Abstract. The accurate estimation of population living in the low-elevation coastal zone (LECZ) – and at heightened risk from sea level rise – is critically important for policymakers and risk managers worldwide. This characterization of potential exposure depends on robust representations not only of coastal elevation and spatial population data but also of settlements along the urban–rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world's population – and an even greater share of the urban population – lived in the LECZ (McGranahan et al., 2007a). Those estimates were constrained in several ways, not only most notably by a single 10 m LECZ but also by a dichotomous urban–rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million and nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g., the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population and urban-proxy data sources in order to guide future research and improvements to characterizing risk in low-elevation coastal zones (https://doi.org/10.7927/d1x1-d702, CIESIN and CIDR, 2021).

India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization.

While the population of the United States has been predominantly urban for nearly 100 years, periodic transformations of the concepts and measures that define urban places and population have taken place, complicating over-time comparisons. We compare and combine data series of officially-designated urban areas, 1990–2010, at the census block-level within Metropolitan Statistical Areas (MSAs) with a satellite-derived consistent series on built-up area from the Global Human Settlement Layer to create urban classes that characterize urban structure and provide estimates of land and population. We find considerable heterogeneity in urban form across MSAs, even among those of similar population size, indicating the inherent difficulties in urban definitions. Over time, we observe slightly declining population densities and increasing land and population in areas captured only by census definitions or low built-up densities, constrained by the geography of place. Nevertheless, deriving urban proxies from satellite-derived built-up areas is promising for future efforts to create spatio-temporally consistent measures for urban land to guide urban demographic change analysis.

Abstract. The accurate estimation of population living in the Low Elevation Coastal Zone (LECZ), and at heightened risk from sea level rise, is critically important for policy makers and risk managers worldwide. This characterization of potential exposure depends not only on robust representations of coastal elevation and spatial population data, but also of settlements along the urban-rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world’s population, and an even greater share of the urban population, lived in the LECZ (McGranahan et al., 2007). Those estimates were constrained in several ways, most notably by a single 10-meter LECZ, but also by a dichotomous urban-rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million to nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g. the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers, and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population, and urban proxy data sources in order to guide future research and improvements to characterizing risk in low elevation coastal zones. DOI: assigned upon completion of data peer-review.