3. Global estimates of population dispersion and sparsity for more than 100 countries Estimates of the geographical scale of population dispersion and dispersion-adjusted population sparsity were calculated for each country with sufficiently detailed data (a minimum of five sub-national areas) in the World Bank’s subnational geography database.

Across the global database, the geographical scale of population dispersion ranges from a maximum of 3,600km in Russia, to a minimum of 40km in Trinidad and Tobago (Figure 5, Appendix E shows the numerical values and country rankings according to the new dispersion measures). None of the PICs lies at the extremes of this range, but dispersion for FSM and Kiribati is at the higher end of the distribution, while Fiji and Tonga are at the lower end. The scale of dispersion in Russia is considerably larger than every other country across the database, with the estimated average distance between every two Russians more than 3,600km. The three countries with the next highest dispersion levels are Brazil (1,300km), China (1,300km) and Kazakhstan (1,200km). Dispersion is smallest in the Caribbean countries of Trinidad and Tobago (40 km) and Jamaica (60 km).

After accounting for population size, the PICs stand out as having extremely low levels of dispersionadjusted population density compared to other countries (Figure 6), and equivalently, an extremely high degree of population sparsity (Figure 7). Figure 6 illustrates the cross-country distribution of dispersionadjusted population density. This measure ranges from a maximum of 1,200 people per square kilometer in Bangladesh, to a minimum of 0.05 people per square kilometer in FSM. The inverse of this density measure, the dispersion-adjusted population sparsity, is illustrated in Figure 7. With the exception of Fiji, the degree of population sparsity in the PICs completely dwarfs that recorded for other countries in the database (the first panel shows a complete scale on the vertical axis; the second panel reduces the scale to better reveal the variation across the non-PIC countries). The seven case study countries of FSM, Kiribati, Tuvalu, RMI, Vanuatu, Tonga and Solomon Islands are the seven countries with most extreme levels of population sparsity across the global database.

The pattern of population settlement in the PICs (which reflects the concentration of people on discrete islands) goes some way to reducing the impact of population sparsity. Figure 8 presents a cross-country scatter plot illustrating the correlation between a country’s territorial population sparsity (the ratio of territorial area to population), and the dispersion-adjusted sparsity measure (the ratio of the dispersionadjusted area-equivalent to population). The PICs stand out as having extreme levels of sparsity by both measures. Most countries lie below the diagonal line, which implies that the lived experience of population sparsity (i.e. after accounting for settlement patterns) is lower than implied by the simple ratio of territorial area to the population. The PICs lie noticeably far below the line. This is illuminated further in Figure 9, which shows the extent to which sparsity would be overestimated by looking solely at a country’s territorial area, rather than adjusting for actual population settlement patterns. In Fiji, territorial sparsity is 25 times larger than the actual dispersion-adjusted sparsity estimate. Many of the island nations (the PICs and Caribbean countries) with large areas of ocean territory, but populations relatively concentrated on islands, are similarly at the upper end of this distribution.

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