4.7 Article

Spatiotemporal evolution of urban agglomerations in China during 2000-2012: a nighttime light approach

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LANDSCAPE ECOLOGY
卷 35, 期 2, 页码 421-434

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SPRINGER
DOI: 10.1007/s10980-019-00956-y

关键词

Urban agglomeration; Nighttime light data; Gravity model; Spatiotemporal evolution; China

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Context Urban agglomeration is an advanced spatial organization of cities, usually caused by urbanization processes when cities develop to a certain level - typically associated with higher population density and a certain density of built environment. However, compared with various studies focusing on specific cities, urban agglomerations are still understudied, especially for the quantitative identification of spatiotemporal evolution of urban agglomerations. Objectives This study aims to identify the boundary of urban agglomerations in China from 2000 to 2012, and to explore the temporal evolution and spatial difference of urban agglomerations. Methods Firstly, the core zone of urban agglomerations was identified using an appropriate threshold of the digital number (DN) of nighttime light. Secondly, the mean patch area and gravity model were used to determine the affected zone of urban agglomerations. Thirdly, spatiotemporal contrast was conducted focusing on the 23 main urban agglomerations in China. Results By 2012, the most highly developed Yangtze River Delta and Pearl River Delta urban agglomerations met the standard of world level, with the Beijing-Tianjin-Hebei urban agglomeration for regional level, as well as 11 urban agglomerations for sub-regional level. Regional differences in urban agglomerations between southern and northern China, or between coastal and inland China remained stable over the study period of 2000-2012. Compared with the western urban agglomerations, the outward expansion of eastern urban agglomerations decelerated. From 2000 to 2012, the overall development mode of urban agglomerations shifted from the core-expansion to the peripheral-development, together with slower expansion of urban agglomerations after 2006. Conclusions Nighttime light data are effective in exploring the spatiotemporal evolution of urban agglomerations.

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