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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 (IGD), to test different ways to encourage healthy and sustainable eating.

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 the 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 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.

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.

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.


  • 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



Consumer Data Research Centre (CDRC)


Consumer Data Research Centre (CDRC)

Local Data Spaces: Supporting Local Authority Covid-19 Response (Data Story)

Aerial shot of random English town

Local Data Spaces: Supporting Local Authority Covid-19 Response (Data Story)

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.

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.

London house price increases linked to areas with more Airbnbs

London house price increases linked to areas with more Airbnbs

Areas in London with more Airbnb listings are more likely to see increases in house prices, according to a team of researchers from the CDRC at UCL.

In a study published in Environment and Planning B: Urban Analytics and City Science, UCL researchers were able to track the relationship between the number of properties listed on the short-term letting platform and the changes in house price in the area – measured in £ per square metre – using a comprehensive listings database and house price data.

Across Central London, researchers found a tendency towards increased house prices in areas with more Airbnb listings, with large parts of Kensington and Chelsea, Westminster and Camden exceeding rises of £20 per m2 per month from January 2015 to May 2018. The highest increases across the city as a whole occurred north of the River Thames, with more modest growth towards the South and East.

an image of a map of London with ares colour coded by property price change.

Despite the generally positive trend, the findings also revealed important contrasts where some areas had a negative association with house prices and experienced reductions of more than £20 per m2.

Whilst more research is needed to understand why such differences occur, the findings paint the most detailed picture yet of Airbnb’s interaction with the London housing market. With boroughs broken down into smaller areas containing between just 400 and 1,200 households, the researchers argue that the level of granularity presented provides local decision-makers with the detail needed to produce better, more localised, housing policy to inform regeneration strategies.

Professor James Cheshire (CDRC Deputy Director, UCL) said: “The housing market in London is complex and there is still a lot we don’t know about how platforms such as Airbnb are interacting with local price fluctuations. Thanks to the detail in this analysis, we can begin to take a closer look to understand some of the patterns that are emerging and the reasoning behind these.”

Whilst the data used in the analysis span January 2015 to May 2018 and capture a pre-pandemic city, the researchers believe their findings could help to inform London’s Covid-19 recovery.

“Central London’s rental market has cooled dramatically over the last year, as many workers and tourists have left the city during lockdown, so we have an opportunity to influence its recovery,” Professor Cheshire added. “Policymakers can either help areas return to business as usual – or take a closer look at both the costs and the benefits that platforms like Airbnb can bring to communities”.

Currently, London is one of a handful of cities on Airbnb to have restrictions on the number of days “entire home” listings can be occupied per year, which is no more than 90 days. Introduced in 2017, this measure was implemented by the platform to ensure the sustainable and responsible growth of home sharing in the city.

The research team note that previous studies have found that Airbnb takes up around 1.4% of the total housing supply in London.

The data used in the study were supplied by the ESRC-funded Consumer Data Research Centre and the analysis was conducted by James Todd (UCL Geography), Dr Anwar Musah (UCL Institute for Risk & Disaster Reduction) and Professor James Cheshire (UCL Geography and Consumer Data Research Centre).

Job Opportunity: Business Development Manager

Job Opportunity: Business Development Manager

Are you skilled in building and sustaining relationships between academics and external organisations? This role offers an excellent opportunity for those keen to work in an exciting and multidisciplinary environment.

The CDRC continues to grow and as such is seeking a talented and highly motivated Business Development Manager, based at the University of Leeds, who can help us maintain and build our relationships with businesses and other external organisations. We are looking for someone who can oversee a portfolio of partnerships and who will contribute to the ongoing business development strategy of the CDRC.

You will provide a vital bridge between the Centre and the business sector, maintaining and building relationships with existing data providers and encouraging new data partners to work with the Centre. In this capacity, you will carry significant responsibility for building the Centre’s business and data portfolio upon which the Centre’s core services depend. You will also be responsible for working alongside professional service teams at the University of Leeds to articulate and execute the legal agreements and data sharing agreements which underpin consumer data operations. You will work directly with the Centre’s co-Directors on implementing the Centre’s business development strategy and work closely with the Centre’s Public Engagement and Communications Officer to ensure that the Centre achieves impact.

Find out more and view full candidate brief.

World Book Day 2021 – Data Science Interns

World Book Day 2021 – Data Science Interns

To celebrate World Book Day we spoke to some of our Data Science Interns about their favourite reads. 

George, Rosalind, Stuart and Simon shared the stories they loved to read as a children and teenagers, and discussed the books that have had the biggest impact on their careers to date.  

George Breckenridge & Stuart Ross

Stuart and George have been working with us over the past six months to Analyse COVID-19 Mobility Responses through Passively Collected App Data. They shared some of their work in our recent blog analysing patterns of Christmas mobility in the UK


What was your favourite book as a child?

‘Who Was Isambard Kingdom Brunel’ (2006) by Amanda Mitchison – I think I read this when I was about 8, which is crazy looking back! A short biography of Brunel and his engineering feats which I think instigated a life-long fascination with our collective journey into the depths of underground civil engineering. 

What was your favourite book as a Teen?

‘Population 10 Billion’ (2013) by Danny Dorling – I’ve always loved Danny Dorling’s writing and this book on demography represented a cornerstone of my reluctantly-optimistic teenage outlook. Its insight that future global resource issues are mostly a product of imbalances in ‘consumption’ rather than global ‘overpopulation’ remains, in my personal view, underappreciated. 

Favourite data related book?

‘Urban Analytics’ (2017) by Alex Singleton, Seth Spielman & David Folc – This concise textbook served as my gateway into truly understanding the diversity and dynamism of urban analytics, perfectly pitched as an introductory text.

Book that has had the greatest impact on your career to date?

‘Imagined Londons’ (2002) by Pamela K. Gilbert (eds). – At a time when I needed it most, this book put theoretical rocket-boosters into my undergraduate dissertation on urban geography, which in turn contributed to my BA classification very helpfully! 


What was your favourite book as a child?

Where’s Waldo by Martin Handford

What was your favourite book as a teen?

This one would be The Old Man and the Sea by Ernest Hemingway

Favourite data related book?

Python for Dummies by Stef Maruch – Great for learning the basics of Python and I still refer back to it from time to time to brush up before an interview. 

Book that has had the greatest impact on your career to date?

An Introduction to Species Distribution Modelling (SDM) Using QGIS and R by Colin D. MacLeod – This is the first book I actually followed all the way through and used as a tutorial to teach myself the basics of SDMs. 

Simon Leech

Simon is working with the CDRC team on a project with the Office for National Statistics (ONS), funded by Administrative Data Research UK (ADR UK) – The Local Data Spaces Pilot. He recently shared his experiences of hybrid working during the pandemic.

Simon, what was your favourite book as a child?

Any from the Horrid Henry Series by Francesca Simon – I remember reading so many of these as a child, and reading them over and over again!

What was your favourite book as a teen?

Gerrard: My Autobiography (Steven Gerrard) – I have to confess I did not read enough during my teenage years, but remember almost exclusively reading footballer’s autobiographies when I did pick up a book! As a Liverpool fan this is the only choice really!

Favourite data related book?

Algorithms of Oppression: How Search Engines Reinforce Racism (Dr Safiya Umoja Noble) – I attended the Open Data Institute 2020 Summit, and found the talk given on this subject very interesting and thought provoking, so I went ahead and bought the book to learn more about the current information ecosystem. 

Book that has had the greatest impact on your career to date?

Spatial Microsimulation with R by Robin Lovelace and Morgane Dumont – I followed this free book closely to produce a spatial microsimulation for assessing Vulnerability to Personal Carbon Allowances for my GIS Master’s Dissertation, something that pushed me to apply for this role as I enjoyed the work so much!

Rosalind Martin

Rosalind has been working with us for the last 6 months to explore Isolation and Inclusion in a Post-Social Distancing COVID World.

Rosalind, what was your favourite book as a child?

Anything by Michael Rosen.

What was your favourite book as a teen?

The Count of Monte Cristo, by Alexandre Dumas.

Favourite data related book?

Moby-Duck: The True Story of 28,800 Bath Toys Lost at Sea, by Donovan Hohn – Set within an entertaining true story, this book introduced me to using data and spatial mapping to understand real events.

Book that has had the greatest impact on your career to date?

How to Lie with Maps, by Mark Monmonier – As an aspiring geographer at the time of reading, Monomier was the first to teach me to develop a critical eye when looking at maps, and how to differentiate the good from the bad in a context where all maps must lie in one way or another.