# Concluding remarks

This paper contributes a new approach to empirically measuring local area-level remoteness, and countrywide population dispersion and settlement sparsity. We apply this approach to study eight case-study PICs, in an attempt to better characterize their spatial geography. The new indicators reveal significant variation in the geographical remoteness of individual communities within each case-study PIC, and the country-level geographical scale of population dispersion across the PICs. Relative to other countries across the world, all case-study PICs display an extreme degree of sparsity in the pattern of population settlements, partly reflecting the countries’ small populations.

The benchmark approach we have explored in this paper can also be extended to consider spatial geographic characteristics from slightly varied perspectives, and we offer two such extensions in Appendices to this paper.

1. Our benchmark approach conceptualizes remoteness (and relatedly dispersion and sparsity) within a country context. However, it is also possible to apply the same approach to sub-national regions; for example, to examine population dispersion within a provincial context. In Appendix B, we conceptualize remoteness and dispersion relative to a nation’s capital. For each case-study PIC, we calculate alternative indicators of remoteness as communities’ distance from the national capital—either directly, or via local regional hubs. The associated dispersion indicators follow, as the population-weighted average community-level remoteness. These alternative measures of remoteness (and relatedly dispersion) are less sensitive to changes in settlement patterns, but potentially more transparent.

2. The remoteness, and related dispersion and sparsity measures presented in this paper are somewhat abstract in that they do not explicitly reflect remoteness from (or dispersion around) any particular center. In an extension of this work, we show that an analogous mathematical approach can be applied to derive more concrete indicators. In Appendix C, we conceptualize remoteness and dispersion relative to a nation’s capital. For each case-study PIC, we calculate alternative indicators of remoteness as communities’ distance from the national capital—either directly, or via local regional hubs. The associated dispersion indicators follow, as the populationweighted average community-level remoteness. These alternative measures of remoteness (and relatedly dispersion) are less sensitive to changes in settlement patterns, but potentially more transparent.

The benchmark remoteness, dispersion and sparsity indicators we focus on in this paper have many appealing intuitive characteristics. In particular, they are highly sensitive to variation in population settlement patterns. Variation in these measures across different geographical settings broadly aligns with intuition around what it means for a community to be remote in a country-context, or for a population to be highly dispersed. For example, a greater level of dispersion will be recorded in a setting with less population clustering, e.g. where two inhabited islands are further apart, all else being equal.

Another appealing characteristic of our new measures is that there is a natural link between the microlevel geographic indicator (local-area remoteness) and macro measures of population dispersion and 26