Here at the Consumer Data Research Centre we’re committed to using consumer data for the public good and we are looking for a Research Software Engineer who can help us deliver impact from our data assets and projects.
We are looking for someone who is data-literate, driven and creative – you will have the opportunity to work with our team (academic, technical and professional services) to build and maintain interactive web based data visualisations and other web applications (such as our Priority Places for Food Index or our Nutrient Profile Model); contributing to our mission to develop innovative and impactful data products and outputs.
You should have a good understanding of project delivery, from inception through to dissemination; have experience in dealing with a range of internal and external stakeholders; and be comfortable providing technical advice, including to non-experts.
It’s a really exciting time here at the Consumer Data Research Centre and we’re looking for an Administrative Assistant to join us to support our team to deliver research projects using consumer data for public good.
We’re looking for someone with strong administrative and organisational skills, who has experience of working in a busy and fast-paced office environment to provide operational support to our team of Directors, professional services staff, data scientists and academic co-investigators. This will include tasks such as diary management, minuting meetings, raising purchase orders, as well as room and catering bookings.
You will also have the opportunity to work closely with key members of the team to help deliver events and training courses and gain experience of working on the Centre’s website and social media channels.
In return we offer:
26 days holiday plus 16 Bank Holidays/days that the University is closed by custom (including Christmas).
Generous pension scheme options plus life assurance.
Health and Well-being: Discounted staff membership options at The Edge, our Campus gym.
Personal Development: Access to courses run by our Organisational Development & Professional Learning team, and self-development courses including languages, Creative Writing, Well-being Therapies and much more.
Access to on-site childcare, shopping discounts, and travel schemes are also available.
We have been working with Wejo to make connected vehicle data available for academic research purposes.
We have today launched a new dataset, supplied by Wejo, which contains GPS trajectories for around 50,000 vehicles during the month of July, representing over 1.8 million vehicle journeys and over 400 million individual records. An observation is available every 3 seconds on average during each journey.
The data contains a journey identifier, timestamp, longitude and latitude coordinates, as well as additional data fields for vehicle speed and bearing. The data is of high quality, with GPS records for every three seconds on average. This enables the successful implementation of map-matching algorithms as well as the identification of vehicle stops as well as periods of acceleration and deceleration.
This is one of the first times such a detailed and in-depth dataset detailing connected vehicle trajectories has been made available for academic research purposes.
Thinking of applying to our 2023 Masters Dissertation Scheme (MDS)?
We asked the winners of the 2022 MDS prize about what attracted them to the MDS and how they got involved. In these short videos Aindrila and Alex tell you about the benefits of the MDS and how their experience, working with industry, is supporting their career development.
Disa Ramadhina, winner of the Best Dissertation 2021, went straight to a Data Analyst role, following her Masters. In this YouTube clip Disa describes how she was able to add value to the partnership between academia and industry and how the MDS experience helped progress her career at Entain.
Thinking of applying to our 2023 Masters Dissertation Scheme (MDS)?
Ten years ago, the Economic and Social Research Council funded a nationwide collaborative Masters Dissertation Scheme for the first time. Over the years, businesses, government and third sector organisations have collaborated with universities throughout the UK to allow Masters students to develop practical solutions to problems through applied research, often using their own sources of data that are not usually available for student projects. Sponsoring organisations solve problems, students get real world problem-solving experience, and many students have gone on to work for the organisations that sponsored their research projects. This year’s projects can be viewed here. As in previous years, there is likely to be very considerable interest in these projects, so interested students are advised to apply as soon as possible!
CDRC data used to understand population displacement from gentrified London council estates with surprising results
New research using our novel Linked Consumer Registers has found that the demolition and associated redevelopment of council estates in London has displaced former residents, potentially separating them from their existing employment, education and care networks. Yet around 85% of the displaced in remain London, and most stay within the same Borough. This suggests that the scale of displacement may be less than has previously been suggested, particularly in the popular press. However, there is also evidence of increasing numbers of moves out of London to the Southeast and East of England.
The full paper in published in Environment and Planning A, authored by Jon Reades, Loretta Lees, and Phil Hubbard.
AHAH is a multi-dimensional index for Great Britain measuring how “healthy” neighbourhoods are based on the locations of services that are ‘assets’ or ‘hazards’ for health in each area.
AHAH is calculated based on data from 15 indicators divided into four domains:
Health services – GPs, hospitals, pharmacies, dentists, leisure services
Air quality – air pollution levels for Nitrogen Dioxide, Sulphur Dioxide and Particulate Matter (PM10)
Natural environment – green spaces and blue spaces
The resource 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. Component inputs for this index use data that is the most up-to-date as of March 2022.
All of the individual indicators to AHAH have also been made freely available in the data resource in a push for opening up small area health data. As such, it provides one of the most comprehensive free data resource available for such data. You can freely explore how your local area compares on our data at https://mapmaker.cdrc.ac.uk/#/access-healthy-assets-hazards. AHAH and all of the individual indicators are openly available at https://data.cdrc.ac.uk/dataset/access-healthy-assets-hazards-ahah.
Changes from previous versions
The latest version of AHAH includes several methodological and conceptual refinements following extensive public feedback on previous versions.
Most noticeable is the change in how we measure accessibility to green spaces. We received a lot of user feedback, especially from rural communities, who did not feel that measuring distance to nearest accessible park or green space was the best measure since it under-represented rural areas (i.e., farmland that is green but not necessarily accessible).
In response, we have updated the measure to use a satellite derived measure of the total green space (NDVI). We note here the measure is for the resident population and their surrounding contexts.
This has improved the accuracy for measuring access to overall green space. These changes have resulted in changes in the overall AHAH index value, mostly seeing rural areas with good access to green spaces having better overall scores. As a result, we would suggest caution in making comparisons over time between the overall AHAH index.
We have also further updated our statistical approaches to give more accurate accessibility estimates and utilize GPU support for faster processing.
Researchers from CDRC have provided vital data analysis as part of London Borough of Camden’s Future High Streets programme
The aim of the programme is to ‘re-imagine’ high streets for the 21st century and ensure they are the heart of community and economy.
Even before the COVID-19 pandemic, high streets across Britain were facing a multitude of challenges, including:
the ongoing shift away from traditional retail to online shopping
a decline in footfall
higher costs due to rising business rates and commercial rent
The pandemic compounded these issues. National lockdowns closed non-essential stores and changed behaviours in various ways, with fewer commuters visiting central London locations.
In response, Camden Council devised the Camden Future High Streets programme to support its high streets through the pandemic and into a robust recovery and ready to face the future.
Professor James Cheshire, Deputy Director of the Consumer Data Research Centre and his team, collaborating with Camden, performed an extensive search for high street related data and datasets in six main categories: high street boundaries, mobility, economic, retail, social and demographic, and sustainability. They used pre-existing data to show changes over time and differences between high streets in order to inform local policy.
“We’re sort of a data of broker,” explains James. “Part of that role is acquiring data, processing it, and giving it back in a way that people can make good use of. But I also think we can be an impartial advisor on what data is good and what data is less good. We don’t have any real sort of commercial imperative to be selling a particular data product so we can impart an honest view.”
James and his team produced a detailed report, Data for Future High Streets, containing their insights and six recommendations of how data can be better used to improve resilience and vibrancy of high streets.
Abigail Hill, played an important role in the project, with elements of her PhD research included the report and utilised by Camden.
“The knowledge exchange with the London Borough of Camden has enriched my research that focuses on measuring the resilience of British high streets,” she says. “The project provided invaluable insights into high street regeneration projects and decision making, enabling my research to make an impact on local policy.”
Councillor Danny Beales, Cabinet Member for Investing in Communities, Culture, and an Inclusive Economy (Camden Council), comments: “It’s been a productive collaboration. Working with UCL provided valuable insights into how London Borough of Camden can use data, including the data we already hold, more effectively to support the recovery of our high streets after COVID.”
You can read more about the Resilience of British High Streets to the COVID-19 Lockdown Restrictions here.
How can supermarket delivery vehicle routing be optimised?
Supermarkets must transport large quantities of stock from depots to many different stores on a daily basis. It is important to deliver this stock efficiently, in order to minimise both transport costs and carbon emissions. This project aimed to investigate methods to optimise the routing of supermarket delivery vehicles. The project used the most basic delivery strategy, by which only one store is serviced during each trip from the depot, as a benchmark for comparison with the optimised delivery vehicle routing solutions.
Data and methods
The delivery vehicle routing optimisation was implemented using integer programming. The optimisation formulation was applied to a group of 81 stores which are all service by a single depot. For large groups of stores, the number of potential routes which a delivery vehicle can take is prohibitively large to be solved within a reasonable timeframe. Therefore, large groups of stores must be divided into smaller groups, based on location, and the optimisation solved separately for each group. Analysis of group sizes found that the groups of stores should contain 10 stores or fewer. As such, the 81 stores which were investigated were divided into nine groups of nine stores. This allowed the optimisation for all groups of stores to be solved within a relatively short time frame.
Key findings
The delivery vehicle routing problem was optimised for the nine groups of stores with 100 randomised sets of demand. It was found that the optimisation reduced the total required travel distance, when compared with the most basic delivery strategy.
Figure 1 shows the total travel distance required against the total demand for each group of stores, with the data coloured by the total number of trips required for each optimisation solution. For some groups, e.g. Groups 2 and 3, the data appears to be highly clustered, with each cluster representing how many trips were required by each solution. For other groups, e.g. Groups 5 and 6, the data doesn’t appear to be clustered. For groups showing data which is clustered, there is little or no overlap in the total required travel distance between solutions requiring different numbers of trips. Conversely, for groups which do not show clustered data, there is significant overlap in the total travel distance between solutions requiring different numbers of trips.
It was found that groups of stores for which the stores were close to one another, relative to the depot, produced highly clustered data, as each additional trip from the depot and back required by the solution increased the total travel distance by a significant proportion. Whereas, groups for which the stores were not as close to one another, relative to the depot, did not produce clustered data as each additional trip from the depot and back increased the total travel distance by a smaller proportion.
Figure 1. Travel distance vs demand for each group
Value of the research
This project has demonstrated a method to optimise delivery vehicle routing which can reduce the distance travelled by delivery vehicles significantly, when compared with the most basic delivery strategy and provides a valuable proof of concept study. The methods investigated in this project may be developed further by WM Morrisons Supermarkets in order to optimise delivery vehicle routing, which could help to reduce both transport costs and carbon emissions.
Quote from project partner
This 6-month research project provided an interesting and insightful investigation into optimisation approaches for delivery vehicle routing. I found the collaboration with Jacob and the wider project team valuable and would welcome the opportunity to work with LIDA on other projects in the future.
Insights
Optimisation of delivery vehicle routing can reduce transport costs and carbon emissions.
Dividing large groups of stores into multiple, smaller groups allows the optimisation to be solved more efficiently.
Research theme
Urban analytics
Statistical and mathematical methods
People
Jacob van Alwon – Data Scientist, Leeds Institute for Data Analytics.
Jon Ward – Lecturer in Mathematics, University of Leeds.
Areas most at risk of food insecurity in the cost of living crisis identified by CDRC and Which?
In a groundbreaking new study researchers from the Consumer Research Data Centre at the University of Leeds and Which? have identified the places around the UK where households are most at risk in the cost of living crisis and likely to be in need of extra support to access affordable, healthy and sustainable food.
The CDRC team, led by Dr Michelle Morris, and the consumer champion developed the Priority Places for Food Index as part of Which?’s national campaign to urge supermarkets to support consumers through the cost-of-living crisis.
The index uses data across a range of relevant dimensions to rank local areas by the likelihood of the people living there needing support.
The researchers considered factors such as deprivation, poor access to affordable food, having no large supermarkets nearby, a lack of online shopping deliveries or circumstances such as no car access making it difficult to shop around. All of these factors can make it difficult for people to find healthy and affordable food.
Michelle Morris, Associate Professor Nutrition and Lifestyle Analytics, University of Leeds said: “With so many people in the UK already suffering from food insecurity and the cost of living crisis making that much worse, we need to do all that we can to support those most in need to access affordable, healthy and sustainable foods.
Our interactive map makes it easy to identify neighbourhoods most in need of support and highlights the main reasons that they need this support, recognising that one size does not fit all and that tailored help is required.”
“We will be engaging widely with the food industry and policy makers to help them use the tool to help our communities, both nationally and locally. Some of our local communities in Bradford have been identified within the top 20 Priority Places across the UK, which is very worrying.”
Which? – Affordable Food For All
Which? are using the index as part of its newly launched Affordable Food For All campaign, and have created a 10-point plan to help supermarkets provide the support people around the country desperately need in order to feed themselves through the ongoing crisis.
Sue Davies, Which? Head of Food Policy explained: “We know that millions of people are skipping meals through the worst cost of living crisis in decades but our new research tells us where around the UK support is most urgently needed.
The big supermarkets have the ability to take action and make a real difference to communities all around the UK. That’s why we’re calling on them to ensure everyone has easy access to budget food ranges that enable healthy choices, can easily compare the price of products to get the best value and that promotions are targeted at supporting people most in need.”
Priority places across the UK
Analysis of the Index shows that overall, seven in 10 UK Parliamentary constituencies have at least one area in need of urgent help accessing affordable food – but there are 16 constituencies across England and Wales for which at least three-quarters of the constituency are at risk.
Within England there is a large variation in where priority places are located across regions. The region with the greatest frequency of priority places is the North East, although because this is a small region then there are more priority places in Yorkshire and the Humber, the West Midlands and the North West in absolute terms. There are relatively few priority places in London, the South East and the South West, although in the latter there is a concentration in Cornwall.
In Wales, the highest concentration of areas at high risk during the food crisis is in the Valleys where proximity to a large supermarket or access to online deliveries may be very poor. Wales has a higher proportion of rural places where accessing affordable food is an issue than England and Scotland.
In Scotland, the places in highest need of support are in the Central Belt, according to the Which? and CDRC index, but there is also a notable concentration in and around Dundee where there is relatively poor access to online food deliveries and people are more likely to be suffering from fuel poverty and on a low income.
Northern Ireland has the most even geographical spread of areas in need of support accessing affordable food. However, there is a noticeably greater concentration in parts of south-west Belfast and in and around Derry/Londonderry.
The Priority Places for Food Index is a composite index formed of data compiled across seven different dimensions relating to food insecurity for the four nations in the UK. It is constructed using open data to capture complex and multidimensional aspects of food insecurity.
Building on the CDRC e-Food Desert Index (EFDI), but with additional domains relating to fuel poverty and family food support, the goal of the Priority Places for Food Index is to identify neighbourhoods that are most vulnerable to increases in the cost of living and which have a lack of accessibility to affordable, healthy, and sustainable sources of food.
