Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?

Abstract

a b s t r a c tUrban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972e1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

“…As reported in many previous studies, spatiotemporal urban land cover change is a serious concern both in megacities, such as Beijing [8,9], Shanghai [10][11][12], Hangzhou [13,14], and Guangzhou [15,16], and in developed coastal urban agglomerations, especially the three largest urban agglomerations, i.e., the Yangtze River Delta (YRD) [17,18], the Pearl River Delta (PRD) [19,20] and the Jing-Jin-Ji (JJJ) [21][22][23]. However, urban expansion in less developed cities and urban agglomerations has received little attention even though rapid urbanization has also occurred in these areas.…”

A System Analysis on Steppe Sustainability and Its Driving Forces—A Case Study in China

“…By comparing the results in this study to previous studies, we can find that anthropogenic driving factors on steppe sustainability dynamics have been changing during the past few decades [14]. Rapid urban expansion led to great loss of ecosystem service values and agricultural land before 2003 [11][12][13][14][15]103].…”

“…However, population growth and economic development during the past few decades [10][11][12], rapid urbanization [13][14][15], excessive deforestation [16,17], massive land reclamation [6,9] and overgrazing [3,7] have made substantial changes to steppe landscapes and ecosystem functioning. These human disturbances, coupled with poor natural conditions, have left to approximately 49.25% of steppe land worldwide suffering from degradation, with nearly 5% of these steppes suffering from serious degradation [18].…”

Characterizing Factors Associated with Built-Up Land Expansion in Urban and Non-Urban Areas from a Morphological Perspective

“…Social-economic indices such as GDP [37,38] and population [39] have been proven to be effective indicators of built-up land expansion. Natural factors such as elevation [40,41] and slope [41][42][43] are other potential factors which have been reported in the previous studies. Subsequently, a three-round Delphi Process and principal component analysis were used to eliminate the indices with high correlation by rotating the multidimensional indices into a new group of mutually orthogonal variables [44].…”