Bbean and the African Union invariably partition populations under nation states.

Bbean and the African Union invariably partition populations under nation states. In this context, the nation state is the primary geographical entity considered for funding, planning and allocation of resources for development. Despite the importance of accurate statistics to quantify the state of a country and progress towards favourable socio-economic outcomes, regular and reliable measurement is difficult and costly particularly in low income countries. With this in mind, in this section we compare the positions of countries within the different networks discussed previously to the values of several socioeconomic indicators. Fig 7 shows the Spearman rank correlation between the network degrees of the six networks (in and out degree, and weighted in and out degree) and various socio-economic indicators: GDP, Life expectancy, Corruption Perception Index (CPI), Internet penetration rate, Happiness index, Gini index, Economic Complexity Index (ECI), Literacy, Poverty, CO2 emissions, Fixed phone line penetration, Mobile phone users, and the Human Development Index. These indicators and their significance for the international development agenda are described in detail in the data section (see Table 1). For each of the six networks, we compute the network degree, defined as the sum of the neighbours for both incoming and RG7666 web outgoing connections where directed. This reflects how well connected a country is in a particular network. We also take into purchase Nutlin-3a chiral account the amount of connectivity by computing the weighted incoming and outgoing degrees on each network, defined as the sum of the normalised flows from all neighbours and reflecting the volume of incoming and outgoing flows. In addition to these standard single-layer network metrics, we define and compute the global degree of a country, which takes into account connectivity across all networks.PLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,11 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 6. Comparative analysis of Postal Network to other networks in terms of Jaccard overlap, percent shared edges, edge weight correlation and in and out degree correlation. doi:10.1371/journal.pone.0155976.gPLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,12 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 7. Spearman rank correlations between global flow network degrees and socioeconomic indicators. doi:10.1371/journal.pone.0155976.gAll degrees of single networks and the global degree appear vertically in Fig 7 and all indicators appear horizontally. In general, weighted outgoing degrees on the single networks perform best for the postal, trade, ip and flight networks. An exception from the physical flow networks is the migration network, where the incoming migration degree is more correlated with the various indicators.The best-performing degree, in terms of consistently high performance across indicators is the global degree (for 7 out of indicators). This suggests 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. A detailed correlation matrix including correlation coefficients is supplied in S1 Fig. The GDP per capita and life expectancy are most closely correlated with the global degree, closely followed by the postal, trade and ip weighed degrees. This shows a relationship between national wealt.Bbean and the African Union invariably partition populations under nation states. In this context, the nation state is the primary geographical entity considered for funding, planning and allocation of resources for development. Despite the importance of accurate statistics to quantify the state of a country and progress towards favourable socio-economic outcomes, regular and reliable measurement is difficult and costly particularly in low income countries. With this in mind, in this section we compare the positions of countries within the different networks discussed previously to the values of several socioeconomic indicators. Fig 7 shows the Spearman rank correlation between the network degrees of the six networks (in and out degree, and weighted in and out degree) and various socio-economic indicators: GDP, Life expectancy, Corruption Perception Index (CPI), Internet penetration rate, Happiness index, Gini index, Economic Complexity Index (ECI), Literacy, Poverty, CO2 emissions, Fixed phone line penetration, Mobile phone users, and the Human Development Index. These indicators and their significance for the international development agenda are described in detail in the data section (see Table 1). For each of the six networks, we compute the network degree, defined as the sum of the neighbours for both incoming and outgoing connections where directed. This reflects how well connected a country is in a particular network. We also take into account the amount of connectivity by computing the weighted incoming and outgoing degrees on each network, defined as the sum of the normalised flows from all neighbours and reflecting the volume of incoming and outgoing flows. In addition to these standard single-layer network metrics, we define and compute the global degree of a country, which takes into account connectivity across all networks.PLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,11 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 6. Comparative analysis of Postal Network to other networks in terms of Jaccard overlap, percent shared edges, edge weight correlation and in and out degree correlation. doi:10.1371/journal.pone.0155976.gPLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,12 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 7. Spearman rank correlations between global flow network degrees and socioeconomic indicators. doi:10.1371/journal.pone.0155976.gAll degrees of single networks and the global degree appear vertically in Fig 7 and all indicators appear horizontally. In general, weighted outgoing degrees on the single networks perform best for the postal, trade, ip and flight networks. An exception from the physical flow networks is the migration network, where the incoming migration degree is more correlated with the various indicators.The best-performing degree, in terms of consistently high performance across indicators is the global degree (for 7 out of indicators). This suggests 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. A detailed correlation matrix including correlation coefficients is supplied in S1 Fig. The GDP per capita and life expectancy are most closely correlated with the global degree, closely followed by the postal, trade and ip weighed degrees. This shows a relationship between national wealt.

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