The website to explore the results in more detail is available here.
The publication with this analysis is freely available at The Lancet Planetary Health.
We retrieve SDG data from the World Bank for all SDGs except SDG 13 which we take from the UN Statistics Division. Annual average temperatures on a country-level are taken from the CRU data set. The 17 SDGs and climate change, measured by annual average temperature, are seen as 18 variables which want to be analysed on their inter-dependencies, often referred to as interlinkages. We compute the partial distance correlations between any two variables given any subset of the remaining variables.
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1_Temperature prepares the CRU data set to be appended to the SDG indicators.
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1_data_preparation splits data in separate
csvfiles per country, appends the temperature, and standardises the data. -
2_imputations_concatenating imputes missing values in the data set with a weighted k nearest neighbour (w-kNN) algorithm, and averages and concatenates data to target and goal-level.
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3_distance_cor_continents and 3_distance_cor_groups computes the partial distance correlation between each unique pair of two variables given any subset of the remaining variables for continents and groups, respectively. These notebooks include the visualisations of the networks and eigenvector centrality figures.
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5_Additions includes the visual exploration of dependencies.