Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city’s central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R²) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future.

Road vulnerability is crucial for enhancing the robustness of urban road networks and urban resilience. In medium or large cities, road failures in the face of unexpected events, such as heavy rainfall, can affect regional traffic efficiency and operational stability, which can cause high economic losses in severe cases. Conventional studies of road cascading failures under unexpected events focus on dynamic traffic flow, but the significant drop in traffic flow caused by urban flooding does not accurately reflect road load changes. Meanwhile, limited studies analyze the spatiotemporal pattern of cascading failure of urban road networks under real rainstorms and the correlation of this pattern with road vulnerability. In this study, road vulnerability is calculated using a network’s global efficiency measures to identify locations of high and low road vulnerability. Using the between centrality as a measure of road load, the spatiotemporal patterns of road network cascading failure during a real rainstorm are analyzed. The spatial association between road network vulnerability and cascading failure is then investigated. It has been determined that 90.09% of the roads in Zhengzhou city have a vulnerability of less than one, indicating a substantial degree of spatial heterogeneity. The vulnerability of roads adjacent to the city ring roads and city center is often lower, which has a significant impact on the global network’s efficiency. In contrast, road vulnerability is greater in areas located on the urban periphery, which has little effect on the global network’s efficiency. Five hot spots and three cold spots of road vulnerability are identified by using spatial autocorrelation analysis. The cascading failure of a road network exhibits varied associational characteristics in distinct clusters of road vulnerability. Road cascading failure has a very minor influence on the network in hot spots but is more likely to cause widespread traffic congestion or disruption in cold spots. These findings can help stakeholders adopt more targeted policies and strategies in urban planning and disaster emergency management to build more resilient cities and promote sustainable urban development.

The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin’s ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin.

Semi-arid loess basins in the middle Yellow River have rapid runoff responses, short hydrological memory, and sparse gauging networks, so pooled metrics across all stations can conceal forecast failures in low-flow tributaries and during dry periods. This study proposes DaSTGODE, a data-augmented spatiotemporal graph ordinary differential equation model with a structural water balance constraint. DaSTGODE integrates directed advection–diffusion propagation, soil-moisture-limited evapotranspiration, and a recursive water balance within a unified framework, thereby closing the water budget at every forecast step through the model structure. A reference scenario with perfect meteorological forcing, DaSTGODE_pre, is also established. It supplies the decoder with meteorological conditions over the forecast window as future meteorological forcing and quantifies the potential contributions of future precipitation, air temperature, and potential evapotranspiration by channel. Using daily records from 10 stations in the Wuding River basin from 2006 to 2020, DaSTGODE is compared with long short-term memory (LSTM) and graph long short-term memory (GraphLSTM) models for 1-, 3-, 6-, and 12-day forecast windows. DaSTGODE increases pooled Nash–Sutcliffe efficiency (NSE) from 0.532 and 0.473 for the best baseline to 0.599 and 0.567 for the 6- and 12-day windows, respectively. Relative to the best baseline, DaSTGODE increases the 1-day station-macro logarithmic NSE (logNSE) from 0.266 to 0.612 and reduces the mean number of stations with a negative NSE in the 6-day window from 4.6 to 2.0. Under the reference scenario in which future meteorological conditions are known, DaSTGODE_pre increases the 6-day outlet NSE from 0.141 for DaSTGODE to 0.331, and future precipitation alone provides 94% of the gain obtained from all three meteorological channels. HBV virtual nodes yield only small improvements in metrics at gauged stations, but provide daily streamflow forecasts at the outlets of 16 ungauged subbasins. Taken together, these results show that, under the semi-arid and sparsely gauged conditions examined here, the complete physical graph ODE kernel mainly improves cross-station reliability and relative low-flow dynamics, while the perfect meteorological forcing experiment quantifies the contribution of meteorological information over the forecast period to accuracy at medium and long forecast windows and identifies the channels responsible for that contribution.