The Multidimensional Measurement Method of Urban Sprawl and Its Empirical Analysis in Shanghai Metropolitan Area

Abstract

Urban sprawl concerns the high-quality and sustainable development of large cities. Due to the ambiguous definition, diversity of measurement indices and complexity of the driving mechanism of urban sprawl, the research results are rich but controversial. How does one carry out multidimensional measurement of urban sprawl? How does one reveal the spatio-temporal evolution characteristics of urban sprawl dynamically? First, according to the three common characteristics of urban sprawl (discontinuity of land use, low population density and inefficiency of land use), we, respectively, measure the urban sprawl of Shanghai metropolitan area by single index and comprehensive indices based on multi-source geospatial data. Next, using geographic information system (GIS) method, the temporal and spatial characteristics of urban sprawl in Shanghai are quantitatively and dynamically analyzed. The results show that (1) land use continuity reveals that fringe expansion is the main mode of urban sprawl, population density exhibits an upwards trend, and land use benefit shows that the sprawl increased first, then decreased and increased again, i.e., “N” type trend. The results of the above three comprehensive superpositions indicate that the urban sprawl in Shanghai changed from severe in 1995 to mild in 2010 and in 2020. (2) From 1990 to 2020, urban sprawl in Shanghai showed a trend of decreasing first, then increasing and decreasing again, which is consistent with an evolutionary trend of newly increased construction land. The larger the sprawl area was, the lower the land use efficiency of the sprawl area was. (3) The main directions of urban sprawl were southeast and southwest, and Songjiang District and Pudong New Area were the main sprawl areas. The peak value of urban sprawl mainly occurred at 20–30 km and was located in the area between the outer ring and the suburban ring. (4) Through time series analysis, we found that the effective supply of housing significantly affected the intensity and scale of urban sprawl but not the speed of urban sprawl in Shanghai metropolitan area. These findings are helpful to reasonably evaluate the real picture of urban sprawl in Shanghai metropolitan areas and provide reference for the formulation of urban sprawl governance policies.

Nighttime Lights and Urban Expansion: Illuminating the Correlation between Built-Up Areas of Lagos City and Changes in Climate Parameters

“…The DMSP-OLS dataset covers 1992 to 2013 and offers nighttime light data at a spatial resolution of 1 km by 1 km. In contrast, the NPP-VIRS dataset spans from 2012 to 2020, offering higher spatial resolution data at 500 m by 500 m [22]. These datasets capture the artificial illumination at night, allowing for the assessment of urbanization trends, economic activity, and population distribution across regions.…”

“…This includes mapping how cities grow and creating models that show economic and environmental factors like Gross Domestic Product (GDP) and electricity use. Studies have been conducted to uncover the impacts of human activities and light pollution on both ecosystems and human health [19,22]. Furthermore, using freely available datasets, which offer a detailed view at a 30 m resolution, researchers have discovered that they provide more accurate information about land use patterns [18].…”

Delineating and Characterizing the Metropolitan Fringe Area of Shanghai—A Spatial Morphology Perspective

“…For example, Li et al developed a fringe extraction model of small-and medium-sized urban areas by using a set of data sources including high-resolution imagery from the GF-2 satellite, the WorldPop dataset, and POI [17]. Shi et al stress that the metropolitan fringe area should be identified from a multi-dimensional perspective and propose an identification index system including indicators such as discontinuous land use, increasing population density, and fluctuating land use efficiency [18]. In addition to the increasing diversity of datasets that have been used to delineate the metropolitan fringe area, some studies have developed more complex mathematical methods by using machine learningrelated algorithms.…”

“…More recently, metropolitan fringe areas have been delineated in a more quantitative and non-subjective way. Within this line of the literature, existing studies have drawn upon a variety of datasets such as remote sensing images [11,12], nighttime lights [13][14][15], points of interest (POI) [16], or a combination of some of these data sources [17][18][19][20]. Importantly, no matter which types of data have been used to delineate the metropolitan fringe area, the rationales behind these quantitative delineation methods are generally similar.…”

Spatio-Temporal Evolution of Urban Expansion along Suburban Railway Lines in Megacities Based on Multi-Source Data: A Case Study of Beijing, China

“…Global experience has shown that growing megacities also often experience significant changes in their functional and spatial patterns [73], especially in China's megacities [74]. It has been demonstrated that urban expansion increases the demand for transport to the urban fringe, while improved transport conditions will in turn affect the city by enhancing the accessibility of the urban fringe, which in turn triggers the evolution of the land-use structure and promotes faster outward development of the city [75].…”