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New partnership pilots trials to help change eating habits

New partnership pilots trials to help change eating habits

What we choose to put into our shopping baskets and how we make those choices will come under the microscope in a series of pilot trials designed to encourage healthy and sustainable diets.

Data analysts from the University of Leeds have joined forces with social impact organisation, the Institute of Grocery Distribution, to test different ways to encourage healthy and sustainable eating and shine a light on what truly drives a long-term change in habits.

They are working in partnership with 20 leading retailers and manufacturers, including Morrison’s, Sainsbury’s and Aldi, to trial different strategies, including signposting better choices, the positioning of products in shops and online and the use of influencers and recipe suggestions.

Some have already begun to use some of those techniques in real-life settings as part of the research designed and implemented by the Leeds Institute for Data Analytics (LIDA) and Consumer Data Research Centre (CDRC).

Researchers from LIDA and CDRC will analyse the results by capturing and measuring sales data from each intervention, enabling the project group to see exactly what is going on in people’s shopping baskets and assess what truly drives long-term behaviour change.

Dr Michelle Morris, who leads the Nutrition and Lifestyle Analytics team at LIDA, and who is a CDRC Co-Investigator, said: “I am passionate about helping our population move towards a diet that is both healthier and more sustainable. I believe that unlocking the power of anonymous consumer data, collected by retailers and manufacturers, is a really important step towards this goal.

Working with the IGD and its members to evaluate their healthy and sustainable diets programme is very exciting – testing strategies to change purchasing behaviour and evaluating the wider impact of these changes.”

The pilot trials have been funded by IGD and form a key part of the charity’s Social Impact ambition to make healthy and sustainable diets easy for everyone.

Hannah Pearse, Head of Nutrition at IGD, said: “We want to lead industry collaboration and build greater knowledge of what really works. Our Appetite for Change research tells us that 57% of people are open to changing their diets to be healthy and more sustainable, and they welcome help to do it. But we also know that people don’t like to be told what to do and information alone is unlikely to change behaviour.

“We believe consumers will make this transition if we make it easier for them; that’s why we are delighted to be partnering with our industry project group and our research partners at the University of Leeds, to pilot this series of interventions over the coming months. The team at LIDA are experts in capturing, storing and analysing big data and have a variety of academic specialties that will be critical for this work.

The work being carried out by CDRC researchers at the University of Leeds is unique because it will use the secure infrastructure at LIDA to allow retailers and manufacturers to share anonymised transaction data over a sustained period of time.

It is hoped that the results of the first pilot trial will be published towards the end of this year.

Further information:

The attached image is courtesy of IGD

For media enquiries contact University of Leeds press officer Kersti Mitchell at k.mitchell@leeds.ac.uk.

The University of Leeds

The University of Leeds is one of the largest higher education institutions in the UK, with more than 38,000 students from more than 150 different countries, and a member of the Russell Group of research-intensive universities. The University plays a significant role in the Turing, Rosalind Franklin and Royce Institutes.

We are a top ten university for research and impact power in the UK, according to the 2014 Research Excellence Framework, and are in the top 100 of the QS World University Rankings 2021.

Being A Data Science Intern

Photo of Rosalind Martin outdoors wearing a blue coat and a scarf

Being A Data Science Intern – insights, challenges and benefits

Rosalind is one of the Leeds Institute for Data Analytics’s (LIDA) current Data Scientist Interns, with a background in Geography (BSc) and Geographical Information Systems (GIS MSc).

I’ve always been a fan of physical geography, but as module choices expanded throughout my degrees I was increasingly drawn to (spatial) data modules. I love using GIS and coding to solve big data challenges.

My internship has been made up of two six-month projects, both funded by the Consumer Data Research Centre (CDRC). My first project was titled ‘Isolation and Exclusion in a Social Distancing Covid World’. Here, I worked under the supervision of academics from the Universities of Newcastle and Leeds, aiming to identify people and households at risk of isolation and exclusion as a result of Covid lockdown rules.

Photo of Rosalind Martin outdoors wearing a blue coat and a scarf

My second project is in the world of nutrition where I’m working closely with Leeds academics, Dr Michelle Morris and Vicki Jenneson, and a retail partner. I am designing an open access tool which will assist retailers in implementing new policy restricting the promotion of foods that are high in fat, salt and sugar – a crucial part of reducing obesity in the UK.

What has been my experience of the LIDA Internship Programme?

Aerial view of desk with hands over a laptop keyboard, pot plant, glasses and pen

As I’m sure many people would echo, the Covid pandemic has placed our jobs in unfamiliar situations. The reality of this internship being my first full-time post means that I’ve not been comparing my days to ways I have worked in the past. Instead, my experience has been shaped by remote team working with virtual training, coffee breaks and meetings. Although working from home (WFH) comes with its own challenges and complexities, I believe this has given me the capacity to be thankful to work on engaging projects rather than pining for something I used to have!

Due to the pandemic, many interns have been able to experience otherwise inaccessible conferences and workshops as they’ve transitioned online. I’ve been to events held by The Alan Turing Institute, the Royal Society, CDRC and more! Working as a remote cohort, the interns have set up coffee breaks and a weekly “pub” session to replicate those water-cooler conversations, lost due to WFH. This space allows us to talk about our projects, seek help from others who have different skillsets and to simply get to know each other.

What have I been proud to have accomplished so far on the internship?

Coding while WFH has been a true test of my perseverance. In the absence of spinning my chair around to ask for a fresh pair of eyes, I’ve really had to learn how to use documentation and online forums to navigate my coding challenges. I’ve also learnt how best to send questions (with reproducible examples) to other interns or my supervisors. I’ve seen a visible increase in my confidence and ability between my first and second projects, and I know this skill will continue to serve me in future careers. 

What are my quick hacks for getting the most out of the internship?

  • Obtaining data always takes longer than you think: be proactive in learning methods, using dummy data and reading around the subject while you wait
  • Talk to the interns: each intern has a different background and therefore their own unique combination of skills. Ask questions and be ready to offer your own experiences if asked
  • Write detailed descriptions of your GitHub commits: your future self will thank you when you return from Annual Leave to find you have a detailed record of what you were working on before you left for your holiday

How has working with the Consumer Data Research Centre (CDRC) helped with the delivery of my first project?

My first intern project aimed to identify those at risk of isolation and exclusion under Covid lockdown rules. In order to make detailed predictions of impacted individuals and households, I worked with a micro-simulated synthetic population called SPENSER. This CDRC and Alan Turing Institute funded project was essential for me to make predications at the household level. I also used other datasets to support my work including CDRC’s Access to Healthy Assets and Hazards dataset. The availability of these datasets enabled me to explore the Covid restrictions that were thought to negatively impact an individual’s risk of isolation.

How will this Internship help me progress my career in data science?

I have learnt more of the mechanics of data access throughout both of my projects – ranging from obtaining freely-available through to applying for safeguarded datasets (including how long the process can sometimes take!). In my projects, I have had the opportunity to talk to the City Council, UK and international universities, not-for-profit organisations and retailers. Speaking to people in a wide range of data roles has helped me to better understand the opportunities available in data science, and how roles interact with non-data scientists. 

Why would I recommend the LIDA Data Science Internship?

The LIDA Data Science Internship has given me the opportunity to own the delivery of two data science projects situated in very different subject areas. This has really expanded my understanding of how data can be used to solve very complex but nationally topical challenges. Owning the delivery of the projects as someone straight out of their Master’s has been a challenge, but I have been well supported by experienced supervisors and the extended LIDA network. With the breadth of internship projects and collaborators available across and in partnership with LIDA, the internship is the place to be!

LIDA is currently recruiting for its next cohort of Data Scientist Interns, due to start at the end of September 2021, with several projects taking place within the CDRC. Click here for more information and to apply.

Mapping Inequalities in COVID-19 Vaccine Accessibility

Mapping Inequalities in COVID-19 Vaccine Accessibility

Successful roll-out of COVID-19 vaccines requires complex logistical delivery to help ensure everyone can receive their dose. England has established over 1700 vaccination sites distributed across the country to help provide vaccines to the population. Excellent progress has been made so far with over 95% of eligible people having received their first dose. Despite this, there are concerns that gaps in the location of vaccination sites may limit the opportunity for equitable uptake in certain communities.

This blog explores the distribution of vaccination sites across England to identify if it is an important factor in explaining vaccination uptake.

Measuring Accessibility to vaccination sites

We used open data on the location of vaccination sites on the 26th of March 2021 from NHS England. There were a total of 1753 sites across England. The NHS notes that 99% of the population live within 10 miles of their nearest vaccination site.

We measure accessibility through estimating the time-weighted road network distance of each postcode in England to its nearest vaccination site. We then calculate the average for MSOAs (Middle Super Output Areas), equivalent to large neighbourhoods within towns or cities (average population size ~7000 people).

The map below shows accessibility to the nearest vaccination sites for England. As expected, accessibility is best in urban centres where the average drive-time to the nearest vaccination site is often <2 minutes, around 1 km on urban roads. In contrast, the largest travel times were in remote rural areas where access was poorest. Here, residents could often have to travel more than half an hour or over 20 km to access their nearest vaccination site. This suggests that the ‘as the crow flies’ metric to establish accessibility, used by the NHS to suggest that 99% of people in England live within 10 miles of their nearest vaccination site, may not suitably account for the true distance required to travel for the most remote populations. By our calculation 1.73% of postcodes in England are over 10 miles (~16 km) from their nearest vaccination site, with the most remote postcodes up to 57 km away by road. This may be particular issue for those who are unable to drive or may be avoiding public transport due to the pandemic; those who are the most important to get vaccinated.

Does accessibility matter for understanding uptake?

We next compare our accessibility data to data on vaccination uptake from NHS England for the 1st of April 2021. As of this date, all people over 50 years old and the clinically vulnerable were eligible for a coronavirus vaccine dose. For this reason, we focus just on adults over 50 years old. We also use data on population estimates from ONS.

The following map shows the percentage of the population that received their first dose. The majority of MSOAs (85.4%) have an estimated uptake >90% reflecting the success of the vaccination roll-out. However, there are some geographical inequalities with areas in the north of England and towards Wales that were previously shown to have poorer accessibility also having lower uptake. Interestingly there are also lower levels of vaccination uptakes in over 50s in some urban centres, particularly in and around London.

There appear two key groupings for those areas with lower than average uptake. Urban centres, where access is widely available, and the most remote areas where access is very poor. The high drive times that appear with these remote areas may be a barrier for uptake, reflecting isolated communities who are unable or discouraged to make this journey. The disparity within urban centres is likely to represent very different drivers, including poorer uptake among marginalised and ethnically diverse populations that Local Authorities are working hard to support.

Overall it appears that poor access to vaccination sites may affect vaccination uptake for only the most extreme examples. Poor uptake in urban centres presents an equally worrying issue, that requires further analysis. All the code and data to replicate our analyses can be found on GitHub.

Work completed by Cillian BerraganMark Green and Alex Singleton.

Masters Dissertation Scheme 2021

Zoom call with coffee

Masters Dissertation Scheme 2021

Zoom call with coffee

The CDRC’s Masters Dissertation Scheme has bounced back this academic year after the impact of the pandemic in 2020. For 2021 we have received a record total of 22 proposals from industry sponsors. 67 students applied for projects and 22 students were finally matched with 20 projects. Sponsors include: Barbour ABI (2 projects), Blinc Partnership, Cambridgeshire County Council, Carto, Entain Group, Here Technologies, Idealista, Institute of Place Management, International Organization for Migration, Local Data Company, Movement Strategies (2 projects), Pet Care Provider, Sainsbury’s, Tamoco, The Data City, The Registry Trust and Walgreens Boots Alliance

Applications were received from the following universities: UCL (38), City, University of London (6), Liverpool (6), Loughborough (6), Edinburgh (4), Westminster (2), Bristol (1), Glasgow (1), Leeds (1) Nottingham (1) and Oxford (1).

The broad appeal of the Masters Dissertation Scheme saw applications received from students studying across the following disciplines: GIS, Social and Geographic Data Science, Spatial Data Science, Advanced Quantitative Methods, Business Analytics, Environmental Change and Management, International Real Estate and Planning, Logistics and Supply Chain Management, Smart Cities and Urban Analytics and Sustainable Urbanism amongst many others.

For more details about the projects, please have a look at our website. If you are interested in participating in the scheme next year, please email Melanie Chesnokov.

Celebrating collaboration: the CDRC Masters Dissertation Scheme

Celebrating collaboration: the CDRC Masters Dissertation Scheme

Celebrating collaboration: the CDRC Masters Dissertation Scheme. Thursday 29th April 2021, 10:30-15:00.

The CDRC Masters Dissertation Scheme, now in its tenth year, has been successfully run by the Consumer Data Research Centre for the last seven years. The event celebrated the success of the scheme, and explored the changing nature of academic-industry collaboration. Masters students who had gone through the scheme presented project case studies, and a selection of alumni spoke of the positive impact the scheme had had on their data science careers. A panel session rounded off the event with a discussion of the possibilities and ambitions for the next seven years of the Masters Dissertation Scheme. The event was attended by industry partners, MDS alumni, and the CDRC team including Paul Longley, Alex Singleton, and Jonathan Reynolds.

Speaker biographies

Programme

1030-1130: The Business of Engagement. Session recording (Longley 0:06, Dugmore 7:05, Reynolds 28:27, Squires 41:21)

  • Introduction & welcome: Professor Paul Longley, Director, CDRC
  • The evolution of academic-industry collaboration: Keith Dugmore, Demographic Decisions. Slides
  • CDRC: Where are they now? MDS 7 years on: Dr Jonathan Reynolds, Deputy Director (Oxford), CDRC. Slides
  • The business of engagement: the firm’s perspective: Martin Squires, Director of Advanced Analytics, Pets at Home. Slides

1145-1245: Alumni presentations. Session recording (Murage 2:16, Davies 25:10, Tonge & Montt 45:53)

  • Nombuyiselo Murage, Tamoco. Dissertation at Tamoco. MSc Geographic Data Science, University of Liverpool. Slides
  • Alec Davies, Pets at Home. Dissertation at Sainsbury’s. MSc Geographic Data Science, University of Liverpool, PhD Geographic Data Science. Slides
  • Christian Tonge, Movement Strategies. MSc Geographic Data Science, University of Liverpool, and Cristobal Montt, Movement Strategies. MSc Data Science, City, University of London. Dissertations at Movement Strategies. Slides

1400-1505: Alumni presentations (continued) and panel discussion. Session recording (Ushakova 1:48, Samson 21:29, Panel 37:26)

  • Alumni presentation: Dr Anastasia Ushakova, Senior Research Associate, University of Lancaster. Dissertation at British Gas.
    MSc Public Policy, UCL; PhD Computational Social Science. Slides
  • Alumni presentation: Nick Samson, Associate Director, CBRE. Dissertation at British Gas. MSc Geographic Information Science, UCL. Slides
  • Panel Discussion. The next 7 years. Achievements and ambitions: Alex Singleton, Deputy Director (Liverpool), CDRC;
    Samantha Hughes, Analytics Innovation Manager, Avon; Martin Squires, Director of Advanced Analytics, Pets at Home.
  • Thanks & conclusion: Professor Paul Longley, Director, CDRC

Nick Samson, 2014 MDS alumnus. Dissertation at British Gas. Project title: Can smart meters save consumers and British Gas money and carbon by pinpointing which consumers are most likely and best placed to install insulation in their homes?

New Insights into workplace and retail dynamics for England and Wales

New Insights into workplace and retail dynamics for England and Wales

CDRC data scientist intern Sebastian Heslin-Rees, working with Dr Nik Lomax, Dr Stephen Clark, and Dustin Foley developed a classification of commercial and employment land use in England and Wales using location and time-series data

Commercial areas and the businesses that inhabit them are not just an important addition to the vitality of urbanised areas but in many ways are essential to the ability of these places to flourish. This project has been utilising the newly available Whythawk dataset to construct a model for presenting and thus, understanding the spatial distributions of commercial areas across England and Wales. Largely, this has involved clustering workplaces of similar characteristics to distil a set of key workplace types, which can then subsequently be mapped and analysed. The Whythawk dataset is more detailed and up-to date than previous workplace/commercial classifications, which have been built from 2011 census data. Consequently, this could provide additional insights and novel avenues for academic research, policy initiatives and location analysis.

Data and methods

The Whythawk data contains details of commercial properties across England and Wales. It contains data such as the type of commercial property, the floor space, and employee count and business revenue. The data comes from both Valuation Office Agency and from local councils.

At the heart of our methodology is an unsupervised machine learning approach known as K-means++. Essentially, K-means++ groups variables of similar characteristics into the same cluster, to distil a specified number, K, of distinct clusters. It does this by minimising the total squared Euclidean distances between the cluster centroid and the data points within that cluster. In our case we used the percentages of floor space of each commercial type per postcode zone (e.g. LS15 8G). To add another layer of nuance to our classification and help further the distinctions between the clusters, we also generated and included an array of additional factors. These factors were selected based upon how they could impact the perceived attractiveness of an area, especially when viewed through the lens of retail and commercial attractiveness. For this we created an index of commercial diversity, rates of crime per business, as well as including measures for degree of urbanisation and accessibility by rail, road and bus.  

Key findings

We produced nine distinct classification types from the k-means clustering algorithm, labelled as follows: Urban mixed commercial land use (Retail focused), Public services, Diverse Industrial and warehousing areas, Urban office spaces, Less urbanised mixed commercial land use (warehousing, retail and leisure spaces), Low diversity Industrial areas, More urbanised and diverse public services, High street retail and As yet untitled (mixed). Moreover, there was also substantial variation in distribution across the nine clusters when examining our additional variables (Crime per business, Diversity, Degree of urbanisation and Accessibility). For instance, Figures 1 and 2 below display an example of the composition of clusters 1 and 4. We can see that the clusters are distinct in their composition of commercial activity. Notably, cluster 1 demonstrates significant diversity of commercial activity, whilst incorporating a large retailing component, whereas cluster 4 has a very low diversity focusing mostly on office spaces.

Figure 1 Catplot displaying the composition of cluster one, urban mixed commercial land use

Figure 2 Catplot displaying the composition of cluster four, urban office spaces

The clusters were subsequently mapped at Unit Postcode level. All postcodes with a cumulative commercial floor space below 100m2 were removed, so that the spatial distribution and characteristics of key commercial space can be examined. Two examples of this mapping can be seen below in Figures 3 and 4.

Figure 3 Map of Greenwich (SE10) in South-East London by commercial cluster type

Figure 4 Figure 3 Map of Leeds city centre (LS1) by commercial cluster type

Lastly, this model can be combined with other data points to provide additional utility for businesses. One avenue for this is examining how business rateable and rentable values compare across the distinct cluster types. For instance, clusters 0, 2 and 4 have their mean and median rental and rateable values significantly above clusters 1, 5 and 8.

Value of the research

The results could be used by businesses to readily locate commercial areas of interest when performing tasks such as determining optimal locations for new store outlets. Additionally, this model can be used in conjunction with many other research endeavours concerning urban analytics that seek to determine the characteristics and dynamics of urban areas. For example, this may be in terms of examining workplace and neighbourhood dynamics, commuting flows as well as retail and high-street health.

Insights

  • Utilising novel datasets combined with unsupervised machine learning.
  • Developing a unique classification concerning commercial land use across England and Wales.
  • Providing insight into urban dynamics.

Project Team

Sebastian Heslin-Rees – Data Scientist Intern, University of Leeds

Dr Nik Lomax – Project supervisor, University of Leeds

Dr Stephen Clarke – Research fellow, University of Leeds

Dustin Foley – Data scientist, University of Leeds

Partners

Whythawk

Consumer Data Research Centre (CDRC)

Funders

Consumer Data Research Centre (CDRC)

Local Data Spaces: Supporting Local Authority Covid-19 Response

Aerial shot of random English town

Local Data Spaces: Supporting Local Authority Covid-19 Response

Covid-19 has strained already insufficient Local Authorities resources, with infection and transmission of Covid-19 further exacerbating existing social inequalities. Four CDRC academic researchers (Dr Mark Green, Dr Jacob MacDonald, Dr Maurizio Gibin and Simon Leech) have been working for the past 6 months using the Office for National Statistics Secured Research Service (ONS SRS) on the Local Data Spaces project.

The Local Data Spaces (LDS) was a novel collaboration between the Joint Biosecurity Centre (JBC), the Office for National Statistics (ONS), and ADR UK. This project was set up to support local authorities, groups and stakeholders respond to the COVID-19 pandemic using granular and secured data and research driven analyses.

After engaging the JBC and 25 local authorities, we identified two consistent core research priorities which focused on broader COVID-19 health impacts and inequalities, and on economic vulnerability and recovery potential. From this, we developed a series of nine reports leveraging the secured data available through the SRS infrastructure – and further replicable and generated consistently for all local authority regions across the country (and available via the CDRC Geodata Packs platform).

For each local area, a set of reports are built to profile the themes of:

  • Demographic Inequalities in COVID-19;
  • Ethnic Inequalities in COVID-19;
  • Geospatial Inequalities in COVID-19;
  • Excess Mortality;
  • Occupational Inequalities;
  • Population, Housing and Affordability;
  • Industry Densities; Economic Vulnerabilities;
  • Human Mobility.

One of the outputs in the reports, allowing used to compare changes in retail and recreation over time for the country (area) and their local authority (line).

We made use of the highly detailed administrative and survey datasets held securely within the Office for National Statistics (ONS) Secure Research Service (SRS), including core national data products such as NHS Test and Trace, the COVID-19 Infection Survey, The Business Structure Dataset (BSD) registry and the Business Registry and Employment Survey (BRES). Non-disclosive research work was conducted within the SRS environment, and generated into the series of reports for each area across England. These data sources were supplemented where relevant with openly available datasets such as the ONS Population Estimates, Google Mobility Data, and CDRC open data products such as the CDRC Business Census, and Access to Healthy Assets and Hazards (AHAH).

From our meetings with local stakeholders, it became clear the huge variation in resources available for research and analytical capacity, and that the Covid-19 pandemic has stretched resourcing within local authorities. Local authorities co-designing analyses alongside the research team ensured the reports generated were relevant and useful, and helped fill evidence gaps at local levels.

We created non-disclosive outputs from the ONS SRS packaged into a series of reports for each local authority district in England. These reports are available through the CDRC Geodata packs platform for any local stakeholder to download. All R scripts, both for data cleaning and analyses are available for re-use by local authority analysts or local researchers in the future, enabling reproduction and even extension of the analyses. The openly-available (appropriately disclosed where necessary) code and workflow pipelines used to clean and format these datasets and produce final reports provide a number of practical efficiencies. Where local analysts have limited resources or capabilities in accessing, working and analysing massive national studies and datasets, cleaned scripts and code to bypass the data wrangling stage can be invaluable when rapid-response research outputs are needed. Alongside this, we hope this may empower those local authorities with lower analytical capacity to be able to access granular data to inform local level evidence bases.

Another output from the data pack reports, allowing users to compare positive Covid-19 rates by work sector for England (green) and their area (purple).

In the short term, reports will be used by local authorities and stakeholders, allowing them access to an evidence base of the impact of Covid-19 at a local level. The way the reports and replicable code are available to other accredited researchers within the SRS (and available appropriated disclosed external to the SRS) allow local authorities to explore these avenues for their own local research priorities. Locally focused research and data is clearly in demand and this resource will be a key part in local authorities’ response to Covid-19.

These reports and data can be accessed here:

New paper: Identifying dietary patterns in supermarket transaction data and their nutrient and socioeconomic profiles

Blurred image of a supermarket aisle - products not distinguishable

New paper – identifying dietary patterns in supermarket transaction data and their nutrient and socioeconomic profiles

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns.

In a recent paper in Nutrients CDRC researchers Dr Stephen Clark and Dr Michelle Morris used 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns.

Results identified seven dietary purchase patterns that they named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders – who may buy as an individual or for a household – is less than the adult reference intake, but this varies according to dietary purchase pattern.

In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

Read paper in full

Post-Covid Resilience of Commuter Towns

Post-Covid Resilience of Commuter Towns

Utilising open source and aggregated retail data through the CDRC secure data service, co-funded Ph.D. student Abigail Hill has created an index of retail resilience and recovery from the Covid-19 pandemic for English commuter towns. Business partner Retail Economics has interested in improving understanding of the impacts of increased ‘working from home’ on commuter town high streets and their immediate and longer-term impacts upon retail resilience.

Abigail’s analysis develops six case studies and finds that of these Guildford has the most resilient commuter town high street, while Rochdale has the least. Cluster analysis also reveals that despite Rochdale high street’s relative weakness, some of the retail areas that adjoin it have better prospects, especially where retail activity projects a strong and unified image. GIS analysis also found that there are specific parts of both Guildford and Rochdale high streets that share similar levels of retail vacancy and occupier turnover and which may each require tailored interventions to restore stability.

Classifications

This research project was carried out as part of a co-funded Ph.D. with the Local Data Company (LDC) under the ESRC Accelerating Business Collaboration scheme. The work had two related components.

First, a resilience index for commuter towns was developed using data sources to represent four domains: wealth, vacancy, retail composition and consumer spending. Office for National Statistics (ONS) open data were used to create indicators of local income, occupational structure, house prices, relative location and consumer spending. Local Data Company data on location, retail unit type and vacancy were used to create summary indicators of high street and adjoining area vacancy rates, levels of trading in essential retail categories, share of chain store occupancy and presence of leisure venues. The methodology entailed data standardisation and factor analysis.

In the second stage, the highest and lowest ranked towns were used to develop detailed case studies. Retail boundaries were developed using LDC data and used to explore the vitality of high streets and adjoining areas. DBSCAN and hierarchical clustering techniques were used to identify areas within high streets that merited locally targeted interventions.

The majority of the data sources used were open data. Secure LDC data were also used to create data aggregations used to create retail area boundaries.

Reflecting on the project, Retail Economics CEO Richard Lim said:

This was an extremely valuable piece of research to Retail Economics which focused on a very important emerging trend in the industry. The research was timely, relevant and forward looking. The process was also well-managed and all stakeholders worked together well to add value in their respective areas of expertise.

In particular, Abi was well organised, enthusiastic and a great communicator which helped keep the project on track and delivered within the time scales set out at the start of the year. The final presentation of the research was delivered in an engaging and succinct manner, aligning very much to the business community.

We will look to leverage value out of the research in our internal analysis to assess the impact of Covid-19 on shopping habits with a particular focus on the commuter belt. There is a depth of quality and rigour within the research that provides confidence in the initial findings that we can share with our clients. Overall, an excellent piece of research.

The Retail Economics website is https://www.retaileconomics.co.uk/about.

Digitising historical telephone directories with BT Archives: GISRUK Best Short Paper Award

Digitising historical telephone directories with BT Archives:
GISRUK Best Short Paper Award

CDRC collaborative Ph.D. student Nikki Tanu and Senior Research Fellow Dr. Maurizio Gibin were awarded the ‘Best Short Paper’ award at the 2021 GIS Research UK (GISRUK) (http://cardiff.gisruk.org/) online conference on 16 April 2021. Their paper, ‘Georeferencing historical telephone directories to understand innovation diffusion and social change’ (https://doi.org/10.5281/zenodo.4665847), described pioneering work in the digital capture and georeferencing of the 1881 telephone directory. The work demonstrates proof of concept that will be rolled out to digitise selected directories up to the 1980s and will make it possible to chart the spatial and social diffusion and use of fixed line telephony in the 19th and 20th centuries.

CDRC researcher Meixu (May) Chen was joint winner of the Sinesio Alves Junior Prize (https://www.ucl.ac.uk/bartlett/casa/remembering-sinesio-alves-junior) for work undertaken whilst she was working at CDRC in Liverpool.

Congratulations to May, Maurizio and Nikki!