Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting flows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the influence of nearby regions. To address these issues, we propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction. Specifically, we first construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. To enhance the effectiveness of the embedding representation, a multitask learning framework is used to introduce stronger restrictions, forcing the embeddings to encapsulate effective representation for flow prediction. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting flows. We evaluate our model using real-world dataset from New York City and the experimental results demonstrate the effectiveness of our proposed method against the state of the art.

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

The continuous worsening of urban thermal environments poses a severe threat to human health and is among the main problems associated with urban climate change and sustainable development. This issue is particularly severe in high-density built-up areas. Existing studies on the thermal environments (temperature data extracted from satellite remote sensing images) are mainly focused on urban canopy areas (airspace below the average height of trees or buildings) rather than the near surface region (at pedestrian height). However, the main outdoor activity space of urban residents is the area near surface region. Hence, this study aims to investigate the influence of urban form (i.e., building density, height, and openness) on thermal environment near the surface region. The high-density built-up areas of a typical megacity (i.e., Nanjing) in China were selected, and the thermal environments of 26 typical blocks were simulated using ENVI-met software. Temperature field measurements were carried out for simulation validation. On this basis, a classified and comparative study was conducted by selecting the key spatial form elements that affect thermal environments. The results showed that in actual high-density built-up areas, single urban form parameter does not determine the thermal environments near the urban surface but mainly affected by the use (function) of space. For this study, the overall thermal environment of a street block is optimal when the building density is between 40% and 50% and the average building height is between 8 and 17 stories. Nonetheless, the urban form can be improved to optimize the overall effects on building functions and thermal environments. Furthermore, function-specific urban form optimization strategies were proposed to optimize thermal environments according to specific functional needs.

The spatial distribution of public hospitals is a crucial factor in patient access and thus important to health equity, especially amid the COVID-19 pandemic crisis. In China, a major issue is to improve accessibility to public health care in the face of the limited space for further growth of cities. Therefore, this study assesses the current spatial accessibility patterns to public hospitals in Shanghai, using point density analysis and the comprehensively improved gravity model. The results identify the challenges of inadequate and imbalanced spatial distribution of public hospitals at the district level and show that the central urban area has more high-quality public health care resources and also has higher accessibility levels. Based on the comparisons in each district, the study also summarizes four policy implications for future public hospital and public health care system optimization in Shanghai, which can also be leveraged for the planning of public hospitals in other cities or other public service facilities. The first is to deploy public hospitals in areas with a large population and to introduce branches of top-level hospitals to noncentral areas. The second is the full utilization of community health service centers to relieve the burden of public hospitals to improve accessibility.

Urban energy simulation can provide valuable information to urban planning, urban energy management, and urban emission reduction. Therefore, urban energy simulation has become an active research discipline. Various urban energy simulation methods and techniques have been developed and applied to cities on different scales. A review is conducted to categorize these methods and techniques and to analyze their pros and cons. Several representative methods and techniques are compared for their data inputs, suitable scales, accuracy, and computing speeds. Hangzhou South Railway Station area, which contains 522 buildings, is used as the case to evaluate the effectiveness and challenges of different urban energy simulation methods.