In Depth Analysis of UN Sustainable Development Goals’ Progression using Data Science
By: Andrew Hong
Tags: Editors Pick, Data Journalism, Data Visualization, Data Science, Machine Learning

Data Journalism

In Depth Analysis of UN Sustainable Development Goals’ Progression using Data Science

A deep dive into the partnerships of the seventeen SDGs using web scraping, exploratory data analysis, time series analysis, and natural language processing/soft cosine matrices. Andrew Hong Aug 11, 2020·10 min read

The 2030 Agenda stands for renewed commitment to people, prosperity, planet and peace through strengthened global partnership. The Sustainable Development Goals (SDG) were set in that agenda in 2015, following the Millennium Goals of the previous decade that had targeted developing countries. It’s been five years since that announcement, so I thought it would be a good time to do some preliminary analysis of the partnerships (which are the largest drivers of this change). For those unfamiliar with the goals, you can find a summary of their statements, targets, and indicators here.

While there are 17 goals, they can each be segmented into three general categories (quoted from source):

1) It should support countries to invest in higher value added products and sectors, technology, a diversified economy and greater productivity, all of which should generate quality jobs and livelihoods (SDGs 7 to 15 highlight critical issues relating to such a transformation). 2) Only then can countries meet the most pressing needs of their societies, including the eradication of extreme poverty (SDGs 1 to 6 reflect this social agenda and the human centered approach to development required to underpin and accelerate sustainable economic growth). 3) Finally, for the first time, the 2030 Agenda acknowledges the quantum leap required in the capacities, collaboration and resources for such a transformation to occur (SDGs 16 and 17 speak to the means by which social contracts are strengthened within and among States).

Based on the above, I believe there should be partners spanning similar goals, as well as similarities in types of assistance/geographic coverage of these goals. The data I collected can be found on the UN SDG Partnerships Platform. It shows 4585 unique partners, with about 14249 total partnerships. The columns I used are shown below (example of partnership here):

All data, interactive charts, and scripts can be found at my GitHub repo.

This article will explore the following questions:

What is the geographic coverage and cross coverage of providers/beneficiaries? Who are the top entities and sub-partners? How has partnership momentum changed over the years? What is the relationship between partnerships for each goal? Is the overlap what we would expect? How diverse is the resource assistance for each goal?

Geographic Coverage:

Let’s start by looking at what countries were the top beneficiaries for each goal. The data for beneficiaries came in variations of this:

ChinaItalyMexicoMoroccoSouth AfricaSpainUruguay

I used re.findall on capital letters, then created a set of the extended list to find unique countries. Since some countries are multiple words (like “South Africa”), they would show up as two unique countries — with that in mind, there were 234 unique beneficiaries. About 7000 of the partnerships did not list a beneficiary on their page, so this data could be slightly skewed.