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

TL;DR

This paper employs network analysis on country-food production data to rank countries by diversity and specialization, revealing patterns in global food production and country capabilities.

Contribution

It introduces a novel network-based methodology to quantify and visualize country food production diversity and specialization, using bipartite matrices and ranking metrics.

Findings

High-fitness countries produce both specialized and low-specialization foods.

Low-fitness countries tend to produce a diverse but limited set of foods.

Patterns reveal a correlation between country fitness and food specialization levels.

Abstract

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