Consistent yet adaptive global geospatial identification of urban-rural patterns: The iURBAN model

REMOTE SENSING OF ENVIRONMENT

卷:187

Article

Aubrecht, Christoph²  Gunasekera, Rashmin¹  Ungar, Joachim¹  Ishizawa, Oscar¹ 

[1] World Bank Social Urban Rural & Resilience GSURR, 18501 1 St NW, Washington, DC 20006 USA

[2] World Bank, European Space Agcy ESA ESRIN, 1818 H St NW, Washington, DC 20433 USA

关键词: Urban-rural;    High-resolution built-up area data;    Population reallocation;    Disaggregation;    Global scale;    Geospatial modeling;    Adaptive;   

DOI  :  10.1016/j.rse.2016.10.031

来源: Elsevier

【 摘 要 】

The main motivation of this paper is to shed new light on the problem of spatial identification of urban and rural areas globally, and to provide a compatible disaggregation framework for linking associated country-specific, non-spatial data compilations, such as building type inventories. Existing homogeneously set-up global urban extent models commonly ignore local-level specifics. While global consistency and regional comparability of urban characteristics are much strived-for goals in the global development and remote sensing communities, non-conformity at the national level often renders such models inapplicable for effective decision-making. Furthermore, the focus on identifying 'urban' leads to an ill-defined 'rural', which is simply defined by contrast as 'everything else'; a questionable definition when referring to strongly spatially localized residential patterns. In this paper we introduce the novel TURBAN geospatial modeling approach, identifying Urban-Rural patterns in Built-up-Adjusted and Nationally-adaptive manner. The model operates at global scale, but at the same time conforms to country specifics. In this model, high-resolution, satellite-derived, built-up data is used to consistently detect global human settlements at unprecedented spatial detail. In combination with global gridded population data, and with reference to national level statistical information on urban population ratios globally compiled in the annually -released UN World Urbanization Prospects, TURBAN identifies matching urban extents. Additionally, a novel reallocation algorithm is introduced which addresses the poor representation of rural areas that is inherent in existing global population grids. Associating all of the population with inhabitable, built-up area and conforming to national urban-rural ratios, iURBAN sets a new standard by enabling careful consideration of both urban and rural as opposed to traditional urban-biased approaches. (C) 2016 Published by Elsevier Inc.

【 授权许可】

Free