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Free School Meals through a local lens – Bradford

Two girls writing at school

Free School Meals through a local lens – Bradford

Alex Dalton and Tom Albone are part of the LIDA Data Scientist Internship Programme and are working closely with CDRC Health and Wellbeing theme lead Dr Michelle Morris. The shared focus of their intern projects is inequalities in the local Bradford area, and one of the pressing local issues affecting this local community (as well as families nationally) is free school meals.

Following on from their previous blog ‘Free School Meals: A National Necessity’in which they demonstrated the regional inequalities within England concerning eligibility and uptake of free school meals, with the data indicating that the Northern regions have the greatest eligibility for the service. This article considers the issue locally, focussing on the Bradford district. 

 

Free School Meals through a local lens – Bradford  

For many, January 2021 has not started as we had hoped. Although there is widespread positivity brought by the Coronavirus vaccination roll-out, at the time of writing, a third lockdown once again means that homes have to simultaneously encompass the office and the classroom as schools are closed to all but vulnerable children and the children of key workers. The Government has agreed to extend free school meals during this third lockdown. In this second blog article we have taken the opportunity to reflect on the free school meals issue, in a more local context. 

Food insecurity and child poverty are worse in economically deprived areas. Bradford (see Figure 1) is the 6th largest city in the UK, yet it routinely reports high levels of deprivation. It is ranked the 13th most deprived local authority in England (out of 317) and the 2nd in Yorkshire and the Humber region. Between 2015 and 2019 Bradford moved up six places from 19th to 13th in the overall list, indicating its worsening situation.¹ 

The recent Born in Bradford Better Start (BiBBS) Covid-19 Survey across families in Bradford found that 18% of children said they do ‘worry about how hard it is for parents to get enough food for us’ – a shocking statistic.  

Plot showing the percentage of students eligible for free school meals per school on census day in the 2019/20 Academic Year

Figure 1. Plot showing the percentage of students eligible for free school meals per school on census day in the 2019/20 Academic Year. Plots are by Local Authority, indicating the quartiles with bw=0.1 and a uniform scale across the plots. Percentages are calculated per school using data collected by the ONS across all schools in the UK (excluding independent schools). The plot of students eligible on census day is overlayed with the plot of students recorded actually taking free school meals on the census day (translucent violin plots).

Within Yorkshire and the Humber, a breakdown of students eligibility by Local Authority allows a comparative understanding of the Bradford area. Our analysis uses government data across all schools, excluding independent schools, for the 2019/20 Academic Year, see Figure 1.  

Comparing the shapes of the violin plots, it is clear that the number of students eligible per school ranges drastically between local authorities, as it does nationwide. The shorter plots (such as North Yorkshire) indicate that the schools in that area all have a similarly low eligibility. In contrast, taller plots (such as Sheffield) indicate that there is a high number of schools in the area with a high percentage of eligible students.  

Bradford schools reported a similar mean eligibility across its schools (20.5%) to the statistics for the Yorkshire and the Humber Region (19.5%). The importance of free school meals in the area jumps out when looking at quartiles: a widely-used statistical measure marking out points in the distributions shown on the plots by dashed lines. 

Looking at the distance between the lower and upper dashed lines for each Local Authority, Bradford can be seen to have a large range of eligibility in its schools. Each dashed line (quartile) progressively marks a quarter of the data, when ranked from lowest to highest. Thus, the large distance between these outermost dashed lines shows values range greatly across most schools in the area. 

Bradford schools reported a similar upper quartile (27.4%) to the statistic for the Region (27.4%). However, the middle (19.8%) and lower quartiles (11.7%) for the area are both higher than the Regional averages (16.5%) and (8.5%). These higher measures imply the majority of schools in Bradford have a higher likelihood of children needing free school meals than the majority of schools in the Region.  

 

Regional variations exist. What about neighbourhood variation? 

In this map of Bradford (figure 2), local variation in eligibility and uptake is clear. The more populated southeast of Bradford has several wards where the percentage of children both eligible andof those eligible, taking free school meals on census day is relatively high.   

Figure 2 - Bivariate map showing percentage of children eligible for free school meals and of those, who took one on census day. School data has been aggregated to ward level.

Figure 2 – Bivariate map showing percentage of children eligible for free school meals and of those, who took one on census day. School data has been aggregated to ward level.

The school data has been aggregated into wards and gives some indications as to which areas of Bradford contain schools attended by children for whom food security is a concern.  The results are not unexpected given that 14 out of 30 Bradford wards are amongst the 10% most deprived in England (shown in bold in Figure 4). The two variables have a strong positive correlation, for example where there are high percentages of children eligible, a higher percentage of those children took a free school meal on census day.  It is important to note that the data used here reflects what happened on census day, considered a ‘normal’ school day.   

At a more granular level, the same spatial patterns can be seen across Bradford with graduated symbols.  In Figure 3, larger icons represent schools with a higher percentage of pupils that are known to be eligible for free school meals. The areal units underneath the schools are Lower Super Outputs Areas (LSOA) showing relative deprivation across England by decile. There is a clear relationship between more deprived areas and higher levels of free school meal eligibility. 34% of Bradford’s LSOAs fall within 10% of the most deprived neighbourhoods in England, whilst 16% fall within the least deprived, showing a stark contrast and potential for inequalities experienced by residents across the district. 

Figure 3 - Map showing schools in Bradford classified by percentage of children who were eligible for free school meals and made a claim on census day. Schools appear over the top of the English Indices of Multiple Deprivation 2019.

Figure 3 – Map showing schools in Bradford classified by percentage of children who were eligible for free school meals and made a claim on census day.  Schools appear over the top of the English Indices of Multiple Deprivation 2019. 

Figure 4 – Map showing schools in Bradford classified by percentage of eligible children who took a free school meal on census day.  Schools appear over the top of the English Indices of Multiple Deprivation 2019. 

Higher levels of eligibility are frequently used to allocate additional funding and interventions for children and can often be a proxy for deprivation (as eligibility is tied to income).  However, as with many demographic and socioeconomic measures, the underlying picture is most likely far more complex. Less than half of the children eligible (and who have already been claimed for) took advantage of free school meals, indicted by the maximum percentage of 43% (Figure 6).  The same spatial patterns occur when compared to eligibility in Figure 3, however the interpretation of such patterns is different. For example, schools in more affluent areas have a lower percentage of eligible pupils having a free school meal. There could be many reasons for this other than underlying demographics, such as an urban/rural split or size of school.  

Research has found that stigma plays a large role in impacting whether children who are eligible use the service or not.²  Anonymity is considered one of the best solutions to this problem, with the traditional method of providing vouchers that are handed to catering staff being identified as one reason children are so reluctant to take a free school meal.³ 

We cannot conduct analysis or reach any conclusions on stigma based on our data, however we can see that in less deprived areas, there was a lower percentage of eligible children taking free school meals on a ‘normal’ school day.  Whether this was because stigma is felt more keenly where a child may be one of a much smaller number eligible is unclear, but it is certainly worth considering.  It is important to note that eligibility measures only consider those who have made a claim for the service. 

 

What can we learn looking forwards? 

Worsening food security is another of the unfortunate consequences of the COVID-19 pandemic. The issue of whether to extend free school meals into the holidays has taken centre stage in the national conversation on several occasions. There are areas of high relative deprivation where free school meals are a necessity for a large percentage of an individual school’s population. Although the Government has changed its policy in response to sustained campaigns, using data to understand the local and national landscape with respect to free school meals will help inform more proactive and timely decision making.  

Bradford is just one instance of a highly disadvantaged region in the UK, where food access is one of the many issues for struggling residents. The most deprived areas in Bradford contain schools with higher percentages of pupils both eligible for and taking free school meals. This indicates the need for continued support in these communities, in order to minimise food insecurity. These areas will be significantly more disadvantaged and face serious financial pressures over winter, particularly over this uncertain period. 

If we can promote change in this area, it would then positively impact many outcomes in children’s lives later down the line. Systems must be put in place to combat food insecurity imminently. It is important for local authorities, such as Bradford, to have adequate resources to continue to provide free school meals to pupils when schools are closed due to COVID-19In 2021, the Government’s extension of free school meals over the school holidays and closures needs to be the first reality of a resolution to get our children the necessities they deserve. 

¹ https://ubd.bradford.gov.uk/about-us/poverty-in-bradford-district/
² https://www.iris.co.uk/blog/children-reject-free-school-meals-because-of-stigma/
³ Holford, Angus. (2015). Take-up of Free School Meals: Price Effects and Peer Effects. Economica. 82. 10.1111/ecca.1214

Free School Meals: a National Necessity

Two girls writing at school

Free School Meals: a National Necessity

Alex Dalton and Tom Albone are part of the LIDA Data Scientist Internship Programme and are working closely with CDRC Health and Wellbeing theme lead Dr Michelle Morris. The shared focus of their intern projects is inequalities in the local Bradford area, and one of the pressing local issues affecting this local community (as well as families nationally) is free school meals.

In this guest blog they have collaborated to discuss this issue, and the data available, in more detail. This article details the uptake of free school meals across the nation, looking to this Christmas period. A second article will follow which covers the issue in local area of Bradford.

Why are free school meals needed in the UK?

Access to affordable and nutritious food is a growing problem in the UK. Since 2008 the use of foodbanks has been increasing [1], and the impact of government austerity measures have become apparent with levels of child poverty and food insecurity escalating.

As the economic ramifications of the Coronavirus pandemic emerge from the depths of the initial health scare, businesses are closing across the UK and there has been a record spike in redundancies.  Individuals and families alike are suffering emotionally and financially.

Financial status is a key indicator of food insecurity. Studies have shown how devastating household food insecurity is for health, social well-being, and child development1. Free school meals provide children with vital access to food. Vital in the immediate sense (hunger) and with longer-term consequences (poor health outcomes).

This article explores the uptake of free school meals in the UK, particularly within areas suffering from high levels of deprivation.

The UK has been reported as one of the worst-performing nations in the EU for food insecurity, with 19% of children under 15 living with food insecurity [2].

This is highlighted by the increase in uptake of free school meals: the government incentive to tackle the effects of food insecurity in children and young people. In recent months, the necessity of free school meals has been heavily discussed in the public eye and in government policy.

The UK government’s decision not to extend the free school meal scheme across school holiday periods since lockdown started has proved contentious. The drastic widening and deepening of food insecurities [2] [3] as a result of the Coronavirus pandemic and associated lockdowns resulted in the issue receiving a lot of public attention. Marcus Rashford, a professional football player, rallied support from the public and incentivised businesses’ donations.  The surge of support and speed of response succeeded in raising £20million to feed children in the UK and demonstrated how crucial the free school meal scheme is for children to access food in the UK.

Eligibility for Free School Meals in the UK

The percentage of students known to be eligible for free school meals has been rising year-on-year. Since 2018, the percentage across all schools has increased from 13.6% to reach 17.3% of students eligible in 2020.

To further understand the proportion of students relying on free school meals across all the UK, it is useful to look at the distribution of eligible students for the scheme per school.

The following analysis uses data recorded by the Office for National Statistics for the 2019/20 Academic Year. The data is recorded across all schools in the UK, however for our investigation independent schools have been excluded from the dataset as they do not appear to record free school meal data.

Figure 1. Plot showing the percentage of students eligible for free school meals per school on a census day in the 2019/20 Academic Year. Plots are per Region, indicating the quartiles with bw=0.1 and a uniform scale across the plots. Percentages are calculated per school using data collected by the ONS across all schools in the UK (excluding independent schools). The plot of students eligible on a census day is overlayed with the plot of students recorded actually taking free school meals on the census day (translucent violin plots). The darker edges showing those who did not take their free school meal.

It is important to note that the data refers to children who were eligible to receive, and who claimed, free school meals (FSM) by census day. The overlay of students recorded as taking FSM refers to eligible children who took a free school meal on census day. This second measure only provides an indication of what happened on the day of the census. However, for the purposes of this blog we can use them as an indicator of the proportion of those eligible that needed to use the service.

The distributions show that a significant proportion of children rely on free school meals across the country. More interesting is the variety of the distributions between Regions, illustrated by the shapes of the plots.

Northern Regions have a larger number of students that are eligible per school. Three quarters of schools (top dashed lines on plots) in the Northern Regions (e.g. North East, Yorkshire and Humber) have up to 30% of students eligible on average, with a spike of 36.5% in the North West illustrating that free school meals are more commonplace and required in these areas. In contrast, three quarters of schools in the Southern Regions (South East, South West, East of England) never exceed 20%.

The long thin violin plots, such as that of the North East show that the percentage of students eligible in schools ranges drastically across the region, with some schools having a particularly high proportion of eligible students. In comparison, the short ‘dumpy’ violins indicate the majority of schools have a similar proportion of eligible students, which here tends towards the lower end of the scale, illustrating the stark differences across the UK and inequalities in food security.

What can we learn looking forwards?

Food banks in the Trussell Trust network had been seeing year-on-year increases in levels of need; in a recent report they stated that:

“This (COVID19) crisis has landed after years of stagnant wages and frozen, capped working age benefits – leaving those on the lowest incomes vulnerable to income shocks”.

With children having spent a great deal of time off school during lockdown at home, pressures on family budgets have increased. During this time, efforts were made to provide families with free school meals despite the schools being closed. Even though this was seen as an essential need for many, it was not provided by the government until public opinion went against them.

With the economy taking hit after hit, those in the most deprived households are more likely to suffer the worst impacts. Recent data from a YouGov survey suggests that many households have fallen into food insecurity since the advent of the Covid-19 pandemic. More than three million people (6%) in the UK went hungry in the first 3 weeks of ‘lockdown’, with households reporting that a member had been unable to eat, despite being hungry, because they did not have enough food. Permanent or temporary unemployment appears to underlie lack of resources, with claims for Universal Credit approximately doubling since mid-March 2020 [4].

The demand for free school meals to be provided over the school holidays is driven by the extraordinary circumstances we all now find ourselves in.

These data let us see how issues of food security and free school meals disproportionately impact some areas more than others. As with the rate of COVID-19 infections and the government restrictions, it is useful to look at this on a local level. The city of Bradford is one such example and will be the focus of a further article.

[1]  https://www.trusselltrust.org/wp-content/uploads/sites/2/2017/07/OU_Report_final_01_08_online2.pdf

[2] Barker, M., & Russell, J. (2020). Feeding the food insecure in Britain: learning from the 2020 COVID-19 crisis. Food Security, 12(4), 865-870.

[3] Power, M., Doherty, B., Pybus, K., & Pickett, K. (2020). How COVID-19 has exposed inequalities in the UK food system: The case of UK food and poverty. Emerald Open Research, 2.

[4] https://www.nihr.ac.uk/documents/2048-food-insecurity-health-impacts-and-mitigation/24905

New Data: The e-food deserts index

New Data: The e-food deserts index

The e-food deserts index (EFDI) is a multi-dimensional composite index for GB which measures the extent to which neighbourhoods exhibit characteristics associated with food deserts across four key drivers of groceries accessibility:

· Proximity and density of grocery retail facilities

· Transport and accessibility

· Neighbourhood socio-economic and demographic characteristics

· E-commerce availability and propensity

It draws on a long interest in food security among Geographers and policy makers, most prominent in the late 1990s and early 2000s ‘urban food deserts’ debate. At the time it was argued that urban food deserts had been ‘abandoned’ by the major grocers, resulting in poor access to the larger format grocery stores which provided fresh, healthy and affordable food. Many of these neighbourhoods were some of the most deprived in England and Wales and were located within inner city areas where residents faced considerable financial and practical (e.g. access to transport) barriers to accessing food store provision.

The EFDI incorporates new indicators of online groceries (home delivery) provision and propensity to engage with online groceries, the latter drawn from an existing CDRC data resource, the 2018 Internet User Classification. In addition to urban deprivation, it highlights a new driver of inequalities in access to groceries, termed ‘e-food deserts’ – remote and rural neighbourhoods which suffer the dual disadvantage of comparatively poor access to physical retail opportunities alongside limited provision of online groceries.

The EFDI is constructed at a neighbourhood level using Lower Super Output Areas (LSOAs) in England and Wales and Data Zones (DZs) in Scotland. Input data are drawn from a range of sources including the Census and existing indicators of deprivation and accessibility at the neighbourhood level. It also incorporates a number of custom-derived indicators of food store accessibility, consumer behaviours and availability of groceries e-commerce drawn from our own modelling.

The index can be viewed on CDRC Maps (England and WalesScotland) and is available to download for researchers and policy makers to attach to their own data. The research team would be very keen to understand how the index has been used in subsequent applications.

Screenshot of Newcastle showing the EFDI at Lower Super Output Areas
Newcastle upon Tyne – users can search the EFDI by location on CDRC Maps

The research team have used the index as input to wider ongoing work into geographical inequalities in e-groceries provision as part of the first GB wide assessment of the geography of online groceries provision. Whilst they found that online groceries coverage is generally excellent at the household level, they note considerable inequalities in online groceries provision between urban and rural areas. Whilst online groceries could afford considerable potential to improve retail access in rural areas, inequalities in provision are currently driving new notions of contemporary food deserts.

Andy Newing, Associate Professor in Applied Spatial Analysis at the University of Leeds, who led this study, said:

“These inequalities in online groceries delivery availability are driven by the challenges of providing services within out most remote and rural areas, alongside the predominantly urban and suburban nature of investment in e-groceries by retailers.”

Recounting Crime – helping to improve the accuracy of crime estimates

The back of two police officers wearing high vis police vests

Recounting Crime – helping to improve the accuracy of crime estimates

Recounting crime is a research project funded by the ESRC Secondary Data Analysis Initiative. The project team are using data from the CDRC to explore new statistical methods to help improve the accuracy and precision of crime estimates.

About the Project

Getting an accurate picture of the true extent of crime is a central task of police forces, the Home Office, and Office for National Statistics. Annual counts of crime are used to determine the costs of crime, which is in turn used to allocate resources to the police and public services, performance manage the police, and evaluate crime reduction initiatives.

The regular publication of crime figures is also a key determinant of public confidence which in turn facilitates greater reporting of crime, whilst also being used by governments to justify – and retract – major policy initiatives. Recent reports about the rise of knife crime, and the large uptick in hate crime post-Brexit both relied on close reference to crime statistics, and the proposed extension of stop and search powers across police forces in England and Wales has also been justified with reference to these apparent increases in crime.

The problems with recorded crime figures are well known, including inconsistencies in recording practices in different forces, incentives to ‘no-crime’ incidents that are unlikely to be solved, and differences in the willingness of the public to report certain crimes (which is itself dependent on the relationship between the police and the public). So severe are these problems that police recorded crime figures recently lost their official statistics designation (following a substantial review in 2014). 

In this project the team will make use of recent developments in the study of measurement error and small area estimation to better understand the nature of the gaps in coverage of the two sources of crime data, explore the implications of relying on crime estimates prone to measurement error, develop adjustment methods and estimate new ‘corrected’ crime counts at the local area level. 

Find out more

Visit the Recounting Crime website.

Follow Recounting Crime on Twitter.

Watch the Recounting Crime project team discuss the impact of measurement error in police crime records at the UK Data Service – Crime Surveys User Conference 2020 on 8 December.

DUG Conference: Data Analysis in a Crisis, plus CDRC Masters Dissertation Scheme

DUG Logo

DUG Conference: Data Analysis in a Crisis, plus CDRC Masters Dissertation Scheme

On Tuesday 10th November, the retail industry DUG (Data Analysts User Group) hosted its annual conference on the theme of Data Analysis in a Crisis. Consonant with this theme, the usual industry-led event could not take place at the usual Royal Society venue this year, but nonetheless attracted an audience of 70 participants online using WebEx.

Full details of the programme and activities are available on the DUG website, including videos of the presentations. DUG Director Tim Drye opened the proceedings with an overview of the science and art underpinning the Data Analyst role, illustrating that foundations from each are essential to understanding data and presenting them to an audience in an intelligible manner.

Mark Stern, Eoin Gleeson and Fraser Gray from Ladbrokes Coral then addressed the organisational setting to high performance data analytics through effective team-building, drawing upon their many varied experiences.

Prof. Paul Longley then introduced the CDRC Masters Dissertation Scheme, noting upcoming launch of the 2021 scheme and the opportunities that it offers for career-enhancing interactions between business, academia and student-centred problem-solving. (The website has more information and can be used to make enquiries or submit projects.) Four selected students who took part in the 2020 Masters Dissertation Scheme then presented their collaborative work:

– Lucy (Ludmila) Sabelnikova, City University worked with Movement Strategies, in an evaluation of the ways in which footfall and mobile network data can be used to predict consumer behaviour at events – view Lucy’s presentation and project overview

– Samuel Li, UCL also worked with Movement Strategies, on an assessment of the Impact of weather upon shipping movements, as evidenced using AIS data and weather APIs – view Samuel’s presentation and project overview

– Nombuyiselo Murage, University of Liverpool worked with Tamoco UK Ltd., to derive spatio-temporal geographies of activity patterns from mobile GPS data – view Nombuyiselo’s presentation and project overview

– Taeyang Jung, Imperial College worked with the Phoenix Partnership to identify and evaluate barriers to use of electronic health records in applied settings – view Taeyang’s presentation and project overview

All of this year’s Masters Dissertations were submitted to the annual national CDRC competition, judged this year by Sarah Hitchcock (Geolytix) and Martin Squires (Pets at Home and UCL Visiting Industrial Professor). This year’s winner of the £500 cash prize was awarded to Lucy (Ludmila) Sabelnikova, and the two runner-up prizes were awarded to Nombuyiselo Murage and Samuel Li. Nombuyiselo also won the Presentation Prize for her contribution to the DUG conference, with honourable mentions also going to Samuel and Ludmila.

Congratulations to all the prize winners, and thank you to Lucy, Sam, Nombuyiselo and Taeyang for the excellent presentations., that will be made available as part of the conference proceedings.

The presentations were followed by a presentation from Dr Andrew Larner that took stock of how local councils are adapting to the Coronavirus, bringing together a range of experiences from across the globe. The contribution of the National Statistician, Professor Sir Ian Diamond was unfortunately cancelled because of technical issues.

Gary Cole highlighted the benefits of DUG membership and outlined how DUG is now moving forward, and Tim Drye wrapped things up sharing his reflections from the meeting.

It was a great opportunity to hear from industry, and see how the CDRC Masters Dissertation Students completed their projects over the summer. If you are interested in submitting projects for next years Scheme, please have a look at our website. If you have any questions, please email projects@cdrc.ac.uk .

Written by Nick Bearman, Project Delivery Manager.

CDRC Data Scientist Interns

CDRC welcomes new Data Science Interns

Last month we welcomed five members of the LIDA Data Scientist Internship Programme to the Centre. Over the next six months George Breckenridge, Stuart Ross, Seb Heslin-Rees, Rosie Martin and Simon Leech will be working with CDRC researchers on the following projects:

Analysing COVID-19 Mobility Responses through Passively Collected App Data – George Breckenridge and Stuart Ross

‘Lockdown’ policies restricting mobility have caused mass disruption to the normal operation of daily activity in cities across the COVID-19 pandemic. They mark the first time in recent memory that national and global populations have been caused to simultaneously re-evaluate transport choices, whilst also causing wholesale changes in the location and spatial footprint of most social and economic activity. The understanding of such dislocated geographies will underpin urban and transport planning policies for maintaining low virus transmission risk and for revitalising the UK economy, far into 2021 and beyond.

The emergence of passively collected anonymous mobility datasets, produced though mobile phone apps in cases where user permission is granted, makes it possible to explore these transportation responses at fine spatial and temporal scales – before, during and after the COVID-19 lockdown(s). The aggregation of these contemporary UK patterns – which will be required to maintain user anonymity – allows for the exploration of hundreds of thousands of users whilst simultaneously protecting privacy. Indeed, the utility of privacy-enhanced outputs for policy will be a lead project focus. Phone data will be provided by partner Cuebiq through their secure online platform, which enables only the export of aggregated outputs to suitable spatial units.

In order to investigate such unprecedented changes in mobility using Cuebiq’s data, we expect to employ a variety of machine learning (ML) methods to extract features. A journal paper documenting these patterns as the COVID-19 crisis evolves is the anticipated output for this CDRC-funded project

New insights into workplace and retail dynamics for English and Welsh cities – Seb Heslin-Rees

This project will be using Whythawk data on commercial properties in England and Wales, at Lower Super Output Area. It will make use of existing methodologies applied to a different scenario to produce new insight on commercial property rent and spatial location.

A commercial geodemographic classification of workplace zones

This research endeavour will utilise the newly available Whythawk dataset to construct a model for presenting and thus, understanding the spatial distributions of workers and workplaces across England and Wales. Largely, this will involve clustering workplaces of similar characteristics to distil a set of key workplace types, which can subsequently be mapped and analysed. In addition, the dataset has made available details of workplaces that have not been present in previous workplace datasets, such as distinguishing different workplace functions within multi-level building complexes. Consequently, this could provide additional insights and novel avenues for academic research and policy initiatives.

Predicting commercial rents using novel Machine Learning approaches

Using novel big data, this study will assess mass market appraisal within the English and Welsh commercial rental market. Mass market appraisal is the valuation of properties at a given time, and is required to ensure each property makes the appropriate tax contribution. This study will use a large volume of data on commercial business type, rental and rateable values and numerous external environmental variables. A range of machine learning algorithms will be used to predict and appraise the commercial rental market in England and Wales.

The outputs will include academic papers focused on methodologies employed, CDRC datasets and detailed maps. These projects are expected to help property professionals better understand commercial rental pricing and businesses who use and occupy these spaces and also researchers who are interested in how property values interact with other aspects of the environment.

Isolation and Inclusion in a post-social distancing COVID world – Rosie Martin 

The disparate impacts of COVID-19 and the associated lockdown have been much discussed recently, particularly in terms of age, deprivation, or employment sector. As the UK and other countries emerge from quarantine, it is equally apparent that the after-effects are likely to be long-lasting, whether through continued mitigation efforts such as social distancing or the economic impacts of economic shutdown, and that these after-effects are likely to further unevenly impact some groups over others. There are many dashboards reporting information on COVID cases and deaths, but information on the impacts on general population and businesses is missing.

Our main objective is to advance understanding of the social and spatial impacts of emergence from lockdown, identifying those households and places at risk of further isolation, under a scenario of continued social distancing, high unemployment, and a potential contraction of local service provision, including public transport. We have three research questions:

1. Are some typical household structures more vulnerable than others as a result of social distancing (and what are they vulnerable to, e.g., unemployment, social isolation, decreased service provision versus decreased access to existing services, decreased mobility, etc.)?

2. Is there a critical intersection of mobility, employment status and social distancing rules that predispose households with particular structures to isolation?

3. Based on current neighbourhood patterns and planning, what is the geography of isolation vulnerability?

It is expected that each of these scenarios have a particular geography. The creation of this dashboard should help predict where geographies of isolation under intersecting scenarios occur. Identifying areas at risk of isolation and exclusion through this project could prove invaluable to local councils who will be working to ensure all individuals are given access to relevant levels of assistance and resources during COVID-19 recovery, rather than allowing pre-existing disparities to widen.

CDRC Masters Dissertation Scheme at Registry Trust: skills, experience and employability

CDRC Masters Dissertation Scheme at Registry Trust: skills, experience and employability

Millie Corless completed her masters dissertation through the CDRC Masters Dissertation Scheme (MDS) with the Registry Trust. I spoke with her after she completed her degree to find out what it was like. The scheme really appealed to her, and it was one of the parts of the MSc Geospatial Data Science (GDS) degree at University of Liverpool that convinced her to apply for that Masters programme. Our industry collaborations allow us to provide projects that have real world impact, giving students the experience of working with a real world dataset, and feeding into the industry partners work.

The Registry Trust is a small company (~ 30 staff) and had one person in their data analytics team. Millie gave them the extra capacity to ask a MSc GDS student with skills and experience in GDS and coding to take one of their data sets which they hadn’t had much work done on it, and spend a significant amount of time analysing the data. The Registry Trust were fairly sure the data set had a good story within it, about the scale and patters of county court judgements for indebtedness but they didn’t have the time or expertise to dig into this and find out the details. Offering the project through the CDRC MDS allowed them to get someone with the skills and time to do this.

Millie Corless completed her masters dissertation through the CDRC Masters Dissertation Scheme (MDS) with the Registry Trust.

Throughout the scheme, the projects vary but they always have some degree of flexibility for the student to focus the project on their areas of interest. For example, Millie is very interested in health and she looked at the CCJ data and explored its’ relationship with health. She met with a number of different people to develop and refine the project proposal, including her industry supervisor (the current data analyst) and others from Registry Trust, including the CEO and Chair of the company. One of the benefits of working with a small company (30 staff) is that she was able to work closely with a range of staff members and they gave her some great insights into working in industry. She felt like she was working within a bigger team for her dissertation; there was a group of people she could go to for advice, including her academic supervisor, her industry supervisor, and others within and outside the Registry Trust.

This project also had a great real world impact; the analysis Millie completed has fed into a blog post by the Registry Trust, and future projects and their policy recommendations. The real world impact was one of the elements that Millie really liked about the Masters Dissertation Scheme, and the Registry Trust project in particular. The fact this project already existed was a great help for Millie – “it allowed me to concentrate on the project rather than needing to come up with a topic. I also knew I was interested in health, so the flexibility within the project allowed me to include that in my research questions and analysis which was great.”

After the scheme, the position of Data Analyst within in the Registry Trust became available, Millie applied and is now working full time for the Registry Trust. “I applied and was interviewed with a number of other candidates, but having taken part in the Masters Dissertation Scheme, I already knew the data sets they were working with and the type of analyses they were interested in which gave me an advantage.” Her role as Data Analyst is developing the in-house skill set that the Registry Trust can utilise and will feed into a number of projects and outputs over the coming year.

Her recommendation to anyone considering applying for the Masters Dissertation Scheme is to “go for it”. The scheme gave her great experience and looks good on her CV, and going forward into any career (in industry or academia or elsewhere) the Masters Dissertation Scheme shows you are interested in the application of the skills you have learnt and gives you experience of working with others in industry.

The Scheme will be open soon (November) for businesses proposing projects, and then available in the new year for masters students to apply. Please have a look at https://www.cdrc.ac.uk/education-and-training/masters-dissertation-scheme/ for more details.

More details on Millie’s research are available in the Registry Trust blog post and in her dissertation.

Written by Dr Nick Bearman, Data Services Manager.

CDRC Open Data Survey & Prize Draw

CDRC Open Data Survey & Prize Draw

The CDRC is currently conducting a review of past and ongoing applications of our data sets.

Users of our open data services are invited to participate in a short survey. Completing the survey will automatically enter you into a prize draw, with a chance of winning one of four Amazon gift vouchers:

1 x £200

1 x £100

2 x £50

We will contact the winning participants with details of how to claim their prize shortly after the survey closes on November 13th 2020.

We are gathering information to track the applications of our data services and to better develop our services with our users’ needs in mind. As an open and accessible data service provider, user feedback is crucial to improve the service CDRC provides and to maintain CDRC as a user-centric platform.

All of those users who have registered to access our open datasets should have received an email for the survey. If you have not, and would like to contribute, the survey is available online at https://liverpool.onlinesurveys.ac.uk/cdrc-open-data-survey .

Please contact james.brookes@liverpool.ac.uk or info@cdrc.ac.uk if you have questions about completing the survey.

Beginner’s Python Review

Beginner’s Python for Data Analysis – Review

Adam Keeley, Analyst at Leeds Institute for Data Analytics, shares his thoughts on our recent online Beginner’s Python for Data Analysis course.

The CDRC Beginner’s Python for Data Analysis training course was outstanding. I’m actually not a complete novice when it comes to Python, and I suspect a few others in the class also had varying levels of experience already. The course was structured such that it covered off the basics of object oriented language without dwelling on them. This helped fill in the gaps in my baseline understanding and ensured we were all quickly up to a similar standard.

The delivery of the content was well paced. When new concepts were introduced we were talked through some worked examples before being asked to work through a set of exercises on our own. Except we were never on our own; Fran and her demonstrators were always on hand to offer help and guidance in a friendly and constructive way. The course structure was logical and methodical with each new concept built on principles previously established and embedded through active application. There were no great conceptual leaps so at no point did I feel lost or wondering ‘how on earth did we get to that?’.

Inevitably when remotely delivering practical training of this nature, there were some technical issues. Impressive efforts were made to proactively sidestep these through the use of online, containerised environments. Every one of us began working from a standardised online environment, set up in advance with all of the required software and package dependencies available. Unfortunately the internet connections of the class were of varying reliability and some of us failed to connect consistently. The order of the course schedule was changed to get us set up on our own machines early but again, the remote delivery meant that every machine was different and the subsequent technical issues threatened to dominate the experience. I was extremely impressed with how well the delivery team handled the situation, providing technical support to all of us both as a group and individually when required. We were soon working with data again and probably with a greater understanding of Python as a result.

One benefit of the online delivery was the open chat enabled us to help each other out as we found solutions, not just troubleshooting the Python environment but in the exercises too. I found my fellow students friendly and keen to offer help when they were able. Coupled with the delivery teams knowledgeable and amiable nature this helped foster an environment where asking questions was easy.

By the end of the two day course many of us were thinking of ways to implement what we’d learnt in our workplace or include it in our research. Many of us were asking questions about the techniques and concepts we’d learnt, in terms of specific application to data and problems we face in the working world. These questions were answered helpfully and with a breadth of knowledge, with tips on where to learn more.

I would definitely recommend this course to anyone curious if Python might be useful to them, or for whom programming does not feel accessible. This course will let you in on the big secret: programming doesn’t have to be difficult!

A classification for English Primary Schools using open data

Two girls writing at school

A classification for English Primary Schools using open data

 

CDRC researchers, Dr Stephen Clark, Dr Nik Lomax and Professor Mark Birkin have developed a new classification for English primary schools to encourage better collaboration and enable more nuanced benchmarking.  

England has statutory regulations in place that ensure state funded schools deliver broadly the same curriculum. However there still exists a wide range of contexts in which this education takes place, including: the management of schools; how the schools chose to spend their budgets; individual policies in regards to staffing, behaviour and attendance, and perhaps most importantly, the composition of the pupil population in the school. Given these contexts, one outcome of interest is the attainment profile of schools, and it is important that this performance is judged in context, for the benefit of pupils, parents and schools.  

Developing a new classification  

To help provide this context CDRC researchers, Dr Stephen Clark, Dr Nik Lomax and Professor Mark Birkin have developed a new classification using contemporary data for English primary schools.  Thanks to the European Regional Science Association, they have made available a resource that identifies families of primary schools in England, where schools share common characteristics.  

The recently published study allocates schools into one of 32 sub-groups, allowing schools to compare their performance, either academically or financially with similar schools. These groupings allow the identification of “families of schools”, to act as a resource to foster better collaboration between schools and enable more nuanced benchmarking. 

Users are able to search by location to view schools in their area.

A novel approach to classifying schools  

Dr Stephen Clark explains “We identified the two most important aspects as firstly, the ethnic composition of the school, either from the background of the pupils or the number of pupils where English is not a first language.  

The second being the level of affluence, measured by neighbourhood characteristics or the number of pupils eligible for free school meals. Other important aspects include the degree of oversubscription for popular schools, and the number of authorised or unauthorised absences. 

In this study the academic performance of the school is not ’baked into’ the classification, so that differences in schools performance can exist, and be identified and investigated or shared.”  

Example 

Looking at the Midland’s City of Derby, we see that for a school like Reigate Park Primary they would be better to look to schools further afield, such as St Mary’s Catholic or Ashgate schools for appropriate peers to share experiences with, rather than the closer by Brackensdale school. 

Map of Derby highlighting that Reigate Park Primary would be better to look to schools further afield than their immediate neighbour Brackensdale Primary
Map of Derby – Background Map © OpenStreetMap contributors

 

Using open data  

The data for the study comes from the Department for Education in England, collected each year during the Spring term’s Annual Census of Pupils and Schools.   

This regularity of data allows the groupings to be revised over time, which is useful as the circumstances of a school are not fixed over time.  We would expect to see some changes to the groupings as schools are transformed through new leadership or by changes to catchment areas and their population.   

View the data

Find out more