Merge pull request #951 from datopian/openspending/berlin-erdf-data-driven-story
OpenSpending - Berlin ERDF spendings data-driven story
This commit is contained in:
commit
b34220cac7
@ -0,0 +1,105 @@
|
||||
---
|
||||
title: Where does Berlin spends its ERDF benefits?
|
||||
date: 2023-06-12
|
||||
authors: ['João Demenech']
|
||||
---
|
||||
|
||||
In this data-driven story, let's analyze how the city of Berlin has benefited from ERDF from 2008 to 2015.
|
||||
|
||||
## What is ERDF?
|
||||
|
||||
If you're not familiar with ERDF, it's the European Regional Development Fund. According to its [official website](https://ec.europa.eu/regional_policy/funding/erdf_en):
|
||||
|
||||
> The European Regional Development Fund (ERDF) aims to strengthen economic, social and territorial cohesion in the European Union by correcting imbalances between its regions. In 2021-2027 it will enable investments in a smarter, greener, more connected and more social Europe that is closer to its citizens.
|
||||
|
||||
## A look into the data
|
||||
|
||||
The dataset that will be used is this one: https://www.openspending.org/@os-data/de3-berlin-2007-2013-erdf (if you're looking for ERDF data for other cities/regions, take a look at the data catalog).
|
||||
|
||||
Here's a sample of this data:
|
||||
|
||||
<FlatUiTable url="https://storage.openspending.org/de3-berlin-2007-2013-erdf/concat.csv" />
|
||||
|
||||
Note that we will only use the rows where the "amount_kind" column is set to "total_amount".
|
||||
|
||||
## How has the total amount changed over the years?
|
||||
|
||||
The line chart below shows the sum of the total amount of EUR that was approved for each year:
|
||||
|
||||
<LineChart data="https://storage.openspending.org/de3-berlin-2007-2013-erdf/berlin-erdf-2007-2013-total-amount-apporved-per-year.csv" xAxis="approval_year" yAxis="total_amount" />
|
||||
|
||||
As you can see, the total increased significantly from 2007 to 2010, later fluctuating between €7 million and €9 million per year.
|
||||
|
||||
## Who were the top beneficiaries for each year?
|
||||
|
||||
Now, let's try to understand who were the top beneficiaries each year. To do that, let's filter the data to show only the two top beneficiaries for each year:
|
||||
|
||||
<FlatUiTable url="https://storage.openspending.org/de3-berlin-2007-2013-erdf/berlin-erdf-2007-2013-total-amount-approved-per-year-per-beneficiary--top-2.csv" />
|
||||
|
||||
Since there are many years in this range, let's split this analysis into two ranges: one for 2008-2010 and the other for 2011-2015.
|
||||
|
||||
<VegaLite spec={{
|
||||
"data": { "url": "https://storage.openspending.org/de3-berlin-2007-2013-erdf/berlin-erdf-2007-2013-total-amount-approved-per-year-per-beneficiary--top-2.csv"},
|
||||
"transform": [
|
||||
{"filter": "datum.approval_year <= 2010"},
|
||||
],
|
||||
"title": "Total Amount (EUR) x Approval Year (2007-2010)",
|
||||
"width": "container",
|
||||
"mark": {"type": "bar", "tooltip": true },
|
||||
"encoding": {
|
||||
"y": {
|
||||
"aggregate": "sum",
|
||||
"field": "total_amount",
|
||||
"title": "Total Amount (EUR)",
|
||||
"stack": null
|
||||
},
|
||||
"x": {"field": "approval_year", "title": "Approval Year"},
|
||||
"color": {
|
||||
"type": "nominal",
|
||||
"field": "beneficiary_name",
|
||||
}
|
||||
}
|
||||
}} />
|
||||
|
||||
<VegaLite spec={{
|
||||
"title": "Total Amount (EUR) x Approval Year (2011-2013)",
|
||||
"data": { "url": "https://storage.openspending.org/de3-berlin-2007-2013-erdf/berlin-erdf-2007-2013-total-amount-approved-per-year-per-beneficiary--top-2.csv"},
|
||||
"transform": [
|
||||
{"filter": "datum.approval_year > 2010"},
|
||||
],
|
||||
"width": "container",
|
||||
"mark": {"type": "bar", "tooltip": true },
|
||||
"encoding": {
|
||||
"y": {
|
||||
"aggregate": "sum",
|
||||
"field": "total_amount",
|
||||
"title": "Total Amount (EUR)",
|
||||
"stack": null
|
||||
},
|
||||
"x": {"field": "approval_year", "title": "Approval Year"},
|
||||
"color": {
|
||||
"type": "nominal",
|
||||
"field": "beneficiary_name",
|
||||
}
|
||||
}
|
||||
}} />
|
||||
|
||||
It's easy to spot some repeating colors. Now, let's see to which sector the top beneficiaries belong to:
|
||||
|
||||
<Table csv="
|
||||
Beneficiary,Sector
|
||||
D & B,Education
|
||||
Stiftung Sozialpädagogisches Institut Berlin (SPI),Education/Social work
|
||||
IB e.V.,Social work
|
||||
gsub-Projektegesellschaft mbH,Consulting
|
||||
BBW Berufsvorbereitungs-u.Ausbildungsgesellschaft,Education
|
||||
Arbeit und Bildung e.V.,Consulting
|
||||
Gesellschaft für soziale Unternehmensberatung GSUB,Consulting
|
||||
SPI Consult GmbH,Consulting
|
||||
Beuth Hochschule für Technik Berlin,Education
|
||||
TU Berlin, Fakultät II,Education
|
||||
Hochschule für Technik und Wirtschaft Berlin,Education
|
||||
WeTeK gGmbH,Education
|
||||
" />
|
||||
|
||||
Based on the sectors of the top beneficiaries, it's clear that education and professional training have been a major focus of ERDF investment in Berlin during that period of time.
|
||||
Loading…
x
Reference in New Issue
Block a user