When 360 Giving GrantNav opened its huge datasets about UK Grant data, I asked myself “what can I understand about things that happen so far away from my region (south américa)?”
In the next few dataviz analysis I´ll try to dig into the only thing I can add: a southern point of view.
I´m more familiar with the overseas grantees point of view, so I´ll focus my experiments in that particular group.
That being said, I found pretty difficult to understand the dataset as I couldn´t find a simple “metadata file” and I had to go to the documentation of the standard, which it´s english only. It´s not a simple matter of translation. Some information needs to be explained as it´s not the same in different countries or regions.
So: how much of those grants are going outside of the United Kingdom?
First of all I´ve found the data it´s not complete. The amount of rows that has no Country Code attached is huge. We are talking about 80% of the full dataset showing (empty) on country code:
We could try to geolocate those or try to pinpoint their location, but that was not the idea behind this exercise, so we just discarded them and worked on the universe of “data we could relate to a country”:
So, we know we are going to be working on a tiny margin on the dataset, but living in a country only known by Messi or corruption cases, makes you accustomed to work on the outskirts.
So, when we strip out Empty and Great Britain records, we end up with a “rest of the world” beneficiaries map.
In this map we can see regions and countries sized by the sum of the amount each countries beneficiaries have been awarded.
As usual, Africa is the place to put the grants.
Now, who is giving the grants to which region?
As there are so many countries, we’ll have a look at how the Funding orgs divide their grants in regions.
Again, we can confirm Africa is the place to send the money. We see that almost all founders are giving most of their overseas grants to African region.
What about the subjects and topics?
So now that we know somwhat who is putting money where, let´s see if we can go into subjects and topics. Here, the dataset had *some* data, but I had to editorialize a little bit: Add missing info, based on the title of the grant, and aggregate subjects to form a small amount of categories.
I ended up with this scenario, sizing the topics based on the amount of money each one received.
As one can imagine, most of the money is going to essential needs as Health, Education, Development and youth-women minorities.
But what about the amount of projects granted? In the previous graphic, apart from sizing the boxes by the amount of money each topic gets, I´ve coded with the intensity of the color the amount of projects each topic has.
With this we can conclude that Development and Employment have a lot of projects with less money per project than Health, for example, that looks like it has fewer amount of projects. But let´s focus on this issue with a separate view:
We can see that Development and Employment are the most funded and the most frequent projects, while Foia and Data Journalism related are the fewer in both areas. We can also see that Civil Society -Governance-Human Rights projects are way cheaper than Health ones.
What about timeframe?
Another important quality of grants is the timeframe of them. Some topics may be a quick punch in time, other ones needs more time to develop.
Here we see all of the overseas projects lie down in a timeline and separated by region and topic.
As an eagle view it´s interesting but we will have to dig deeper.
If we lie side by side Development (left) and Health (right), we can see that they have different time distributions:
While Development projects tend to be longer in time and more sparse in time, Health ones looks like are more concentrated in 2017–2020 area of the timeline, with shorter durations.
We can choose another significant pair: Education (left) and Women-Young-Child (right):
In these two we confirm that Education projects are the longest and the most future propelled ones (good idea!) and women are the ones that really spread all over the four regions. Sadly, these doesn´t look to be synched with Education ones, what would be awesome.
We could, of course, get things the other way around and forget about the regions and focus on topics, and we can confirm some of the previous insights:
What about duration?
When you´ve been more in the “applying for grants” side than in any other, you know that the duration of any grant it´s important. Most of the times, if you are a small organization in a underdevelopment environment, you need time to even start to make things happen.
Here I´ve coded the duration as the size of the bubbles, and the horizontal value tells us the amount of money granted.
As expected, more duration usually means more money and vice versa, but there are some exceptions on both sides.
What are those long duration but cheap projects on the left side (marked in red)? What about those short spanned expensive ones (marked by the arrow)?
This will pinpoint interesting outliers to go and have a look what kind of projects they are.
Jumping to conclusions
- We will always need more metadata. Especially when looking at things from far away and or distant cultures.
- Even if we know little about the subject being inspected, dissecting the data as shown, could give us some pretty interesting insights.
- If I were more informed on the realities of the African continent, which is a world in itself, I´d probably be able to understand why most of these projects behave like they do.
- I´m happy to see Health, Development and Education being the higher funded projects in any undeveloped part of the world (like mine). I wonder when this can start to tip over and underdeveloped regions of the world start to get more attention on the upper end of the development spectrum. Maybe Trickle-down development by grants (?) could yield the way to different approaches. Yes, you need basic development to going up into the ladder, but maybe you also need something up there to make you want to climb it!
If you find any error or insight in my shallow analysis, please comment!