How a simple two-axis bubble chart can help universities understand what is really happening in their teaching spaces

Universities across the UK and Ireland report space utilisation figures to national bodies such as HESA and the HEA. These figures typically combine occupancy and frequency into a single percentage. While this metric provides a standardised snapshot, it may not capture the full picture of how spaces are used.

At Baker Stuart, we approach space analysis with a method that looks in more depth at how spaces are actually used. It’s not just about numbers – it’s about understanding the story they tell. During a recent Higher Education roundtable we hosted, the need for a better metric for utilisation was a key point of discussion. Rather than relying on simplistic metrics, we use a more nuanced approach: plotting frequency against occupancy, with bubble size representing room capacity and colour used to distinguish space types.

This method brings utilisation data to life, revealing usage patterns, identifying underutilised spaces and highlighting where adjustments can make the biggest impact. It’s a method we have developed and refined through our work with university clients – and it consistently delivers clearer insights and more confident decision-making.

In this latest blog we explain more about this approach and how it can help understanding of space utilisation.

The traditional utilisation formula (and its flaws)

Most universities track space use with a simple formula: utilisation = occupancy × frequency.

Occupancy is how full the room is when in use, frequency is how often it is used, as a percentage of available timetable slots. Multiply these two, and you get a utilisation percentage e.g.

Room A: 90% occupancy × 30% frequency = 27% utilisation
Room B: 30% occupancy × 90% frequency = 27% utilisation

Both rooms report exactly the same figure but, as you can see, they tell two very different stories. Room A may be popular but poorly scheduled whilst room B may be heavily booked for activities with smaller group sizes, yet the headline number gives you no clue.

At our recent roundtable, this limitation sparked a lively discussion, the estates teams present agreed that the single utilisation percentage is not nuanced enough to inform good decisions, especially in a world where teaching and attendance patterns are often very varied and evolving rapidly.

Why the traditional view hides the root cause

The main problem with a single utilisation percentage is that it lumps together two very different statistics about room use. It can’t tell you whether a room is underused because it is rarely booked (low frequency) or because it is booked but underfilled (low occupancy), it gives no indication whether the room is the right size or type for its demand and it doesn’t show which types of rooms are the issue.

It also doesn’t factor in room size. A small seminar room at 30% utilisation is not the same operational problem as a 200-seat lecture theatre at 30%, yet the metric treats them equally.

A better way: visualising frequency vs occupancy

To solve this, we have developed a simple but powerful way of visualising the data we collect when we undertake our space utilisation surveys for our university and other higher education clients. We use what are often called scatter diagrams or bubble maps as follows:

• X-axis: frequency (% of available time used)
• Y axis: occupancy (% of room capacity filled when used)
• Bubble size: room capacity (number of seats or square metres)
• Bubble colour: room type or space category

This gives you a clear visual map of how each room is performing across all key factors. We usually model this on our online portal which allows us and our clients to filter the data set further to really drill down into different space types, sizes, location and even by nominal owner (faculty, school and department).

Here is an example of this approach applied to a university dataset from a recent space utilisation survey we undertook for one of our university clients.

Several patterns immediately stood out:

• Some of the larger spaces are used frequently but at very low occupancy rates. These are often lecture spaces that are overbooked for small group activities, usually due to legacy timetabling or changes in student behaviour.
• Rooms with low frequency but better occupancy when used. These are often specialist rooms or smaller spaces that are not well integrated into central scheduling or are restricted to certain departments.
• Several spaces had little or no usage in the study period. Two of the lecture theatres had very low usage and two specialist spaces were not observed in use at all. The specialist spaces were allocated to a particular department and upon further engagement we discovered they were no longer used and surplus to requirements unless the curriculum changed again.
• A significant number of larger spaces were underused on both dimensions. These are clear candidates for further investigation and possible repurposing.
• Variation by space type. Colour coding the bubbles by room category revealed that different room types behave very differently. Lecture theatres perform quite differently from seminar rooms or specialist spaces. This must be reflected in planning decisions.

This visual approach allowed us to quickly see where to focus further engagement with the relevant faculties to understand why the spaces were underperforming and recommend actions to improve utilisation or to free up space for other purposes.

Diagnosing the root causes

As you can see from the real-world example above there are a number of very different root causes that require different actions. If we look at each quadrant in our model these are:

Low frequency, high occupancy
This often suggests a timetabling or ownership issue. The room may be in a less desirable location or may be restricted to a particular department. It may lack the facilities that other departments need.

High frequency, low occupancy
This points to poor space fit. Class sizes may have declined. The room may be too large for its current uses. Students may be attending online or accessing material later, leaving booked spaces underused.

Low frequency, low occupancy
Spaces in this part of the chart are unlikely to be adding value. The room may no longer suit current teaching needs. It may have been left in the timetable out of habit rather than genuine demand.

High frequency, high occupancy
These are the star performers. These rooms are in demand and well used. Estates teams should monitor them to ensure they remain fit for purpose as needs change.

Moving from “how bad is it?” to “why is it like this?”

At our roundtable, one estates lead put it perfectly:

“A single number can tell me how worried I should be, but it can’t tell me why the problem exists, or how to fix it.”

This is where the two-axis visual helps. It allows teams to ask more useful questions. Why is this space underused? Is it the wrong type of space? Are we timetabling effectively? Are we matching room size to cohort size? Are some space types performing better or worse than others?

Adding colour by room type is especially useful here. It helps show whether the estate is providing the right mix of space types, or whether certain categories need attention. Further, filtering the graph or switching the colour coding to look at the space owner can also very revealing – do centrally managed and booked spaces perform better, are certain faculty owned spaces performing better than others?

Final thoughts

I have long felt that the single number for utilisation was not the answer. While it may offer a convenient benchmarking tool, it rarely tells the full story – and more often than not, it leads to misleading comparisons. One university’s performance cannot be meaningfully measured against another’s using a single metric. It’s apples and pears.

If you want to understand your estate better, don’t rely on one number. A simple frequency vs occupancy chart, with bubble size and colour for space type, will give you a much clearer view of what is really happening in your teaching spaces. It will help you focus your efforts where they will deliver the most value. It will help you make the case for change, and it will allow you to get away from single number meaningless targets and to be able to undertake proper evidence-based planning.

This is what we do for a living, for universities, the NHS and other public sector and corporate clients. If you would like us to help you build this view for your own estate and turning it into something more meaningful and actionable, just ask. It is one of the best ways we have found to turn space data into practical insight and action. Without proper interpretation or insight, data is just data. It might make us feel good about how we are performing but doesn’t answer the question I always ask – “So What?”.