Measurement of Urban Expansion and Spatial Correlation of Central Yunnan Urban Agglomeration Using Nighttime Light Data

Abstract

The Central Yunnan Urban Agglomeration (CYUA) is an important zone of western development in China. The clarification of the spatial structure and changing trends in CYUA could help promote the coordinated development of the CYUA and enhance the overall competitiveness of the region. Based on data from the Yunnan Statistical Yearbook and the nighttime light data, this paper extracts the urban built-up area of the CYUA and analyzes the urban expansion and urban spatial connection intensity of the CYUA from 2000 to 2018 by using the urban gravity center model and the gravity model. The results show the following: (1) From 2000 to 2018, the urban built-up area of the CYUA expanded rapidly, and the urban built-up area increased by 369.35%, with Kunming accounting for 45.41% of the increased area. Kunming was the main contributor to the increase in the urban built-up area in the CYUA. From 2000 to 2018, the urban built-up areas of the CYUA were scattered in various mountain basins. (2) Overall, the urban gravity center of the CYUA has moved to Kunming, and the distance of the urban gravity center has increased since 2005, indicating that urban expansion has accelerated since 2005. (3) The development of the CYUA is extremely unbalanced. The urban spatial connection intensity between Kunming city, Yuxi city, and Qujing city, and Yi Autonomous Prefecture of Chuxiong is relatively strong, while the urban spatial connection intensity among cities other than Kunming is weak. Overall, the CYUA is characterized by stellar radiation with Kunming city as the core and Yuxi city as the secondary core.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

“…Remote sensing data from nighttime light (NTL) emissions provide unique and direct perspectives for investigating some human socioeconomic activities and can quantitatively characterize the intensity of human activities by detecting urban nighttime illumination, even low-intensity artificial nocturnal lighting arising from small-scale communities and fishery activities. As a definitive proxy of the dynamics of human activities, NTL data have been widely applied in diverse socioeconomic fields, such as urban sprawl [3][4][5], population estimation [6,7], electricity consumption [8][9][10], carbon emission [11,12], poverty evaluation [13][14][15][16], and disaster relief and alleviations [17][18][19]. After the outbreak of COVID-19, NTL data served as an effective indicator for investigating human responses to the pandemic because of its extensive coverage, objectivity, and high temporal resolution.…”