Mega-sports events have a profound impact on promoting the urbanization process, optimizing the urban spatial structure, and improving the competitiveness of the host city. Taking the 19th Asian Games Hangzhou 2022 (AGH) as an example, we used remote sensing data and a scenario-based model to simulate land-use changes and developments from 2005 to 2025. By setting two scenarios, natural development and AGH-driven development, we explored the impact of AGH on urban development and its driving factors. The results show that (1) cultivated land areas decreased by 369.96 km², while construction land areas increased by 488.33 km² among the main land-use types in Hangzhou from 2005 to 2020. Urban areas quickly expanded with the West Lake as the center. (2) Urban sprawl intensity under the AGH-driven scenario is expected to increase by 0.91% compared to in the natural-development scenario, indicating that hosting AGH would accelerate the expansion of urban land, particularly in districts set with sports venues such as Binjiang, Xiaoshan, and Yuhang. The strategic trend of supporting the Qiantang River is obvious. (3) Under the influence of AGH, the centroid of urban construction land shifted towards the southeast, and the spatial direction was remarkable. The construction of venues and supporting facilities, and construction land for public rail transit, are the main direct driving forces of urban expansion. The AGH enhances the pace of urbanization, significantly altering the urban spatial structure and helping the city achieve a major transition from the West Lake Era to the Qiantang River Era. Furthermore, our research can provide insights into other cities that will host mega-sports events.

Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R² was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R² (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products.

Urban growth and housing development can intensify congestion on road networks, but conventional congestion indicators mainly describe current operating conditions and provide limited insight into how congestion could intensify in response to future development of housing and industry. This paper develops a development-responsive framework for assessing capacity and congestion in passenger road transport networks under alternative development scenarios. The framework links process-based passenger-flow modelling with a multi-level hierarchy of indicators across road segment, network, and systems levels. It combines segment-level capacity utilisation and time loss with a novel Congestion Transition Range (CTR) indicator for classifying evolving uncongested, transitional, and congested operating regimes. Indicators are proposed for network-level accessibility loss and the elasticity of congestion changes relative to development growth. The framework is demonstrated for England (UK) by comparing a baseline scenario (2021) with a future high-growth scenario (2050). Results help to locate where future development substantially increases capacity stress and delays, with impacts expanding beyond existing metropolitan hotspots into surrounding commuter belts and strategic corridors. In England, the greatest pressures are concentrated in London and the wider Southeast, with road congestion around Cambridge, Birmingham, and the Oxford–Cambridge and Manchester–Sheffield corridors also being sensitive to additional economic growth. The priority-regime classification further distinguishes where congestion is already severe and where it is emerging, and helps to identify where intervention should focus on local assets, network operations, or strategic coordination between transport investment and land-use planning. The framework therefore provides a scenario-planning tool for spatial prioritization of transport interventions under future development scenarios.