Urban morphology quantitatively expresses a city’s spatial structure, internal relationships, and physical form. It has advantages for predicting urban growth and analyzing the current state of cities in the literature. A comprehensive study on the complex relationships between urban morphology and urban heat island intensity (UHII) is of great importance for mitigating the urban heat island (UHI) effect for megacities. This study models urban morphological indicators in fine resolution based on three aspects: building morphology, ecological infrastructure, and human activities. The model accurately captures UHII by employing the definition of UHI effects. The relationship between urban morphology and UHII was further examined using extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). By taking central Beijing, China as study area, major findings include the following: (1) Significant daytime UHI effects were observed within the research area, particularly during the summer months, when it appears to be most severe. More than 90% of the region experiences varying degrees of the UHI effects. (2) UHI is significantly correlated with both 2D and 3D urban morphological indicators. Low sky view factor (SVF) and high SVF tend to mitigate UHI, whereas moderate SVF tends to aggravate UHI. (3) In densely populated areas, tall trees may be more effective than other forms of vegetation at mitigating UHI. Based on the aforementioned findings, this article suggests that urban morphology optimization should focus on seasonality, spatial specificity, and indicator specificity for megacities in urban design and spatial planning aimed at mitigating UHI.

The entire world is experiencing a crisis in public health and the economy owing to the coronavirus disease 2019 (COVID‐19) pandemic. Understanding human mobility during the pandemic helps to formulate interventional strategies and resilient measures. The widely used bike‐sharing system (BSS) could illustrate the activities of urban dwellers over time and space in big cities; however, it is rarely reported in epidemiological research. In this article, we analyze the BSS data to examine the human mobility of shared‐bike users, detecting the key time nodes of different pandemic stages and demonstrating the evolution of human mobility owing to the onset of the COVID‐19 threat and administrative restrictions. We assessed the net impact of the pandemic using the results of co‐location analysis between shared‐bike usage and points of interest. Our results demonstrate that the pandemic has reduced overall bike usage by 64.8%; however, a subsequent average increase (15.9%) in shared‐bike usage has been observed, suggesting partial recovery of productive and residential activities, although far from normal times. These findings could be a reference for epidemiological research, and thereby aid policymaking in the context of the current COVID‐19 outbreak and other epidemic events at the city scale.

Band selection is an important step in the dimensionality reduction processing of hyperspectral images and is highly important for eliminating redundant spectral information and reducing computational costs. In recent years, band selection methods based on ordered partition have been widely used in the dimensionality reduction processing of hyperspectral images. However, most of the methods use coarse and fine partition for band subspace partition, so that the partition results are affected by equal interval partition. Furthermore, existing methods usually select a representative band in each band subspace but do not consider the relationship between the output bands in the selection process, resulting in a certain degree of redundancy between the output bands. To solve the above problems, we propose a band selection method based on global partition clustering that contains band subspace partition and band selection. The band subspace partition is based on coarse and fine partition and the similarity-based ranking-structural similarity method, which divides the band space into band subspaces according to the relationship between the number of bands in the hyperspectral image and the number of selected bands. Band selection is based on the sequential forward selection method, which iteratively selects one band as the output band in each band subspace. The proposed method makes two main contributions. Firstly, this method avoids the negative effect of equal interval partition on the results. Secondly, this method fully considers the relationship between the selected bands and the bands to be selected. The effectiveness of the proposed method is demonstrated via ablation and comparison experiments on three publicly available datasets. Comparison experiments show that the classification accuracy of the proposed method can exceed the accuracy of the comparison methods.

The identification of urban functional regions (UFRs) is important for urban planning and sustainable development. Because this involves a set of interrelated processes, it is difficult to identify UFRs using only single data sources. Data fusion methods have the potential to improve the identification accuracy. However, the use of existing fusion methods remains challenging when mining shared semantic information among multiple data sources. In order to address this issue, we propose a context-coupling matrix factorization (CCMF) method which considers contextual relationships. This method was designed based on the fact that the contextual relationships embedded in all of the data are shared and complementary to one another. An empirical study was carried out by fusing point-of-interest (POI) data and taxi origin–destination (OD) data in Beijing, China. There are three steps in CCMF. First, contextual information is extracted from POI and taxi OD trajectory data. Second, fusion is performed using contextual information. Finally, spectral clustering is used to identify the functional regions. The results show that the proposed method achieved an overall accuracy (OA) of 90% and a kappa of 0.88 in the study area. The results were compared with the results obtained using single sources of non-fused data and other fusion methods in order to validate the effectiveness of our method. The results demonstrate that an improvement in the OA of about 5% in comparison to a similar method in the literature could be achieved using this method.

The urban heat island (UHI) effect accelerates the accumulation of atmospheric pollutants, which has a strong impact on the climate of cities, circulation of material, and health of citizens. Therefore, it is of great significance to conduct quantitative monitoring and accurate governance of UHI by calculating the index rapidly and expressing spatial distribution accurately. In this paper, we proposed a model that integrates UHI information with the GeoSOT (Geographic Coordinate Subdividing Grid with One-Dimension Integer Coding on 2n Tree) grid and subsequently designed the calculation method of UHI indices and expression method of UHI spatial distribution. The UHI indices were calculated on Dongcheng and Xicheng District, Beijing, in the Summer of 2014 to 2019. Experimental results showed that the proposed method has higher calculation efficiency, and achieved a more detailed description of the spatial distribution of the urban thermal environment compared with the Gaussian surface fitting method. This method can be used for large-scale and high-frequency monitoring the level of UHI and expressing complicated spatial distribution of UHI inside the city, thus supporting accurate governance of UHI.

Accurate extraction of urban landscape features in the historic district of China is an essential task for the protection of the cultural and historical heritage. In recent years, deep learning (DL)-based methods have made substantial progress in landscape feature extraction. However, the lack of annotated data and the complex scenarios inside alleyways result in the limited performance of the available DL-based methods when extracting landscape features. To deal with this problem, we built a small yet comprehensive history-core street view (HCSV) dataset and propose a polarized attention-based landscape feature segmentation network (PALESNet) in this article. The polarized self-attention block is employed in PALESNet to discriminate each landscape feature in various situations, whereas the atrous spatial pyramid pooling (ASPP) block is utilized to capture the multi-scale features. As an auxiliary, a transfer learning module was introduced to supplement the knowledge of the network, to overcome the shortage of labeled data and improve its learning capability in the historic districts. Compared to other state-of-the-art methods, our network achieved the highest accuracy in the case study of Beijing Core Area, with an mIoU of 63.7% on the HCSV dataset; and thus could provide sufficient and accurate data for further protection and renewal in Chinese historic districts.