The urban construction land change is the most obvious and complex spatial phenomenon in urban agglomerations which has attracted extensive attention of scholars in different fields. Yangtze River Delta Urban Agglomeration is the most mature urban agglomeration in China, a typical representative in both China and the world. This paper analyzes the evolution dynamic, effect and governance policy of urban construction land in Yangtze River Delta Urban Agglomeration 2011–2020 using a combination of BCG model, decoupling model and GIS tools. The findings are as follows. (1) There are large intercity differences in urban construction land in urban agglomerations, but the spatial heterogeneity is gradually decreasing. (2) The change trends and evolution patterns of urban construction land in urban agglomerations are increasingly diversified, with emergence of a variety of types such as rapid growth, slow growth, inverted U-shape, stars, cows, question and dogs. (3) The population growth, economic development and income improvement corresponding to the change of urban construction land in urban agglomerations have no desirable effect, with most cities in the expansive negative decoupling state. (4) The decoupling types show increasingly complex changes, in evolution, degeneration and unchanged states. Affected by economic transformation and the outbreak of COVID-19, an increasing number of cities are in strong negative decoupling and degeneration states, threatening the sustainable development of urban agglomerations. (5) Based on the division of urban agglomerations into three policy areas of Transformation Leading, Land Dependent, and Land Reduction, the response strategies for each are proposed, and a differentiated land use zoning management system is established.

Within globalization, the significance of urban innovation cooperation has become increasingly evident. However, urban innovation cooperation faces challenges due to various factors—social, economic, and spatial—making it difficult for traditional methods to uncover the intricate nonlinear relationships among them. Consequently, this research concentrates on cities within the Yangtze River Delta region, employing an explainable machine learning model that integrates eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDPs) to investigate the nonlinear and interactive effects of multidimensional factors impacting urban innovation cooperation. The findings indicate that XGBoost outperforms LR, SVR, RF, and GBDT in terms of accuracy and effectiveness. Key results are summarized as follows: (1) Urban innovation cooperation exhibits different phased characteristics. (2) There exist nonlinear and interactive effects between urban innovation cooperation and multidimensional factors, among them, the Scientific and Technological dimension contributes the most (30.59%) and has the most significant positive promoting effect in the later stage after surpassing a certain threshold. In the Social and Economic dimension (23.61%), the number of Internet Users (IU) contributes the most individually. The Physical Space dimension (20.46%) generally exhibits mutation points during the early stages of urban development, with overall relationships predominantly characterized by nonlinear positive trends. (3) Through the application of PDP, it is further determined that IU has a positive synergistic effect with per capita Foreign Direct Investment (FDI), public library collections per capita (LC), and city night light data (NPP), while exhibiting a negative antagonistic effect with Average Annual Wage of Staff (AAS) and number of Enterprises above Designated Size in Industry (EDS). (4) For cities at different developmental stages, tailored development proposals should be formulated based on single-factor contribution and multifactor interaction effects. These insights enhance our understanding of urban innovation cooperation and elucidate the nonlinear and interactive effects of multidimensional influencing factors.

Based on multi-source data including mobile signaling data and remote sensing imagery, this research constructed human activity indicators and applied spatial analysis methods to identify gender-specific patterns in green space usage across different types of days. A Standardized Gender Difference Index (SDI) was developed to quantify gender-based disparities in both temporal and spatial dimensions. Correlation analysis was employed to examine spatial influencing factors and their interrelationships. The results indicate that on holidays, a stable male-dominant usage pattern was observed across all parks, with males showing a preference for cultural parks. On weekdays, although male users remain predominant overall, considerable variations exist among parks. On a daily scale, males exhibit earlier and more flexible activity patterns, whereas females demonstrate significantly lower presence during evening hours, indicating a “nocturnal poverty” phenomenon. Spatially, community parks display the greatest gender disparity, whereas comprehensive parks show relative gender balance. Although both genders value barrier-free facilities, females place additional emphasis on service facilities, while males are more influenced by mixed land-use functions surrounding the parks. This study demonstrates that high-precision, large-coverage multi-source data can accurately and dynamically identify gender-based differences in green space usage and their influencing factors, providing valuable insights for inclusive urban planning.