10/7/2016

Postal big data: Global flows as proxy indicators for national wellbeing

 

A new project has developed an innovative means to approximate socioeconomic indicators by analyzing the network of international postal flows.

The project used 14 million aggregated electronic postal records from 187 countries collected by the Universal Postal Union over a four-year period (2010-2014) to create an international network showing the way post flows around the world.

In addition, the project builds upon previous research efforts using global flow networks, derived from the five following open data sources:

For each network, a country’s degree of connectivity for incoming and outgoing flows was quantified using the Jaccard coefficient and Spearman's rank correlation coefficient.

Although the highest Jaccard overlap was between the postal and trade networks, the rest of the networks did not strongly overlap in terms of edges.  Conversely, the Spearman rank correlation revealed that the volume of goods, people, and information flows are correlated, with the exception of the digital communications network, which was entirely uncorrelated with any other network.

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Image: Comparative analysis of Postal Network to other networks (CC BY 4.0).

To understand these connections in the context of socioeconomic indicators, the researchers then compared these positions to the values of GDP, Life expectancy, Corruption Perception Index, Internet penetration rate, Happiness index, Gini index, Economic Complexity Index, Literacy, Poverty, CO2 emissions, Fixed phone line penetration, Mobile phone users, and the Human Development Index.

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Image: Spearman rank correlations between global flow network degrees and socioeconomic indicators (CC BY 4.0).

From this analysis, the researchers revealed that:

  • The best-performing degree, in terms of consistently high performance across indicators is the global degree, suggesting that looking at how well connected a country is in the global multiplex can be more indicative of its socioeconomic profile as a whole than looking at single networks.
  • GDP per capita and life expectancy are most closely correlated with the global degree, closely followed by the postal, trade and IP weighed degrees - indicative of a relationship between national wealth and the flow of goods and information.
  • Similarly to GDP, the rate of poverty of a country is best represented by the global degree, followed by the postal degree. The negative correlation indicates that the more impoverished a country is, the less well connected it is to the rest of the world.
  • Low human development (high rank) is most highly negatively correlated with the global degree, followed by the postal, trade and IP degrees. This shows that high human development (low rank) is associated with high global connectivity and activity in terms of incoming and outgoing flows of information and goods.

After finding that metrics such as the network degree can be used to estimate wellbeing at a national level, the researchers looked that these in relation to the connectedness between pairs of countries - guided by the hypothesis that countries that are paired together in communities across more networks are more likely to be socioeconomically similar. To test this, they conducted a community detection analysis in each individual network using the Louvain modularity optimisation method. These communities were then compared via the community multiplexity index - a measure of socioeconomic similarity derived from the difference between each socioeconomic indicator for two countries plotted against their community multiplexity. Multiplexing is the process of placing one network on top of another, so that if one network partially covers a region, then another can help plug the gaps in the corresponding socioeconomic understanding. Each separate network was treated as a different “layer” in this model, and connections between countries were considered for all of the networks in combination.

As the below graph illustrates, the researchers observed that countries with the greatest community multiplexity have the smallest margin of difference across all indicators - or, in other words, that countries with the highest community multiplexity have a very similar socioeconomic profile.

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Image: Socioeconomic difference margin between countries who share communities in the global flow networks (CC BY 4.0).

Moreover, every country analyzed by the study was found to connect with an average of 110 other countries in two or more networks. The global network degree – measuring a country’s connectivity across all six networks in combination – served as the best proxy for half of the indicators considered when compared to the individual networks.

Read the fully study here.

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