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Funding playground – who funds with who in the UK?


This blog was published in 2018. Please visit for up to date visualisations and statistics. 

For #GivingTuesday2018 we are sharing a cool new visualisation by data scientist David Kane. Below he talks through what he’s created, and offers a recipe for making (or commissioning) your own version:

The great thing about grantmakers publishing data about who they fund in the 360Giving Standard is that we can start to explore the network of UK grants, and answer one of the questions that 360Giving was formed to answer – who funds who?

One way of doing this is to look at how many recipients have received funding from two or more funders, which can be visualised with a chord diagram. This type of visualisation shows all the entities (in this case funders) in a ring, with lines showing connections (in this case shared grant recipients) between them.

I’ve used the fantastic Flourish tool to create this.

(If the diagram doesn’t display properly, please view it here.)

What this shows…

This visualisation shows that funders with high numbers of grantees have deep links between them, with 500 organisations receiving funding from both the Big Lottery Fund and the Co-operative Group in 2017.

The results also show the connections between funders that concentrate on one area or specialism. Highlighting the Robertson Trust, for example, shows the connections with other Scottish funders like Corra Foundation, SCVO and RS Macdonald.

It’s important to remember that the proportion of a funder’s grantees varies – the majority of recipients only receive grants from one funder. For example, only 12% of recipients of Big Lottery Fund grants also received funding from another 360Giving funder in 2017. For the Co-operative Group it’s 15% and Garfield Weston 39%. Generally, funders that make larger grants to larger organisations are more likely to have recipients with multiple funders.

Recipients with multiple funders for the top 10 funders in 2017 (by number of grants)


Funder Proportion of recipients with multiple funders
The Big Lottery Fund 12.2%
Co-operative Group 14.7%
Garfield Weston Foundation 38.5%
Sport England 6.7%
Quartet Community Foundation 16.9%
The Robertson Trust 38.5%
Heart of England Community Foundation 20.0%
The Tudor Trust 48.0%
Lloyds Bank Foundation for England and Wales 58.6%
Community Foundation serving Tyne & Wear and Northumberland 20.1%


For the big three organisations that share the most connections, both the Big Lottery Fund and Garfield Weston give a median grant of £10,000, while the Co-operative Group’s median grant is £2,384. The Co-op made 7,000 grants in 2017 mostly of under £5,000. Big Lottery has its own Awards for All programme of grants of up to £10,000.

What other questions does this raise for you?

This post gives us a taste of the analysis that becomes easier when grants data is shared to the 360Giving Standard. Next steps in the analysis could be to look at the profile of these charities and how they were funded over a period of time to see if there is a trend that can help grantmakers in their funding decisions.

Remember that this kind of analysis and comparison is only possible when grantmakers include consistent identifiers (like a charity or company number) for the organisations they fund.

The steps for creating the chord diagram:

  1. Download the data you need from GrantNav. In this example I’ve filtered a list to include all the 31,300 grants in GrantNav for 2017.
  2. Create a list of all combinations of recipient and funder. This example was based on organisation identifiers for recipients and funders, making sure that recipients can be tracked between multiple funders. This means that where a recipient has received more than one grant from a funder they only have one row in the data table.
  3. Filter this list to remove recipients that only received grants from one funder in this period. This reduces the 24,976 unique recipients to 2,316.
  4. Reshape the data table so we have a list of the funders showing how many recipients they have in common with other funders. This forms the input to the chord diagram.

Unfortunately, you can’t do all of this data preparation in Excel or other spreadsheet applications. In this example, I’ve used the Python programming language and the pandas data analysis library. You can see the code used to produce the source data in a python notebook.

Please share yours

Have you used any tools (including GrantNav or Beehive) to better understand the grants you have made or received? We would love to share your examples with the rest of the community!