With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

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The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and we finally identified six main ridership patterns. We then developed a newly built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis results revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type stations, and centrality was only associated with employment-oriented hybrid type station. The research results could inform the spatial planning and design around metro stations and the planning and design of metro systems. The built environment of pedestrian catchment areas surrounding metro stations can be enhanced through rational land use planning and the appropriate allocation of urban infrastructure and public service facilities.

The emotional health of urban residents is increasingly threatened by high temperatures due to global heating. However, how high temperature affects residents’ emotional health remains unknown. Therefore, this study investigated the spatiotemporal pattern of temperature’s impact on residents’ irritability using data from summer high-temperature measurement and emotional health survey in Beijing, combined with remote sensing images and statistical yearbooks. In detail, this study formulated a multiscale geographically weighted regression (MGWR) model, to study the differentiated and spatial influence of high-temperature factors on emotion. Results show: From 09:00 to 20:00, irritability level rose first then gradually dropped, with a pattern of “aggregation-fragmentation-aggregation.” Irritability is very sensitive to intercept and building density (BD). Other variables all have spatial heterogeneity [except for fraction vegetation coverage (FVC) or road network density (RND) as they are global variables], including normalized difference vegetation index (NDVI), water surface rate (WSR), floor area ratio (FAR), and Modified Normalized Difference Water Index (MNDWI) (sorted from the smallest to the largest in scale). Irritability is negatively correlated with NDVI, WSR, and RND, while positively correlated with intercept, MNDWI, FVC, FAR, and BD. Influence on irritability: WSR < NDVI < BD < MNDWI < RND < intercept < FVC < FAR.

Continuous global warming and frequent extreme high temperatures keep the urban climate health risk increasing, seriously threatening residents’ emotional health. Therefore, analysis on spatial distribution of the health risk that the urban heat island (UHI) effect imposes on emotional health as well as basic research on the characteristics of vulnerable populations need to be conducted. This study, with Tianjin city as the case, analyzed data from Landsat remote-sensing images, meteorological stations, and digital maps, explored the influence of summer UHI effect on distress (a typical negative emotion factor) and its spatiotemporal evolution, and conducted difference analysis on the age groups, genders, family state, and distress levels of vulnerable populations. The results show: (1) During the period of 1992–2020, the level and area of UHI influence on residents’ distress drastically increased–influence level elevated from level 2–4 to level 4–7, and highlevel influence areas were concentrated in six districts of central Tianjin. (2) Influence of the UHI effect on distress varied in different age groups–generally dropping with fluctuations as residents got older, especially residents aged 50–59. (3) Men experienced a W-shaped pattern in distress and were more irritable and unsteady emotionally; while women were more sensitive to distress in the beginning, but they became more placid as temperature got higher. (4) Studies on family status show that couples living together showed sound heat resistance in the face of heat stress, while middle-aged and elderly people living alone or with children were relatively weak in adjusting to high ambient temperature.