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The easy access to large data sets has allowed for leveraging methodology innetwork physics and complexity science to disentangle patterns and processesdirectly from the data, leading to key insights in the behavior of systems.Here we use to country specific food production data to study binary andweighted topological properties of the bipartite country-food productionmatrix. This country-food production matrix can be: 1) transformed into overlapmatrices which embed information regarding shared production of products amongcountries, and or shared countries for individual products, 2) identify subsetsof countries which produce similar commodities or subsets of commodities sharedby a given country allowing for visualization of correlations in largenetworks, and 3) used to rank country's fitness (the ability to produce adiverse array of products weighted on the type of food commodities) and foodspecialization (quantified on the number of countries producing that foodproduct weighted on their fitness). Our results show that, on average,countries with high fitness producing highly specialized food commodities alsoproduce low specialization goods, while nations with low fitness producing asmall basket of diverse food products, typically produce low specialized foodcommodities.

A data driven network approach to rank countries production diversity and food specialization