The assurance of a consistent supply of daily necessities in megacities is pivotal in fortifying community supply resilience. It is axiomatic that a community system is not an insular entity; rather, it intricately intertwines with various elements of urban systems. As a foundational unit of urban governance, the urban community is instrumental in facilitating a congruent nexus between supply and demand, thereby augmenting urban resilience. This study proposes an exploratory evaluation method for the urban community supply support and resilience based on complex network theory, attempting to achieve a breakthrough in the underlying theoretical framework of resilience assessment from "single system assessment" to "multi-system correlation assessment". Taking the six districts in the central city of Guangzhou as an example, we build a supply-demand network based on citizens' spatio-temporal behaviors using multi-source data such as mobile phone signaling data and other data. The attacking strategies of network are based on five community resilience indicators. Besides, the cascade failure mechanism is introduced to evaluate the network resilience, and the entropy-weighted method is employed to obtain resilience evaluation results. The influence mechanism of community resilience on the supply system is further analyzed by studying the factors affecting community node failure at different stages of supply network. The findings are as follows: (1) The proposed evaluation model of the community supply support and resilience can effectively simulate urban community supply-demand networks and evaluate the resilience of communities. Low-resilience communities are mainly categorized into three spatial types: old blocks, urban villages, and suburban blocks; (2) Through the analysis of network resilience under five different attack strategies, it is found that the dominant influencing factors are different, with the population density being the primary factor; (3) There exists a complex bidirectional relationship between community resilience and supply security, including the obvious vulnerability of low-resilience communities. And the community self-organization ability, the supply facility layout, and the linkage scheduling between supply points all affect the overall community resilience.

The converting evolution of cascading disaster scenario refers to that in the process of disaster scenario evolution, the disaster bearing bodies transform into new disaster hazards, forming a disaster chain. Rainstorm can easily cause serious secondary disasters such as waterlogging, debris flow and flood, and the combination of these secondary disasters will make the city more vulnerable. However, existing research on rainstorm cascading scenario deduction lacks the analysis of specific scenario evolution situations such as multi disaster combination, scenario element converting, and human-induced emergencies. Meanwhile, traditional research often relies on the probability inference based on existing scenario evolution networks, without providing a construction method for scenario evolution networks, making it difficult to adapt to the knowledge requirements of actual scenario situation converting deduction. To address the scenario converting evolution problems of urban rainstorm cascading disasters, this paper proposes a scenario converting deduction method for rainstorm cascading disaster response based on multi-source spatial data and probability analysis tools. First, based on local and non-local historical emergency cases, the scenario elements involved in the rainstorm cascading disaster scenarios and their potential converting paths are identified. Next, with the support of Baidu Encyclopedia and Wikipedia network knowledge resources, relevant scenario element features and their associations are extracted, and a Group Lasso machine learning method is adopted to achieve feature selection of involved scenario elements. Then, considering the multi-stage and complex scenario correlation in the process of cascading scenario evolution, a dynamic Bayesian network model for scenario converting deduction is constructed. Finally, a Markov chain Monte Carlo method is used to solve the Bayesian network and generate the converting probabilities. The proposed method is applied to the rainstorm response practice of Wuhan High-tech Zone. The use case results show that the proposed method can combine historical cases and network data to achieve rapid and effective generation of key scenario elements and their features, helping to improve the reliability of scenario converting deduction. At the same time, the proposed method supports the scenario converting deduction of small-scale disaster-bearing bodies such as geographic grids, which helps to provide more accurate rainstorm emergency decision-making support and provide good performance in visual analysis. The uncertainty analysis of the proposed method shows that the precision of original probabilities of key scenario element features and the size of generated geographic grids significantly affect the scenario converting deduction results. These findings provide important information for the local area and are expected to help the rainstorm disaster management of other jurisdictions.

About 60% of all known causes of cancer are related to environmental pollution. Identifying the spatial co-location pattern of prevalent neighbor spatial feature sets in geographical space is important to explore the potential relationship between industrial outdoor air pollutants and cancer risk. The traditional spatial co-location pattern mining algorithms usually calculate the prevalence of co-locations based on the frequency of cancer instances when measuring pattern interest. However, the influence of pollution source on cancer instances is also dependent on their proximity. In addition, pollution sources are also influenced by factors such as meteorological conditions, concentration levels, and the degree of harm. So, the pattern interest cannot be measured by relying solely on the number of instance occurences. To address this issue, a new spatial co-location pattern (called spatial ordered-pair pattern) is defined, and a novel mining algorithm is proposed based on the Gaussian kernel density estimation model. The Gaussian kernel function can well capture the decay of the influence of pollution sources on cancer cases with distance. To better represent the real-world diffusion of pollution sources, a spatial neighbor relationship between pollution source and cancer is defined, which considers urban wind direction, wind speed, and pollution emission concentration. Furthermore, pollution sources are categorized into different carcinogenic groups, and a weighted differentiation method is employed to distinguish pollutants based on their carcinogenic categories. The influence of various pollutants on cancer is calculated by weighting their contributions by the "carcinogenic coefficient." Therefore, a novel metric of the influence of pollution sources on cancer along with corresponding mining algorithm is presented. It not only effectively measures the impact of distance between pollution sources and cancer instances on the prevalence patterns but also models the mechanism of the influence of pollution sources on cancer by incorporating real-world conditions, overcoming the limitations of the traditional methods. Furthermore, this study improves the robustness of the method by using a smoothing factor to mitigate mining anomalies caused by uneven distributions of cancer instances. Finally, the effectiveness and efficiency of the metric and the mining algorithm proposed in this study are tested through experiments on real and synthetic datasets, and insights are also provided for cancer prevention and urban planning for Yunnan Province. The experimental results indicate that both the influence degree and participation index can accurately reflect the pattern interest from both macroscopic and microscopic perspectives. Furthermore, the mining efficiency increases by an average of 60% compared to other algorithms. The proposed influence degree measurement can more effectively capture spatial co-location patterns and can better reflect the impact of pollution sources on the incidence of cancer.

Car-sharing services can meet the diverse travel needs of users while helping to alleviate traffic congestion and reduce pollution. In many scenarios, car-sharing is more economical than taxis. One-way car-sharing allows users to rent and return cars at any station within the system, which leads to low operating costs and flexible services. However, the spatiotemporal skewness of user travel demand gives rise to imbalances between vehicle supply and demand among stations, which limits the profitability of car-sharing companies. Relocating vehicles can alleviate the above problems to some extent. Most existing studies construct optimization models with the goal of maximizing expected revenue or reducing system imbalance. The former is limited by the insufficient accuracy of travel demand prediction, and the mode of discarding definite orders and pursuing higher possible expected benefits instead cannot guarantee actual profits. To improve system balance, the latter pays more relocation costs such that reduces the profitability. To this end, we propose a revenue-driven one-way car-sharing user relocation model RUG that is suitable for real-time scenarios. The model is based on the deterministic effect of prospect theory, which ensures the current definite gains. For orders that cannot be fulfilled due to imbalanced resources, RUG provides users with alternative travel routes, which not only attempts for promising gains but also effectively balances the system. Users are incorporated into the system as relocation subjects by designing rational user incentive and acceptance models. Public transportation is utilized to break through the distance limitations of user relocation. Relocation plans are evaluated with a greedy heuristic. Experimental results on real-world New York datasets show that the RUG model has significant advantages over existing user-based relocation methods. Under the same parameters, compared to the representative user-based relocation method, RUG increases service order volume and profit by 14% and 60%, respectively. Notably, RUG can effectively raise unit profits during traffic rush hours. By incorporating travel demand forecasting, the model further increases revenue by 5.4% while also improving user service level and system balance.