Contains Duplicate III

Given an array of integers, find out whether there are two distinct indices i and j in the array such that the absolute difference between nums[i] and nums[j] is at most t and the absolute difference…

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Introducing the Local Needs Databank

A screenshot from the Local Needs Databank showing Covid-19 cases across Local Authorities

This databank was created because there’s a large amount of data out there about the economic vulnerability of various communities, as well as the health and Covid-19 statistics of those communities. Until now, this openly available data was scattered across many different sources. The Local Needs Databank brings all this valuable data together into a simple dashboard, so that organisations and individuals can look at it all at once, make comparisons, and more accurately identify potential demand for their services.

Other data relating to demographics and Covid-19 is taken from a wide range of sources, and includes:

Essentially, just by improving access to all this data, it enables those in the sector to make better decisions. This isn’t just general data on poverty alongside some Covid-19 statistics — this is data provided by charities who are working on the ground to meet the needs of those in crisis. The charity data gives context to poverty statistics, and helps paint a much more detailed picture of the state of poverty in the UK.

This is the first time shared knowledge in this sector has been consolidated like this. The databank shows us data on the most vulnerable groups, and the most deprived parts of the UK, and compares that with the demand that charities are seeing. Having a greater understanding of how demand changes around the country has now enabled other organisations to make meaningful impact at a local level.

Here’s a quick example: let’s say you work for a charity that is seeking to support children and young people in specific local authorities and regions of the country. You’d likely want to start by looking into areas with the most urgent need for this support, so that you can proactively design programming for them.

The databank makes it extremely straightforward to do this. You can find the areas that have the highest rate of child poverty, and look at the charity density. Then you can compare that with demand data provided by charities and identify potentially underserved areas. If you also take the insights generated by your charity into account, you can get an accurate understanding of which areas to prioritise with your services.

This example hopefully makes two key things clear: firstly, targeting your support to the ‘right’ places is now much more straightforward than it was before. Secondly, the more charities join the databank, the more useful it will be — more detail on local demand will only make it easier to have a local impact.

If your organisation generates data relating to UK poverty, you already meet half the criteria to join. The other half is to want to be part of an initiative which empowers organisations across the sector to make data-driven decisions which lead to greater impact on a local level.

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