Tweets about coronavirus apps have gone from relatively low numbers (just 203 tweets on 25 April, the first full day of collection) to peaks of over 1300 tweets per day on the 27 April and 5 May. These peaks can be linked to the ‘COVIDSafe’ app release in Australia and the announcement that the NHS ‘track and trace’ app was to be trialed on the Isle of Wight in the UK (see figure 1a).  

Time series graph showing daily counts of tweets about COVID-19.  Shows a peak following the release of the COVIDSafe app in Australia on 26 April and a peak following the UK Government announcement on 4 May re testing of 'track and trace' app on the Isle of Wight.
Figure 1:  Time Series of Covid-19 app tweets showing (a) the number of tweets about Covid-19 apps per day (top) and (b) the number of geo-coded tweets that were produced in different countries highlighting Australia and the UK (bottom), 25 April –11 May 2020.  

Where are people talking about COVID-19 apps? 

Some tweets are geo-located, indicating the country and even city the tweets were produced. Although these tweets make up only a small proportion (2.5%) of the total tweets collected, they act as a sample to indicate where people were tweeting about COVID-19 apps.  

Graph showing locations of Geocoded Tweets about COVID-19 app - UK and Australia have the most tweets.
Figure 2: Locations of geo-located tweets about Covid-19 apps showing (a) the number of tweets collected from each country (top) and (b) their locations on the world map (bottom), 25 April –11 May 2020. 

Most tweets are shown to be produced in the UK and Australia. In these countries contact tracing apps have been nationally introduced and promoted (in the case of Australia) or locally trialed (in the case of the UK). Canada and the US currently constitute only a small proportion of tweet locations; however, this could change in the forthcoming weeks as these countries are yet to announce apps. 

India is the third most popular country for tweet locations where the contact tracing app ‘Aarogya Setu’ has been introduced with associated controversies about personal privacy. Many more tweets about this app have likely been created but in languages other than English, so are not included within the dataset. This is important to consider as insights gained from analysing  tweets will reflect a majority Western perspective. Some of the first countries to introduce contact-tracing apps are non-English speaking (for example South Korea) and additionally have restriction on access to Twitter (in the case of China).  

Over 80% of the geo-located tweets were produced in two countries – the UK and Australia.  Yet, this is not consistent across time.  As shown in figure 1b, during the first week of data collection the conversation was dominated by the Australian context (shown in blue), and this is consistent with the first peak of tweets related to the roll-out of the Australian contact tracing app. Following this, the second week of data collection shows the conversation has shifted towards the UK context (shown in orange) as the NHS app is trialed in the Isle of Wight.  

What do tweets say? 

Next week’s blog post will focus what people are saying about Covid-19 apps, whether attitudes are positive or negative, and if this differs based on the country and context.  The wordcloud below gives an initial insight into the current conversation around these apps.  Two findings stand out. First, context appears to play a large role in shaping the conversation. Words referring to key places and actors (both technological and state) are frequently included in tweets. These include ‘government’, ‘nhs’, ‘apple’, ‘google’, ‘India’, ‘Australia’ and ‘Isle [of] Wight’.  Second, it is striking that the words ‘privacy’ and ‘trust’ are amongst the most frequent words used, showing data management and personal privacy to be at the forefront of discussion.  

Word cloud of most frequent words included with COVID-19 app tweets.
Figure 3: Word cloud of most-frequent words included with Covid-19 app tweets2 

1Search terms included any reference to ‘corona/coronavirus/covid/covid-19 app’ as a single phrase and inclusive of alternative punctuation and spacing  

2Note: common ‘stop words’ are excluded, for example ‘is’ or ‘and’, also the words ‘corona’, ‘covid’ and ‘app’ are excluded as these were the search terms and thus highly frequent. 

Household Mobility – Where and how far do we move?

Flow Map

In the first article in this series I discussed why it is important for us to understand household mobility, outlined the sources currently available to researchers and highlighted the potential of using commercial data as a possible alternative to census or admin data.

Using large scale commercial data sets, such as the Whenfresh/Zoopla Property Transactions and Associated Migration (available via the CDRC), is exciting as it can answer questions and enrich our understanding of mobility patterns and population change in the UK.

The Consumer Data Research Centre has partnered with online property search provider Zoopla and data insight consultancy Whenfresh to obtain data about the characteristics of properties which have been sold in England and Wales. For the 2014 calendar year, visualised here, there were over 900k unique property transactions.

Where do we move?

This image highlights some larger UK cities and the flow of households between them. When a line becomes more opaque on one side it means that the flow of house movers is directed towards the respective city by a majority of people.

1. Over 68% of moves occurred within the same postcode area (e.g. the postcode area starting LS covers Leeds, EX covers Exeter, etc.) and 34% of moves occurred within the same postcode district (e.g. LS1 covers much of Leeds city centre).
2. Largest number of moves within the city:

  • London – 6057
  • Bristol – 2523
  • Nottingham – 1938
  • Leeds – 1427
  • Sheffield – 1387
  • Manchester – 1342

3. London accounts for 7.1% of all moves in England and Wales (either in to, out of or within the city) and is very well connected to the greater South East region, towns on the south coast and other key cities. London and the greater South East has been termed an ‘Escalator Region[1]’, whereby young people move in to gain skills and training before eventually moving on.

How far do we move?

  1. On the whole, households don’t tend to move very far, the overall median distance of a move in England and Wales is 3.2 miles (we use median distance as opposed to average distances as there are a small number of moves across very large distances which skew the reporting)
  2. People who move within the East of England move further than anywhere else (2.8 miles) whereas the median distance of moves in London is only 1.6 miles. These values are not surprising, given the settlement patterns in the East of England are very different to those in London: the move from Cambridge to Norwich is 64 miles, compared with just 30 miles between Hayes in the far east of London to Dartford in the far west of London.
  3. People moving within the regions of the north of England travel shorter distances than those in the south. The median distance of move within the North East is 1.61 miles, within the North West is 1.73 and within Yorkshire and Humber is 2.01 miles. This is compared to 2.42 miles in the South East and 2.3 miles in the South West.

How connected are our cities?

CityReceived households from % of places in England and WalesSent households to % of places in England and WalesMore households moving into or out of city?
London39%60%Out (-4157)
Bristol23%28%Out (-376)
Norwich20%15%In (+148)
Nottingham18%22%Out (-265)
York17%14%In (+81)
Leeds15%16%Out (-238)
Swindon15%12%In (24)
Manchester15%18%Out (-396)
Birmingham15%21%Out (-560)
Southampton15%19%Out (-169)
Reading14%25%Out (-288)

Norwich appears to be an attractive destination with 20% connectedness and a net gain of 148 households. Nottingham, York, Leeds, Manchester, Birmingham and Southampton are all well connected cities, appearing in the top ten. One surprisingly well connected city for inflows was Swindon, which received households from 172 other places. The surprising entry in terms of outflow connectivity is Reading, which was connected to 25% of other places.

What data are used in these visualisations?

The Consumer Data Research Centre has partnered with online property search provider Zoopla and data insight consultancy Whenfresh to obtain data about the characteristics of properties which have been sold in England and Wales. For the 2014 calendar year, visualised here, there were over 900k unique property transactions. Attached to these transactions data are the Royal Mail redirection service data which provides details about the forwarding postcode for over 212k households who moved. Those records for which there is a forwarding address (and as such an origin-destination link) represent 19% of the 1.1m residential property sales made in 2014[2].  There are a number of unique advantages to using these data:

  • They are timely and easy to update. The data presented are for the calendar year 2014 but other date ranges could be specified.
  • They provide good temporal coverage. Instead of a yearly snapshot of transitions they report the exact date that a property was sold. This allows us to start to understand seasonal trends in mobility.
  • The geography provided is extremely detailed. Sales (origins) are at address level while destinations are delivered as postcodes. This allows us to start to assess mobility patterns at a very small scale, answering questions about communities rather than the more common administrative units used in previous work.

Consumer data like those used here have a role to play in the future of understanding mobility patterns, and population change more generally.

The visualisations were created by Herwig Scherabon[3], an expert in data visualization and information design. Interpreting and visualising flow information is particularly difficult because of the dimensions of the data. Having origins and destinations dictates a need for some way of linking the two together, and the volume of data soon becomes unmanageable. This is why design decisions, like being selective in the number of data entries shown or deciding how opaque crossing lines should be, is important when trying to get a message across.

About the author

Dr Nik Lomax is a University Academic Fellow at the University of Leeds, his research focuses on the way in which demographic behaviour changes over time and how people interact with the areas in which they live and work. Much of his work focuses on the dynamic processes involved in migration but he is also interested in the social implications of changing demographic composition: household formation, social exclusion and population ageing for example. Areas are shaped by changing economic conditions, policy interventions and social attitudes, which in turn has an impact on demographic behaviour. Modelling and explaining these complex interaction is key to the work the work which he does.

[1] Reference to Tony Fielding’s work here

[2] HMRC data

[3] http://scherabon.com/

What’s happened to UK migration since the EU referendum – in four graphs

In this recent Conversation article, CDRC’s Nik Lomax explains what has happened to UK migration since the EU referendum. 

Many of the analyses of why a majority of British voters opted to leave the European Union in a referendum in June 2016, have pointed to a desire to control immigration as a key driving factor. However, surveys since the referendum show fewer people are now concerned about the issue than they were before the poll.

But what has actually happened to immigration in the three years since the UK voted for Brexit?

Decline in net migration

The latest migration estimates published by the Office for National Statistics (ONS) show a steady decline in net migration – the number of immigrants entering the country minus number of emigrants leaving the country – in the three years since the EU referendum in 2016.

The UK saw a net gain of 311,000 migrants in the year to June 2016, which dropped to a net gain of 212,000 migrants in the year to June 2019. This means that while more people are still arriving in the UK than leaving it, the net figure has gone down.

This trend is driven by both sides of the equation. Alongside a decline in the number of people immigrating to the UK – which fell from 652,000 to 609,000 per year in the three year period – the number of people emigrating rose from 341,000 to 397,000. However, the headline figure masks substantial differences between migration from within and outside the EU during this time.

UK long-term international migration UK.
Office for National Statistics – Long-Term International Migration (LTIM), LTIM with preliminary adjustments based on Department for Work and Pensions and Home Office data

Increase from outside the EU

There has been a fall in EU migration since the referendum. In the year ending December 2015 there was a net gain of 218,000 EU citizens. Following a steep decline covering the time of the EU referendum in June 2016 and the immediate aftermath, the figure for the year to June 2019 was 48,000 – its lowest level during the whole of the 16 years covered by the latest ONS data.

EU immigration fell considerably during this time, from 304,000 to 199,000 per year, while emigration of EU citizens increased steadily from 86,000 to 151,000. The net decline can be seen for the EU as a whole, but is most striking for the so-called EU8 group: Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia. The UK’s net gain of people from these countries was 80,000 in the year ending December 2015, falling to around zero in the year ending June 2019.

In contrast, net migration from outside the EU has steadily risen over the same time period, from 164,000 to 229,000 in June 2019, continuing a trend which began in 2013. This has been driven primarily by an increase in immigration rather than a drop in people leaving.

Net migration by citizenship.
Office for National Statistics – Long-Term International Migration (LTIM), LTIM with preliminary adjustments based on Department for Work and Pensions and Home Office data

While the UK is unable to put limits on the number of EU citizens arriving under free movement rules while it remains in the EU, it can control migration from outside the EU. Yet, it is this type of migration that has increased consistently.

Uncertainty in employment markets

The International Passenger Survey, one of the data sources upon which the latest ONS publication is based, asks for the reasons that people migrate, with employment and study consistently the most common answers. The latest migration data reveal a decline in EU citizens migrating to the UK for work-related reasons, which include looking for a job or to take up a job offer. Work-related reasons are the most common for EU citizens, and more migrate for a definite job than to look for work.

International Passenger Survey Data, Office for National Statistics

The fall in the number of EU citizens migrating to look for a job is most apparent when comparing the year before the EU referendum against one year after it. The fall in those migrating to take up a definite job is most apparent when comparing the year ending December 2017 with the December 2018 figure. This might reflect uncertainty in the immediate post-referendum period, meaning EU citizens were less prepared to migrate speculatively but still willing to move to take up definite employment. There is consistent evidence that the number of national insurance number registrations (required to work in the UK) for EU nationals has been falling since a peak in 2015.

Attraction of British education

For migrants from outside of the EU there was a similar decline in the number of immigrants looking for work over in the three years since June 2016 (from 24,000 to 8,000), largely driven by a more restrictive migration regime. However, the numbers migrating for a definite job increased from 51,000 in the year to June 2016 to 74,000 in the year to June 2019.

Among migrants from outside the EU the most common reason for migrating to the UK was to undertake formal study – with the number giving this reason up from 113,000 in the year to June 2016 to 157,000 in the year to June 2019. This rise, combined with the rise in those migrating for employment has contributed to the net gain of migrants from outside the EU.

International Passenger Survey Data, Office for National Statistics

Given that international students generally stay in the UK for a defined period of time while studying for a course, there was considerable debate about if students should be included in the governments now abandoned net migration target. However, the debate will continue if similar targets are pursued after the 2019 election.

Where are British citizens going?

Net migration for British citizens remains fairly stable, with a net loss in each of the past 16 years. So where do these British citizens go? This is a surprisingly difficult question to answer comprehensively as the data are not routinely collected, rather estimates are constructed from various sources.

In 2006, the Institute for Public Policy Research, drawing on individual country census and other data sources, reported that around three quarters of all Britons living abroad live in 10 destination countries: Australia, Spain, US, Canada, Ireland, New Zealand, South Africa, France, Germany and Cyprus. An update in 2008 showed that UAE and Switzerland had taken Cyprus’s place at number 10.

Recent research published by demographers Guy Abel and Joe Cohen broadly confirms the top nine destinations using 2010 data, although Italy comes in at tenth spot in their work.

An eye on the future

The latest 2018-based National Population Projection from the ONS take into account trends in migration over the past 25 years. This helps put things in to perspective, as over the longer term, trends tend to fluctuate less than in the short term, where they are influenced by events such as economic conditions or Brexit.

The principal projection has factored in a decline in net migration over the next six years, with a fall to 190,000 annually from 2025. It remains to be seen if the current short-term trend for an overall decline in net migration seen in the latest estimates will continue, or indeed accelerate depending on the outcome of the Brexit negotiations. If it does, then there is a more radical low migration projection variant, which assumes a much lower annual 90,000 net gain by 2025.The Conversation

Nik Lomax, Associate professor in Data Analytics for Population Research, University of Leeds

This article is republished from The Conversation under a Creative Commons license. Read the original article.

We’re hiring: Research Data Scientist/Data Engineer at the CDRC in London

We are seeking someone with a MSc or PhD in computer science/information science or an allied discipline with a strong programming component, with awareness of quantitative methods and social science datasets. The postholder will research improved ways of concatenating and conflating consumer Big Data sources with conventional social surveys and framework datasets. The postholder will research future data acquisition strategies, including development of appropriate preservation and custodianship, consistent with data user priorities.

Further information on the post and person specifications.

What does supermarket loyalty card data reveal about food purchase behaviours?

What does supermarket loyalty card data reveal about food purchase behaviours?

Dr Michelle Morris, UAF LIDA

This week LIDA and CDRC researchers presented two posters at the 13th European Nutrition Conference in Dublin, showcasing some recent work with the large UK retailer: Sainsbury’s.

The first poster presents PhD results from Vicki Jenneson, a student in our Data Analytics and Society Centre for Doctoral training. Results reveal that households in Leeds purchase, on average, 3.5 portions of fruit and vegetables daily. This is higher in affluent and rural areas and with 22% of households purchasing more than 5 portions per day. Conversely in poor, urban areas 18% purchase less than 1 portion per day.

The UK recommendations are to consume 5 portions of fruit and vegetables per day per person. It may be that people get their fruit and veg at school or buy them at a work canteen. However, the transaction data offer a novel and objective measure of fruit and veg purchases.

The work additionally revealed variation in purchasing according to the time of year and the age and gender of loyalty card holders. For the full abstract visit here,or view the poster here.

The second piece of work, from CDRC Research Fellow Stephen Clark, reviews the UK dietary recommendations, the Eatwell Guide, compared with loyalty card purchases for Yorkshire and the Humber. As a proportion of the weight of all purchases, fruit and vegetable purchases are encouragingly close to the recommendations, with 31% purchased compared with 39% recommended. Surprisingly purchases of starchy products, such as bread and pasta, were below the recommended with 17% purchased compared to 37% recommended. Meat and plant based protein products were similar to recommendations and more than twice as many dairy products are purchased compared to recommended. Perhaps unsurprisingly, sweet and savoury snacks like chocolate and crisps exceed recommendations with 17% of purchases by weight on these food, compared to 3% recommended. For the full abstract visit here,or view the poster here.

We are excited to be collaborating with Sainsbury’s on this work and by the potential of these types of transaction data to understand the food purchasing behaviours of our population. We accept there are limitations to these data as they may not capture all food consumed and that individuals may buy from multiple retailers. However, compared to limitations of self-reported data such as recall bias, in addition to the burden of completing a food diary, limiting the scale of data collection, these novel data sources offer great potential in future research and policy making.

The work described here is in the early stages, full academic papers are forthcoming.

Towards a Better Understanding of Footfall


Since 2015 the Consumer Data Research Centre (CDRC) has worked with the Local Data Company to collect and analyse ‘SmartStreetSensor’ footfall data for research purposes. The data form part of the CDRC research data collections and are held in a secure data lab under strict access protocols.

To date, the technology, the revealed footfall patterns and their relationships with other data have received extensive attention in three University College London (UCL) PhD theses:
– Estimating Footfall from Passive Wi-Fi Signals (Bala Soundararaj, August 2019)
– Towards a Comprehensive Temporal Classification of Footfall Patterns in the Cities of Great Britain (Karlo Lugomer, awarded June 2019)
– Retail Sales and Footfall (Terje Trasberg – thesis in preparation)

Analysis has been based on more than 650 sensors sited across 80 retail centres.

Please find the full report here.

Access to Healthy Assets and Hazards (AHAH) v2 Data Resource

The Geographic Data Science Lab are proud to announce the latest release of their data resource, “Access to Health Assets and Hazards” (AHAH).
AHAH is a multi-dimensional index for Great Britain measuring how “healthy” neighbourhoods are derived from data on:

  • Access to retail outlets (fast food outlets, pubs, off-licences, tobacconists, gambling outlets)
  • Access to health services (GPs, hospitals, pharmacies, dentists, leisure services)
  • Air quality (green space, air pollution)
  • Access to natural environment (green spaces including parks and recreational spaces, blue space including rivers, canals and lakes).

It allows researchers and policy makers to understand which areas have poor environments for health and helps to move away from treating features of the environment in isolation to provide a comprehensive measure of neighbourhood quality.

AHAH is produced for Lower Super Output Areas for England and Wales, and Data Zones for Scotland. Estimates are produced for 2017 and is generated annually.

All of the individual inputs have also been made freely available in the data resource as well in a push for opening up small area health data. It provides one of the most comprehensive free data resource available for such data.

Data can be freely accessed at http://maps.cdrc.ac.uk/. AHAH and the individual input variables are are located under the “indicators” tab. Data can be downloaded from http://data.cdrc.ac.uk.

A summary of the results:

Soho identified as unhealthiest place to live in Great Britain according to new study

  • University of Liverpool study analysed latest lifestyle and environmental measures
  • Great Torrington in North Devon was the healthiest place to live

New analysis by University of Liverpool researchers of the latest lifestyle and environmental measures has revealed that Soho in London is the most unhealthiest neighbourhood to live in the country. Researchers used the latest updated version of their data resource tool which contains a range of lifestyle and environmental measures to identify neighbourhoods that are healthy and those that are unhealthy. The type of information which the data tool analysed included levels of air pollution, access to various amenities such as fast food outlets or pubs, and proximity to health services including GPs, and parks/recreational spaces.

The study found that Soho had the greatest access to unhealthy opportunities such as fast food outlets, pubs and off licenses, combined with high levels of air pollution and low levels of parks and green spaces. By contrast, the healthiest place to live was ‘Great Torrington’ in North Devon. The small market town has low levels of pollution, good access to parks and green space, few retail outlets that may encourage poor health-related behaviours, and good access to health services.

All of the other top ten healthy places to live where located in Scotland. These included Lochwinnoch in Renfrewshire, Fauldhouse in West Lothian, Foxbar in Renfreshire and Marnoch in North Lanakrshire. At a national level, the study found that six neighbourhoods in the top ten were located within Inner London. Also in the top ten were Shotley Gate near Ipswich and areas North of Immingham in Humberside.

Liverpool Senior Lecturer in Health Geography, Dr Mark Green, who undertook the study, said:

“Our research, in conjunction with the Consumer Data Research Centre and Public Health England, has allowed us to pull together freely available information from sources such as GP surgeries, Health Centres, fast food outlets, air pollution statistics published by the Environment Agency. Our updated data release makes it now the most comprehensive free source of data on healthy environments available. The statistics reveal important insights about the concentration of certain amenities that may be damaging or promote health. For example, on average, individuals in Great Britain are just as close to a pub or bar, as they are to their nearest GP (1.1 km). We also found that 42% of people are within 1 km (or a few minutes’ drive time) of their nearest gambling outlet. These statistics reveal troubling issues with the neighbourhoods we live in and how they may be damaging to our health.”


Professor Alex Singleton, Deputy Director of the Consumer Data Research Centre, said:

“Our study found that access was not evenly spread across Great Britain – rural areas have poorer access to many health services, and those services which are seen as damaging to health are often concentrated in poorer areas. For example, 62% of people who live in the 10% most deprived areas are within 1 km of a fast food outlet compared to 24% in the 10% least deprived areas.”


The data resource is available online (http://maps.cdrc.ac.uk) and free for anyone – from policy makers to members of the public – to explore how near or far away they are to services, and how this varies across their local (and national) regions. Users can log on and can either click on the map or search with a postcode.

Dr Mark Green added:

“We anticipate that this resource will be an important tool for citizens and policy makers alike interested in how their neighbourhoods may be associated to their health.”


The data resource is part of the Consumer Data Research Centre (CDRC) which aims to unlock valuable insight from the vast amounts of data collected by business, local and national government organisations.

The findings of this study will be presented for the first time at the International Medical Geography Symposium (1st July 2019).

Oxford Retail Futures Conference – call for papers

Understanding the impact of emergent technologies on retail business models, processes and places

The Oxford Institute of Retail Management, Saïd Business School, is holding its annual Oxford Retail Futures Conference in Oxford on 10 December 2019.

This year’s topic is ‘Understanding the impact of emergent technologies on retail business models, processes and places’.


Traditional forms of retailing have been significantly affected by the growth of emergent and often disruptive digital technologies. The rhetoric suggests apocalyptic impacts on existing forms of retailing as well as higher costs arising from integration and transformation strategies. The reality suggests that the benefits of emergent technologies may be more nuanced and contested than previously anticipated. Similarly, the amount and characteristics of data generated by internet users, mobile devices and sensors and organisational and integrative IT systems continues to provide both challenges as well as opportunities for retail firms.

The task continues to comprise the extent to which emergent technologies and big data-driven analysis can genuinely deliver benefits and outcomes which carry real business value, including how such technologies can improve business performance and competitiveness.

Topic Selection

In this call for papers or extended abstracts (minimum one page of A4), we would like to capture the current state of the art in areas related to understanding and evaluating the impact of emergent technologies upon retail business models and processes in the retail sector, including the consequences for places. Submissions may include theoretical and conceptual work, as well as examples from practice, but should focus on outcomesimpact, and/or managerial implications. The work of early researchers and doctoral students are particularly invited. Results of analysis of large data sets such as those of the ESRC Data Initiative’s Consumer Data Research Centre (https://data.cdrc.ac.uk/) are also welcome.

The call is focused, non-exclusively, on the following topics (applied in the retail context, both at the store-end and in the extended retail value/supply chain):

  • Analysing the impact of emergent technologies upon customer shopping behaviour
  • Consequences of AI and machine learning for the development of more effective customer insight and retail business analytics
  • Ethical aspects of new and emergent technologies for retailers
  • Evaluating the impact of augmented reality and other instore technologies on the repurposing of physical retail spaces
  • Impacts of new data sources and emerging analytical methods on the efficiency of corporate decision-making by retailers, suppliers and third-party business service firms
  • Implications of data-driven research in retailing for public policy
  • Robotics and retail automation
  • Supply chain consequences of emerging retail distribution networks
  • The evolving technology requirements of omnichannel retailing

Papers submitted will be reviewed by the academic board. Extended abstracts and work in progress are welcome.


  • 09. September 2019 – draft abstract/paper submission
  • 30 September 2019 – notification of abstract/paper acceptance
  • 29 November 2019 – submission of final papers/extended abstracts

Members of the Conference Academic Board

  • Dr Richard Cuthbertson, OXIRM, Saïd Business School, University of Oxford, UK
  • Dr Jonathan Reynolds, OXIRM, Saïd Business School, University of Oxford, UK

Contact Details

The conference is being organised jointly by the Oxford Institute of Retail Management, Saïd Business School, University of Oxford and the Consumer Data Research Centre (CDRC).

For enquiries, please contact OXIRMEnquiries@sbs.ox.ac.uk.

Registration fee

The registration fee (£195) can be waived for students and presenters.

Registration details and links will be provided nearer the date.

CDRC Data Partner Forum 2019

On Tuesday 21 May 2019 the Consumer Data Research Centre hosted its fourth annual Data Partner Forum at the Leeds Institute for Data Analytics.

The aim of the event was to celebrate the Centre’s achievement over the last five years and spotlight our research and partnership strengths. Led my Co-Director Professor Mark Birkin who introduced the session; stating that the CDRC has more than 60 partners, 40 of which are sharing their data with and through the Centre with an extended data research community.

The day consisted of three sessions, featuring presentations and lightning talks under the themes:

  • Smart cities
  • Mobility, migration and housing
  • Health and lifestyle

The full agenda can be found below.

Should you be interested in attending the event next year, or wish to discuss ways in which your organisation can engage with the CDRC, please contact Paul Evans.


Session 1: Smart Cities (Chair: Dr Yi-Chun Ou)

  • Welcome and intro by Professor Mark Birkin
  • Talk 1: Dr Yi-Chun Ou, University of Leeds – An understanding of interactions with smart objects and their consequences
  • Talk 2: Dr Emmanouil Tranos, University of Birmingham – Modelling clusters from the ground up: a web data approach
  • Talk 3: Professor Alison Heppenstall, University of Leeds – Using machine learning to uncover hidden structures and patterns in cities
  • Lightning talks:
  • 1. Dr Kevin Minors, University of Leeds – What is a particle filter? How can smart cities use them?
  • 2. Natalie Rose, University of Liverpool – Exploring the impact of weather on retail sales and consumer behaviours using machine learning.
  • 3. Alex Coleman, University of Leeds – Extracting actionable insights from free text police data
  • 4. Professor Roy Ruddle, University of Leeds – Using visualisation to profile data

Session 2: Mobility, migration and housing (Chair: Professor Alison Heppenstall)

  • Talk 1: Dr Kate Pangbourne, University of Leeds – Shifting travel behaviour through personalised messaging: data, argumentation, attitude and personality.
  • Talk 2: Professor Alex Singleton, University of Liverpool – Contemporary geodemographic analysis and urban analytics
  • Talk 3: Dr Minh Le Kieu, University of Leeds – Data-driven cities: bringing together machine learning and big data
  • Lighting talks:
  • 1. Jeroen Bastiaanssen, University of Leeds – Job accessibility and employment outcomes: the role of public transport in the UK
  • 2. Jack Lewis, University of Leeds – Assessing the presence of ‘e-food deserts’ in the UK groceries e-commerce sector
  • 3. Stelios Theophanous, University of Leeds – Synergy PRIME – Multi level Modelling, Simulation and Visualization

Session 3: Health and lifestyle (Chair: Dr Michelle Morris)

  • Talk 1: Dr Carmen Piernas-Sanchez, University of Oxford – Behavioural interventions to improve the quality of grocery shopping: working with retailers and clinicians
  • Talk 2: Dr Emma Wilkins, University of Leeds – Assessing diet and physical activity in a university student population: A longitudinal novel data approach
  • Talk 3: Dr Will James, University of Leeds – Local area estimation profiles and consumer attitudes
  • Lightning talks:
  • 1. Victoria Jenneson, University of Leeds – Supermarket purchase data in population dietary research
  • 2. Francesca Pontin, University of Leeds – Physical inactivity; determining the demographic and geographic factors using smartphone data.
  • 3. Michelle Morris, University of Leeds – What do you think about res

Life cycle: is it the end for Britain’s dockless bike schemes? Copy

CDRC researcher Oliver O’Brien used two datasets on the CDRC Data platform, as part of a significant data contribution for a Guardian newspaper online article looking at the recent rise and fall of dockless bikeshare systems in various English cities. Oliver was also interviewed for the article, and contributed commentary and insight into the success of the systems, based on his observations of the data on CDRC Data and elsewhere during the course of his research.

The datasets used were:



The Guardian article can be viewed at https://www.theguardian.com/cities/2019/feb/22/life-cycle-is-it-the-end-for-britains-dockless-bike-schemes

Over 50 datasets relating to individual city bikeshare systems are available on CDRC Data, including some open datasets which can be directly downloaded. They are listed at: https://data.cdrc.ac.uk/product/bicycle-sharing-system-docking-stations