Search

Megacities serve as crucial catalysts for national economic and social development, and Shanghai, one of China's most prominent metropolitan areas, exemplifies this transformative urbanization. To study Shanghai's urban expansion, we extracted urban land cover data from 1985 to 2020 using impervious area products and simulated urban expansion dynamics from 2021 to 2035 by employing the cellular automata model. Leveraging these data, we analyzed a 50-year period of urban expansion and investigated the drivers, including economic factors, population growth, and transportation infrastructure. Our findings indicate that the size of Shanghai's urban area in 2035 will be nearly 13 times that of 1985. Over these five decades, Shanghai's urban centroid shifted from the northeast to the southwest, with early urban expansion concentrated in the northeast and later expansion in the southwest. New urban patches primarily emerged at the edges of the initial urban area. As time progressed, areas with higher urban expansion intensity moved outward from the city center, mirroring the trend of urban expansion hotspots. Landscape indicators also demonstrated a trend of urban patches initially spreading and subsequently clustering. Overall, the development of Shanghai's metropolitan area exhibits substantial spatiotemporal heterogeneity. By integrating correlation analysis and generalized additive models, we quantified the impact of urban expansion drivers. The results show that economic and population factors had high correlation coefficients (over 0.97) with urban area, and proximity to the city center and road network greatly contributed to urban expansion. Our research amalgamates various theories and methods to analyze the spatiotemporal dynamics of urban expansion in metropolitan areas. This work provides a valuable data foundation to aid policymakers in designing effective metropolitan development policies. [ABSTRACT FROM AUTHOR]

Background: Community screening for SARS-CoV-2 Omicron variant plays a significant role in controlling the spread of infection. However, loopholes may exist in the current management of community screening in Shanghai, China. The objective of this study was to discover loopholes in the management of community screening for SARS-CoV-2 Omicron variant in Shanghai, China and provide targeted solutions. Methods: The cross-sectional study was carried out April 4 to April 30, 2021, among residential committee directors from the Putuo District, Pudong District, and Minhang District of Shanghai, China. Data were collected using a self-designed questionnaire about the management of nucleic acid testing (NAT) sampling in communities through the network platform powered by www.wjx.cn. Results: A total of 203 residential committee directors responded to the survey. Of them, 47.3% were not accepted training and 40.4% were not aware of cross-infection. Comparison among sampling sites and communities, high-risk group contained lower proportion of community training (P = 0.093~0.200), higher awareness of cross-infection (P = 0.039~0.777), more medical workers (P = 0.007~0.724) and more tests performed (P = 0.001~0.992). Larger communities had more medical workers, sampling sites, sampling tables (P = 0.000) and higher awareness of cross-infection (P = 0.009), but lower proportion of community training (P = 0.051). Conclusion: Overall, community training and awareness of infection control were inadequate. Government or institutions should organize the community training and raise the awareness of infection control. Significant differences exist in NAT management patterns between sampling sites, as well as communities of different sizes. Residential community directors minimize high-risk sampling point settings in the future. Special personnel designated by the government or institutions should tour to guide each sampling site. [ABSTRACT FROM AUTHOR]

Predicting future urban land-use scenarios and assessing their encroachment on ecological land is important for sustainability policy formulation. This study develops a new Futureland model using cellular automata with user-defined transformation rules, graphical neighborhood configuration and matrixed transformation cost to simulate multiple land-use changes. Compared with existing models, Futureland applies different factors to different land-use types in a single simulation experiment. Futureland was developed using Geospatial Data Abstraction Library and parallel computing, which significantly improve the implementation efficiency. The case study of Shanghai 2010–2020 illustrates an overall accuracy of 86.6% and a Kappa simulation of 0.79. The land-use scenarios for 2020–2035 were projected under greenspace planning constraints using Futureland. The results indicate that Shanghai's urban sprawl will gradually slow down by 2030, and the increased urban areas will be mainly in the urban fringes and suburban regions. Futureland can help decision-makers to manage future land-use and optimize urban planning policies. [ABSTRACT FROM AUTHOR]

• The scale behaviors of three indicators were examined in delineating expansion dynamics. • These indicators are the quantitative OUER metric and their variates GUER and BUER. • OUER and GUER yield polynomial scaling relations in delineating expansion dynamics. • BUER does not comply with any known functions in terms of the scaling relations. • 1800–2700 m was identified as the optimal scale range of urban expansion analysis. The delineation of spatiotemporal urban expansion highly depends on the selection of moving window size. Few studies examine the effects of moving window size and the scaling mechanism when analyzing urban expansion patterns. This study selected nine moving windows from 450 m to 4050 m with a scale (size) interval of 450 m to examine the urban expansion dynamics of Shanghai from 1995 to 2005, and from 2005 to 2015. Three urban expansion rate indicators were selected to explore the scale dependence of urban expansion, and these indicators include the overall urban expansion rate (OUER), the gridded urban expansion rate (GUER), and the boxed urban expansion rate (BUER). In addition, Global Moran's I was used to measure the spatial autocorrelation of urban expansion on different scales. The results show that the changing moving window size has varying impacts on delineating urban dynamics when using these three indicators. Specifically, OUER and GUER yield polynomial relationships with the moving window size, while BUER does not comply with any known functions in terms of the scaling relations. All three indicators reach their extreme points and/or inflection points at sizes of 1800 m, 2250 m and 2700 m, and the optimal size is speculated in the range of 1800–2700 m. This study should help to choose appropriate moving window sizes and suitable urban expansion indicators to more accurately capture the spatial differences in urban expansion dynamics and assist land-use planning. [ABSTRACT FROM AUTHOR]