UCAS End of Cycle data – the Whitty effect and getting lost in the (Boston) Matrix

In February the good people at UCAS released their end of year report along with some lovely dashboards allowing people to look into the major trends around applications and accepts for the UK HE system. They also released their massive range of data sets which gave the VizHE folks something to do in lock down.

With their shiny dashboards UCAS had pretty much covered the basic summarisation of the data so we wanted to see what else we could extract that piqued our interest.

With the current world situation we talked through looking at subject trends from a position of what are the popular subjects and to what degree had they been influenced by the brilliant scientists and doctors who have been a large part of the collective effort in the past year or so.

We each followed separate lines of enquiries and came up with three fantastic vizzies – one using the Boston Matrix approach to review subject trends, one digging into the Whitty effect and one looking at the choices both within subject and ethnic group around clearing and adjustment.

The Boston matrix is a tool used to look at the portfolio of products to see how they are performing. It effectively places the products into four quadrants:

Star (High market growth, high market share)

Question Mark (High market growth, low market share)

Cash Cows (Low market growth, high market share)

Dogs (Low market growth, low market share)

The quadrants are created by plotting the change in market share against the market share for the current year based on the applications for each subject. It is possible to look at the data by different date ranges and by individual institution performance.

The visualisation is available here: https://public.tableau.com/views/UCASApplications-BostonMatrix/InstitutionBostonMatrix?:language=en&:display_count=y&:origin=viz_share_link

Jennie – The Chris Whitty Effect – Analysing the increase in UCAS applications for health and social care courses

I decided to focus on the increase in applications for health and social care courses coinciding with the COVID-19 pandemic, termed ‘The Chris Whitty Effect’ by WONKHE in their recent blog available here (Tracing the Chris Whitty effect in the 2020 end of cycle admissions subject data | Wonkhe).

Delving into the data more closely I could see straight away the increase in JACS subject area ‘Subjects allied to Medicine’ which has a big jump from 2019 to 2020 and it stands out amongst other JACS subject groups. The trend over time is also interesting for this subject; applications dropped in 2017 and 2018 coinciding with the change in removal of bursaries from 1st August 2017 for students taking pre-registration nursing, midwifery and allied health courses. This year though, main scheme applications (the main UCAS application scheme whereby students can apply for up to five institutions or course) are up by almost 9%.  Medicine and Dentistry has also seen a 5% increase in applications, but by far it is Subjects allied to Medicine that has seen the biggest increase.  When looking at total accepted places, again it is these two subject areas that are ranked on top, with a 17% increase in accepted places for Subjects allied to Medicine and a 9% increase for Medicine and Dentistry.  Conversely Non-European Languages and Literature has seen the biggest drop, with a 15% decrease in main scheme applications and an 11% decrease in total accepts, so it would be interesting to delve further into this data in future analysis.

Looking specifically at Subjects allied to Medicine I wanted to focus on individual providers to see if an increase in applications is also reflected in an increase in accepts. As I expected, the majority of providers that saw an increase in applications from 2019 to 2020 also saw an increase in total accepts. Of interest though are those providers for which applications increased but accepts either stayed static or even decreased. I wondered if this was due to caps on the numbers of places available at these providers so I looked at applications and accepts across all JACS subject areas for these particular providers.  Although I couldn’t see any obvious pattern here, some of these providers did see an increase in applications and accepts for certain subjects (see University of Bath).  This could mean that caps on places for Subjects allied to Medicine might be the issue, however more in-depth knowledge of courses at the provider would be needed to know for sure.

This time I experimented with using a long form dashboard which I haven’t used before.  I usually like to see everything all in one go, however I do think it works and helps tell the story.  I also played with the golden yellow colour across the dashboard, I don’t think it looks too bad, but let me know what you think 😊. If I had more time I would have liked to repeat the bottom chart for all providers in scatter plot to see if this pulls through any patterns when comparing across different subject areas.

The visualisation is available here: The Chris Whitty Effect – Analysing the increase in UCAS applications for health and social care courses – Jennie Holland | Tableau Public

Sasha – Widening Participation – Exploring changes in relationships of ethnic groups in UCAS application stages

I set out to investigate the difference between applications and acceptances recorded for each acceptance route against measures of widening participation in HE. The first challenge was to combine the files: there was one file containing applications, another acceptances and a set for each of the WP metrics. Starting with the ethnic group data I noticed that there was a huge difference in numbers; far more acceptances than applications. The crux being the applications file only contained main scheme applications whereas acceptance also included others (see below). Not a big issue as you can use this field to join the data. However what you end up with is only three acceptance routes: Firm, Insurance and Other, and not many insights to draw.

Back to the drawing board, I looked at the initial question that interested me. I wanted to look at the differences in groups of people going to university at the opposite end of the scale acceptance scale: adjustment and clearing. I dropped the combined file altogether and focused solely on acceptances. The below DNA chart shows the few groups going for adjustment and their subject choice by year. It also compares the popularity of these subjects when it comes to clearing.

The visualisation is available here: TBC


Student Numbers

The Office for Students started to provide updates of the general student numbers at providers under their regulatory control. This covers both traditional higher education providers as well as further education colleges and alternative providers.

The data is available here: https://www.officeforstudents.org.uk/data-and-analysis/student-number-data/get-the-current-student-numbers-data/

The visualising HE team decided to give it a crack as it is a simple data set and it provided a genuine whole sector view of data.

This as it turned out was not as straightforward as we had hoped. The simplicity of the data set made it challenging to find the interesting story within the data.

This gave us an opportunity to try out different approaches and pushing both technical and design skills to try and come up with something that is interesting.

Each member of the team outlines below what they have produced and what they have gained from the process.

Dave – Size and shape over the last 2 years

Access interactive dashboard

I struggled to find an interesting narrative at first then as I looked at the changes between the years I noticed that although the numbers for further education colleges were smaller than the changes in higher education the proportions of the total was much more dramatic.

Once I found this I decided that I would create a visual that stepped through the data to draw out this interesting element. The long form story telling nature of the visualisation focusing on bar charts hopefully works well.

Jennie – Simple student numbers reporting

My visualisation simply looks at reporting student numbers at different levels, allowing the user to select a higher education provider and compare how the proportion of students at different levels compares with other institutions. Student numbers on the right-hand side of the chart are provided for context and both 18/19 and 17/18 figures can be shown.


This analysis focuses on higher education institutions.  Unsurprisingly at the majority of these institutions 100% of students are taking level 6 or above qualifications, however I feel as though it is more interesting to look at the institutions which offer levels 4 and 5 qualifications.  For example, on average only 1.5% of students across all university institutions are studying for level 5 qualifications, however almost 30% of University College Birmingham students are at level 5.

Whilst the dashboard isn’t too complicated, it nicely uses sets, set actions and animations to reorder the main chart based on the proportion of students at the level selected.  The stacked bars also sort dynamically, with the selected level always at the end of the bar to allow easy comparison between universities.

James – student population comparison

When I began exploring the data in Tableau I was struck by the size of some of the institutions. That gave me the idea to try to find out how many large institutions it takes to match the populations of the remaining institutions. I decided that two stacked bars next to each other could show this.

The data cover both Further and Higher Education institutions and different education levels. So I built the workbook to allow you to make comparisons within and between them. The most striking finding is that when looking at all levels combined for 18/19, it takes only the top 3 Higher Education institutions to match the population size of all the Further Education institutions.


Building the workbook required me to solve some technical challenges. To make the stacked bars gain or lose institutions I made some plus or minus buttons. Clicking these triggers a parameter action increasing or decreasing the number in the parameter by one. The number of institutions in the right stacked bar is based on the rank of the sum of the students at that institution. If the rank is less than or equal to the value in the parameter then that institution appears on the right hand side (and disappears from the left hand side where appropriate).

To make the buttons I put institution on columns in a sheet in Tableau and put index() on detail in the marks card. I then filtered to only leave the institutions with an index() one above and one below the current value in the parameter. When you click a button the parameter is updated and the button sheet is filtered to the institutions above and below that rank.


Blog number 2 for the 2020 VizHE team which now includes the skills of Rhodri Rowlands and Jennie Holland both visualisation masters and data wrestlers extraordinaire.

The standard data returns that UK HE institutions have to complete is focused on the estate that University inhabits.  This data set is not always the most exciting (although VizHE have covered it before here and here) but with the current position the sector, country and world are in the estate and it’s make up suddenly becomes an important topic.

The initial discussion for the VizHE team was to look at to what extent social distancing would be possible on campuses.  In the data set from HESA we have students, staff as well as internal and external space for each institution so a couple of calcs and we would have the answer….

As we talked it through though we realised we were getting caught up in the jump to an overly simplistic view of a complex problem.  There are many factors that the metric does not take into account such as how the spaces are configured, what groups the students are in and also to what extent the data focuses on students and staff who would be on campus!

So changes in direction needed – luckily this data set had plenty of options.Within the data set there is a wealth of information around the environmental credentials of the HEIs.  From waste to energy to emissions to travel.It was in travel that Dave decided to explore and to see how the UK HE staff get to work.  In this Viz Dave has defined four categories:

  • Great – where staff cycle or walk to work

  • Not too bad – where staff get the bus, train or car share

  • Bad – where staff travel via motor bike or single occupancy car journey

HEIs were then ranked by how great they were.  There were some unsurprising results with many Oxford and Cambridge near the top and a lot of London and big city HEIs doing well.  Within this though there was a contrast between how many were really great against how much they were just not too bad.Interactive viz – click hereHow do staff get to work_

Jennie’s visualisation is titled ‘Uni-cycling – which institution is #1 for cycle spaces?’, and looks at the total number of cycle spaces at each institution against the number of staff and students to build a cycle space ranking.  The visualisation is split into two halves.  The left-hand gears control the ranking and can be used to flick through the ranking list.  The right-hand gears are used to highlight institutions and look for patterns based on groupings such as Million Plus, Russell Group etc. (data sourced from learning-provider.data.ac.uk), as well as their geographical location.

Unsurprisingly Oxbridge do well, with approximately 5 people per cycle space on average (2018/19) compared to 29 people on average across all other institutions.

To expand the analysis further it would be interesting to source some additional datasets and see if there are differences in the number of cycle spaces based on institution type (city, campus etc.), as well as how well the surrounding area is equipped for cycling based on the number of cycle routes. 

View the viz here!

Jennie Viz

Hopefully that has shown that there are some interesting insights in the Estates data!

Let us know what you think

VizHE crew

VizHE 2020 – starting with Finances

Hey up – it has been a while since the Visualising HE crew have got together.  Since the last post Elena has moved to Sweden so we have roped in Sasha from the Information Lab to bring much needed energy and skills to the project.

As with the post last year we have picked up the Finance data to see what interesting insights we could identify.  The talented bunch at WonkHE have been pulling the Finance HESA data apart in recent weeks in particular looking at the potential impact the current situation may have on the sector and indeed individual institutions – if you have not read their stuff then you really should.

We each took different routes through the data which brought the opportunity to try out new chart types.

Adam and Sasha both looked at the income streams for Universities to see how diverse they are.  Adam wanted to be able to the see the whole sector whilst also being able to see the individual Universities within the sector.  His Viz also allows the user to change the sort order if there is one income stream you are more interested in.

Adam’s Viz

HE Income by category

Link to interactive viz

Sasha wanted to focus on the individual University so you can see how diverse the income is and which of the income streams dominates by University.  The visualisation also has the ability to see the information for previous years as well.

Sasha’s Viz

How diverse is the income portfolio of HE providers_

Link to interactive viz

Dave decided to focus on the picture of the sector as a whole and using basic quadrant analysis spit out the Universities into what degree their income is made up of tuition fee income and out of that tuition fee income how much is from non UK or EU students.  The visualisation paints quite a stark picture given the current position.

Dave’s Viz

VizHE April 2020 HEI Tuition fees

Link to interactive viz

Well that is the first post for Visualising HE for 2020 – let us know what you think

Frolicking with Finance

Hiatus over…..Team #VisualisingHE breaking the drought with some fun with HE Provider Financial data.

Back in March 2019 HESA ‘sprung’ a never-before-public release of HE provider institutional finances. Prior to 2019 this level of institutional data has been available to key contacts within institutions through HEIDIPlus platform within institutions – and paying customers. However as part of HESA’s open data programme we can access and visualise more than ever before.

Finance data release

HESA’s new release of Finance data, is the first time HESA has released its full Finance record as open data, freely available for re-use under the Creative Commons 4.0 licence. As well as income and expenditure analysis the release includes HE providers balance sheets, cash flow and capital expenditure.

What do HESA collect from Providers?

HESA collect financial data (submitted in the winter of each academic year in templates provided by HESA) from universities , colleges, and other HE providers in the UK and covers income, expenditure, balance sheets, statement of gains and losses, capital expenditure, senior staff pay and Key Financial Indicators. details on what is collected under each of these broad terms are on HESA’s definitions pages.

Where to start?

Team #VisualisingHE have pondered this data release for some months, and whilst there is a whole lot of juicy figures to dive into, by way of introduction to the HE Finance data, we thought that we should focus some of this post on ‘introducing’ the finance data and the first publication of the Key Financial Indicators (presented as a three year time series).

Key Financial Indicators or KFI’s for short, are compiled using information provided to HESA as part of the HESA Finance record. The KFI’s defined and shown below are a set of ratios extracted from the finance record. They are not performance indicators and take no account of provider-level characteristics such as the range of subjects taught or the types of provision provided.

What are the 9 KFI measures?

  • Days ratio of Total net assets to total expenditure
  • External borrowing as a percent of total income
  • Net cash inflow from operating activities as a percent of total income
  • Net liquidity days
  • Premises cost as a percent of total costs
  • Ratio of current assets to current liabilities
  • Staff costs as a percent of total income
  • Surplus/Deficit as a percent of total income
  • Unrestricted reserves as a percent of total income

For definitions of the KFI’s detailing the numerators/denominators see here

What have team #VisualisingHE done?

Team #VisualisingHE have focused on trying to present these data as simply and cleanly as possible. A #TableauKISS you might say.

Adam came come up with a KFI scorecard, heavily critiqued by both Elena and Dave to settle on the dashboard below, where a single provider can be chosen, the benchmark be selected (sector, country or region) and the dashboard simply presents a provider overview of indicators:

  • Current (2017/8) figure
  • Year on year percent change
  • Provider vs Benchmark comparison trend
  • Sector distribution (and percentile on tooltip)

KFI OverviewV2_final.png

Interactive Viz: Key financial Indicator (KFI) Scorecard

Dave decided to have a ‘rustle’ around the ‘Russell group’ to see what he could find…..

Whilst playing around with the income and expenditure data we were struck by how much Russell Group institutions dominated with nearly half the income going to the small number of Russell Group institutions compared to the large number of non Russell Group institutions. These is also increasing over the time period.

HESA Finance Russell Group and the rest

Interactive Viz: “The Russell group and the rest – Income in UK Higher Education”

Additional resources

My go to: WONKHE gives HE data a timely on the button exploration of data releases. Here David Kernohan put together an excellent first exploration of this data within moments of this data being released for the first time and is always my go to for initial headlines and points of analysis. HESA institutional finance 2019 release: KFI is going to rock you published in March 2019.

Thanks for reading, and as always we would love to hear your thoughts and critique.

We love to viz, write and discuss all things HE, but most of all we love to learn, so let us know what you think.

Adam, Dave and Elena.

21 Posts to date | Which have been Adams favourites?

Team #VisualisingHE have had a lot of fun and learned a lot over the last 21 months and I thought I would kick off my reflective post covering some of my favourite bits.

We kicked off the project in March 2017, to-date we have created some 21 posts, we have challenged ourselves to numerous new charts including; Sankey’s (Apr 18), radial charts (Nov 17), and even a chord diagram (Apr 18), to name just a few. Explored many open HE datasets, I have learned a ton about remote communication and working together, peer editing and critique, not to mention furthering my Tableau skills and bettering my fairly poor writing skills.

So what have been my favourite bits?

  1. Overall favourite blog post
  2. Favourite Viz – Elena
  3. Favourite Viz – Dave
  4. Favourite Viz – Adam
  5. Favourite solo blog post

Overall favourite blog post

Having written this paragraph a few times whilst in draft, it became obvious to me I have a few blog posts that I have really enjoyed (for various different reasons).

However I have settled on one standout post for me, of which I wasn’t personally involved, but admire a lot, it is the NSS 2017 – one dataset many dataviz approaches post, where Dave and Elena opened up the concept of #VisualisingHE to the #MidlandsTUG,  the result was a fantastic post showcasing the TUG’s many and varying vizzes.


My favourite viz – Elena

Elena’s contribution to the project has been invaluable. Her style is also unmistakable and has earned her quite a few Tableau public ‘viz of the day‘ accolades! My favourite is without doubt her solo post ‘destination Europe‘ a topic that really sang with her and it showed in her viz and really came over in her superb blog post.

Inflow & Outflow (6)

Interactive viz

My Favourite Viz – Dave

Dave consistently knocks out meaningful and insightful dashboards with care and thought. I love his style, ever clean and simple, which is, as we know often more difficult to achieve than the most complicated and intricate of vizzes.

I think my favourite viz of his is found in our September 18 post ‘A looksie at UCAS‘. It’s clean, simple and effective, uses a minimal colour palette but packs a punch.

UCAS subjects 2009 to 2018 (1)

Interactive viz

My Favourite Viz – created by me

For me, my favourite viz is probably one also featured in our latest post (Sept18). I have tried to develop my concept of #Coffeetableviz over the last couple of years, and this ‘looksie at UCAS viz’ gets close to the effect I have been trying to create. It isn’t totally finished and has a few elements I should tweak, however I think it packs a punch, gets the BANS over to the viewer, has a playful side, provides a takeaway but also encourages the user to take a deeper look at the interactive viz (and that’s what I wanted to achieve).

A looksie @ UCAS placed Applicants

Interactive viz

Given this post is about ‘my favourite bits’, I feel it justified to dwell on another post to which I enjoyed vizzing AND writing (I don’t always enjoy the writing bit as much as the vizzing if I’m honest!).

My favourite post and viz was my post on LEO – #Realtableau to funviz .

Why? Well I took a viz I crunched for my Exec team and Employability directorate and had a little fun with it over a few evenings at home. I really enjoyed documenting my journey of exploration, my battle with creating the radial chart and the iterative nature the viz took whilst working remotely with Dave and Elena. They have both always been on hand to comment on my viz outputs, suggest improvements and laugh with me, whilst I bash out expletives during the struggle to get a viz to behave!

The main viz for the post finished up looking like this:


Interactive viz

Thanks Dave and Elena, I have really learned a lot over the last 21 months.

Can’t wait for the next tranche of open HE data sets to emerge.

Thanks for reading.


A Looksie at UCAS placed Applicants 2018

So this month we decided to take looksie at UCAS again. This time focusing on the recently released ‘Placed Applicant’ data as at 31/08/2018, which is part of the daily clearing analysis 2018 statistical releases.

There has been a lot of talk around the demographic dip which will give Universities fewer 18 year olds, so we wanted to see where it has had an impact.  We also wanted to see to what extent International students felt welcome and the impact on areas, such as nursing, which had a change in funding arrangements.

International Interest

A looksie @ UCAS placed Applicants.png

A looksie at UCAS placed Applicants

Interactive viz

In the last year there has been a slight positive shift to both International student groups (Non EU and EU, excluding UK).  Both areas have increased in numbers as well which is a positive sign that International students still regard England as a place to come and study.  This has also offset some of the decline in the number of Home placed applicants.

Subject split

We started out by looking at how things have changed over three separate time periods (1 year, 5 years and the full 9 years in the data set).  What this showed was that despite the decline in the last year over the time period of the data set there has been an increase in nearly all the subjects with only one big subject seeing a decline.

UCAS subjects 2009 to 2018 (1)

Interactive viz

We then started looking at grouping up the subjects which showed that the majority of the placed applicants reside in the science and social science groups and these are also where the biggest increases over the time period are observed.  There has been a downward shift from Arts & Humanities over the last 9 years.

UCAS placed applicants by subject2.png

Placed Applicants by subject group

Interactive Viz


Nursing has changed shape in the HE setting quite a lot over the time of the data set.  There has been a shift to it being only accessible via a funded degree then they have moved to remove the funding.  It has always been a challenge for HE providers as there needs to be placement in place but ultimately it does lead to employment in most cases.  Looking at the data there looks to be a shift with 18 year olds over taking 20-24 year olds for the first time.  They still lag behind the 25-and-over, though, who make up the vast majority of the placed applicants for nursing.

B7 nursing placed applicants2

Placed Applicants – B7 Nursing only

Interactive Viz

So hopefully that was an interesting window into the latest UCAS placed applicants data.  Do let us know what you think.

Adam and Dave

National student survey – how happy are they?

So this year there was a change with the national student survey (NSS). This time it was not changes to the survey itself (last year the questions were changed and new themes introduced) but, rather, changes that it now came under the custody of the Office for Students (OfS) since they replaced HEFCE.

The changes were not dramatic but represented a subtle shift towards the students’ interests.  The data was not shared with Higher Education Institutions (HEI) before publication so that the students got to know the performance of the HEI at the same time as the Institutions themselves.

The other changes have been that the data has been presented in a slightly different, more accessible format.  It is a long way off dashboards with visualisations but the data is there for people to easily review.

So we at #VizHE decided to pick up from where we left off last year (NSS 2017: One data set, many dataviz approaches.) and pull out a few interesting nuggets for you.

The starting point was to see how the students had responded on a national level looking at the themes.  This was also tied into the monthly story telling with data challenge which was on dot plots.

NSS Theme score comparison (1)

The interactive viz: is here!

The main takeaway is that it really has not changed that much and where changes have occurred, they have been negative.  The only exception is Wales where the students have responded much positively this year compared to last.

Adam decided to reviz the headline theme performance overview originally posted on the OfS pages because he wished to make it easier to compare satisfaction with the themes rather than themes and years mixed up together.




NSS2018 themes.png

Following on from revizzing the headline NSS theme results Adam wished to dig a little deeper into the student satisfaction with the separately reported question 26 – ‘Students’ union’ part of the ‘Student Voice’ theme, highlighted above as very much lagging in student satisfaction compared to the sector theme scores.

Which unions are getting it right?

Which unions are getting it right

The interactive viz is here!

The main takeaway is that Alternative Providers (AP’s) appear to be getting it right more than Higher Education Providers and Further Education Colleges when taking the median score for each the provider types as a reference point. However, the populations are small for AP’s and this could be causing the volatility and wide distribution of scores seen in the figures. Hover over the box plot distributions to see which providers are getting a thumbs up from the students and who are in the dog house!

So that’s all from us for now. Thank you for reading and we hope you enjoyed exploring our  visualizations!

Dave and Adam

Team #VisualisingHE

Finding a Story in Europe’s Over-qualification Rates

What is the data about?

The data used in this post relates to information brought together by Eurostat in their Experimental statistics on ‘Skills’. Specifically, the “over-qualification rate by economic activity for the period 2008 to 2016”.

Rather than paraphrase the Eurostat definition, you can read it here. However, to briefly set out why we thought it was an interesting dataset for #VisualisingHE to have a quick play with, we’ve taken a few sentences from the Eurostat preamble about why we may need indicators about skills:

The European Commission communication ‘A New Skills Agenda for Europe‘ defines among others policy priorities and sets out actions to be undertaken with the aim to make better use of people’s existing skills. It should be ensured that the skills available in the labour market match the needs of businesses and the economy.

This undertaking includes finding ways to improve the matching between skills and labour market needs as well as bridging the gap between education and work. Skills mismatch indicators should measure the gap between demand and supply of skills (macro-level) as well as conditions of workers, jobs or vacancies (micro-level).

Where does the data come from?

The indicators are derived by combining available figures using EU Labour Force Survey (EU-LFS).

The use of a single source ensures consistency, comparability and reliability, and the methodology behind is based on the literature in the field (‘The skill matching challenge: Analysing skill mismatch and policy implications’ – Cedefop 2010) and on existing empirical exercises (‘Employment and labour demand’ – Eurostat 2016 and EU-LFS ad hoc module 2000).

Full methodological description including data.

What does the original data look like?

The image below is an example of one tab of the 8 occupational job categories, 2008-2016 by European country: Wholesale and retail trade, repair of motor vehicles and motorcycles


How might we present this data for better consumption?

Having brought the data together using Easymorph, Adam wanted to understand the distribution in proportions of over-qualified roles by occupation types for a single year (2016) to establish which sectors where hotbeds for skills mismatches across Europe and whether the data presented any areas with differing deviations.

The following chart highlights Transportation and storage as the occupation type with the highest median proportion surveyed, with Spain the country with the highest rate @ over 70%.


[Link to interactive viz]

This visualisation of the data provides the ability to grasp all of the proportions for all of the countries, for all of the occupation groups in one visual. The interactive viz also enables you to delve into where countries appear in the distribution of proportions.

How might we present the idea of change over time?

Wanting to recreate elements of the original data, a focus on the percentage point change in proportion over qualified between 2008 and 2016 followed:


[Link to interactive viz]

Again, presenting the change for all countries and all occupations using the box plots enables you to contextualise a particular countries change against that of the other countries.

Focus on a single occupational grouping

Could we create a simple highlight table only, the success of this is wholly down to Chris Love of the Information Lab, he helped me work through the calculations needed to rank by a single year, which turned out to be a little more complicated than initially thought. blog post here.

highlight table

[Link to interactive viz]

How could we present all the data in one visual?

Could we go one further and present all of the year data, all of the country data and all of the occupational data in one chart? And what would you get out of it?

Having a bit of fun with presenting change with Radials?  This view does draw out the changes so that you can see that wholesale and retail have had an increase in virtually all countries where as something like construction has had some big decreases and a fair few more moderate increases.

radial of change

[Link to interactive viz]

What about trend over time?

Elena took a more focused approach and only produced one visualisation that expands on the idea of change over time.

By utilising a slope graph and Tableau’s latest out-of-the-box style if step line charts, Elena’s dashboard allows the user to see how a country’s overqualification rate changed from 2008 to 2016 in each of the eight occupational areas whilst also allowing the reader to see the fluctuation over time. The colour indicates whether the rate for the country in the given year was above or below the median.

Overqualified in Europe.png

[Link to interactive viz]

So….. Did we find a story?

The data threw up plenty of small nuggets but no overall narrative so for the moment this post will be a demonstration of what data is there and how you can visualise the data in a few ways to present the proportions and change in proportions in more ‘graspable’ ways.  This also allows the viewer to identify some of the outliers and find trends in either a particular occupation type, or country.

Thanks for reading!

Team #VisualisingHE

(doing a post on something a little wider this month – Education, moreover over education!)

A tour around the Estate….

A bit of background…

This blog post comes straight off the back of a well-attended and thoroughly enjoyable HETUG (Higher Education Tableau User Group) Conference this week held at the University of Nottingham, at which Dave and I were lucky enough to present our wares.

Dave gave a fantastic presentation of his work on an approach to a ‘Matrix of Metrics’ – a topic whereby the key is in the ladders!!! He discussed how through engagement in data he has embedded targets and solid awareness of sector performance through clear and informative dashboards.

Myself, I wanted to demonstrate first and foremost the wealth and quality of information held by HESA accessible to HE providers through the HEDIPlus service. The HEIDIPlus service is a resource which enables a much-needed access to benchmarking data at provider level on many and varied HE topics – an enabler for providers to set appropriate targets for continued improvement in their strategic goals.

A key thread to my presentation was to highlight the flexibility Tableau (as a data visualisation tool) offers for enabling creative approaches to gaining engagement from non-data-savvy users and therefore facilitating the sharing of insights to a wider audience. My approach floated the notion of developing data awareness and building a data driven decision making culture through more infographic style visual dashboards.

And so, to this month’s post..

In this month’s post we celebrate a few nuggets from my explorations into, for many, the under explored HESA Estates Management Record (EMR) data set. What a treasure trove of figures this is, calling out for a bit of #VisualisingHE attention!

The freely available data broadly includes information about grounds and buildings, water and energy usage, waste management, transport and other environmental measures.

Within these broad areas hide some 228, YES, that’s 228 measures of estates related loveliness! In this post we have taken a few measures that caught the eye, and have given them a #VisualisingHE makeover.

First up is an interactive viz which presents a visual of the TOP30 providers in England boasting the highest number of buildings on their estate. This viz was inspired by Simon Beaumont after seeing something he knocked up for a #MakeoverMonday viz some months ago [Wk22 – world most expensive prime property], whereby he visualised the property as a city skyline. After stumbling over this no. buildings stat I thought I would apply a similar visual to this Estates dataset as a potentially approachable way to contextualise the volume of buildings a provider is maintaining on their estates.

In this viz each bar line denotes a provider and the height of the bar denotes the number of buildings a provider has. This viz simply uses a dual axis chart: one a simple bar chart and the other –  a custom shape to give the ‘buildings’ a top. A darker colour = more buildings.

Which Providers boast the highest number of buildings..

No. Buildings[Interactive Viz]

Next to peek my interest was the desire to understand the size of the estates providers manage. The visualisation below attempts to provide visual cue to area, by using a tree map chart type to express the area.

The top chart shows the sum of area in hectares by UK region, the one below displays the total site area by provider. You can click each region in the top chart and an action will filter the provider chart to provide an overview of the distribution of estate by provider in the selected region.

Total site area…

total site area

[Interactive Viz]

The viz below is the viz I used in my presentation at the #HETUG. This viz focuses on the total water consumption used in 2016/17 – overall and by provider. I have chosen to create this viz because the environment is a very topical issue and water consumption plays a significant part in the impact on the environment. I wanted to express and try to contextualise the seemingly vast consumption of water the UK HE sector consumes in a potentially engaging infographic.

Water Consumption…

Water consumption_Coffetable[Interactive Viz]

In the spirit of encouraging the ‘green’ ethos I thought I would wheel in a cheeky viz of the number of cycle spaces UK providers have installed on their campuses/estates to facilitate safe parking and encourage green and healthy commuting for both their staff and student bodies. The viz below highlights the TOP10 providers provision.

Build the ‘Cycle’ spaces & they will come…

no of cycle spaces

[Interactive Viz]

And finally for this months blog we focus on a key by-product of all the amazing stuff HE providers do in their cycle and that is waste mass! Question is what do we do with it all?

Tonnes of waste mass by type…

As part of Cole Knaflic’s monthly #SWDchallenge (June) she challenged the community to explore the use of slope charts (SWD’s June blog). For this I pulled together a viz on the UK HE’s total waste mass in tonnes by type between 2015/16 and 2016/17.

UK HE Waste mass by type

[Interactive Viz]

Let me know whether these visualisations encourage you to take a closer look at the data available on how UK HE providers manage their estate.

Many thanks for reading!

Adam and the #VisualisingHE Team