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A global analysis of human settlement in coastal zones

A global analysis of human settlement in coastal zones
A global analysis of human settlement in coastal zones
Recent improvements in mapping of global population distribution makes it possible to estimate the number and distribution of people near coasts with greater accuracy than previously possible, and hence consider the potential exposure of these populations to coastal hazards. In this paper, we combine the updated Gridded Population of the World (GPW2) population distribution estimate for 1990 and lighted settlement imagery with a global digital elevation model (DEM) and a high resolution vector coastline. This produces bivariate distributions of population, lighted settlements and land area as functions of elevation and coastal proximity. The near-coastal population within 100 km of a shoreline and 100 m of sea level was estimated as 1.2 X 10(9) people with average densities nearly 3 times higher than the global average density. Within the near coastal-zone, the average population density diminishes more rapidly with elevation than with distance, while the opposite is true of lighted settlements. Lighted settlements are concentrated within 5 km of coastlines worldwide, whereas average population densities are higher at elevations below 20 m throughout the 100 km width of the near-coastal zone. Presently most of the near-coastal population live in relatively densely-populated rural areas and small to medium cities, rather than in large cities. A range of improvements are required to define a better baseline and scenarios for policy analysis. Improving the resolution of the underlying population data is a priority.
coastal population, coastal hazards, exposure, vulnerability
0749-0208
584-599
Small, Christopher
ba36bac1-97f4-4064-9f69-95e05a153978
Nicholls, Robert J.
4ce1e355-cc5d-4702-8124-820932c57076
Small, Christopher
ba36bac1-97f4-4064-9f69-95e05a153978
Nicholls, Robert J.
4ce1e355-cc5d-4702-8124-820932c57076

Small, Christopher and Nicholls, Robert J. (2003) A global analysis of human settlement in coastal zones. Journal of Coastal Research, 19 (3), 584-599.

Record type: Article

Abstract

Recent improvements in mapping of global population distribution makes it possible to estimate the number and distribution of people near coasts with greater accuracy than previously possible, and hence consider the potential exposure of these populations to coastal hazards. In this paper, we combine the updated Gridded Population of the World (GPW2) population distribution estimate for 1990 and lighted settlement imagery with a global digital elevation model (DEM) and a high resolution vector coastline. This produces bivariate distributions of population, lighted settlements and land area as functions of elevation and coastal proximity. The near-coastal population within 100 km of a shoreline and 100 m of sea level was estimated as 1.2 X 10(9) people with average densities nearly 3 times higher than the global average density. Within the near coastal-zone, the average population density diminishes more rapidly with elevation than with distance, while the opposite is true of lighted settlements. Lighted settlements are concentrated within 5 km of coastlines worldwide, whereas average population densities are higher at elevations below 20 m throughout the 100 km width of the near-coastal zone. Presently most of the near-coastal population live in relatively densely-populated rural areas and small to medium cities, rather than in large cities. A range of improvements are required to define a better baseline and scenarios for policy analysis. Improving the resolution of the underlying population data is a priority.

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Published date: 2003
Keywords: coastal population, coastal hazards, exposure, vulnerability

Identifiers

Local EPrints ID: 39496
URI: http://eprints.soton.ac.uk/id/eprint/39496
ISSN: 0749-0208
PURE UUID: 4bc391d9-ecc7-4fbd-b90f-613c3b186501
ORCID for Robert J. Nicholls: ORCID iD orcid.org/0000-0002-9715-1109

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Date deposited: 28 Jun 2006
Last modified: 16 Mar 2024 03:36

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Author: Christopher Small

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