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• This paper takes Beijing as a case to study regional economic resilience. • We systematically explore the effects of COVID-19 on industrial dynamics. • Individual-owned firms located in high-risk regions are more vulnerable. • The epidemic shock leads to cascading effects among the local industrial network. The COVID-19 epidemic has altered the economic geographies of cities. However, the academic focus on urban industries and their structural dynamics is far from enough. In this paper, we integrate literature from economic geography, business studies and network sciences, and design an adaptation framework to theorize urban economic resilience. Taking the COVID-19 outbreak in Xinfadi, Beijing during the summer of 2020 as a case study, we use Logit regressions and DID estimations to identify the impacts of the epidemic on local industrial dynamics. Our results support that the COVID-19 outbreak significantly increases the number of business exits. The negative impacts are concentrated in high-risk regions, pointing to specific industries and individual-owned firms. Furthermore, we use a machine learning method to visualize the dynamics of industry networks in a small-scale urban area, and then simulate the transmission paths of industry-failure. In doing so, we contribute to current economic resilience literature that firm-level adaptation and industrial dynamics complement regional adaptation theory, while the cascading effects in the industry network underpin the micro foundations of resilience formation. [ABSTRACT FROM AUTHOR]

• We investigate driving forces of different land-use types to BSD. • Diversified machine learnings are proposed for spatial interpolation fusion. • Hotspot detection establishes sampling points for reducing the MAUP effect. • Fusion models improve the interpolation performance: accuracy and stability. • Word clouds identifies predominant facility names in each land-use type. Land use plays a crucial role in promoting the bike-sharing demand. Traditionally, studies on bike-sharing demand (BSD) are mainly focused on its prediction through regression methods, but the influence of MAUP (modifiable areal unit problem) in modeling is ignored. This paper aims to model spatial BSD distribution and prove the driving forces of different land use types to BSD through a machine-learning-based multiple interpolation fusion method. The hotspot detection model is employed to establish sample points covering different land use types in urban areas. In order to capture the differences in adaptations among different urban regions and for different data sizes, six machine learning methods are applied and evaluated to improve BSD estimation by fusing five spatial interpolation algorithms, including Inverse Distance Weight, Spline, Kriging, Natural Neighborhood and Trend. The methodological verification of Beijing City shows that the fusion models improve the estimation performance compared with individual interpolation algorithms, and that GRNN (generalized regression neural network) method is superior to all the others. According to fitting results of all POIs based on the GRNN fusion model, we identify which types of facilities correspond to customers that will have a stronger preference for bike-sharing and demonstrate which facility names are more prominent in each land use type. The conclusions presented here enrich our understanding relationships between land-use and BSD, which provide a valuable foundation for the bike-sharing development. Compared with implementing regression in an analysis zone or a square grid, troubles caused by the MAUP are effectively solved through this method. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]