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 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.

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 Great Wall was listed as a World Heritage Site in 1987. In recent years, the Chinese government placed significant emphasis on the preservation of the Great Wall and its invaluable resources. However, it remains challenging to quantitatively analyze the impact of multiple factors and further identify the Great Wall resource sites. In this paper, a spatial quantitative analysis framework with binary logistic regression is implemented for the impact factors of village site selection along the Great Wall watchtowers. Elevation, slope, orientation, water distance, road distance, distance-to-watchtowers were selected as candidates. 177 villages were analyzed, and the model calculation was assessed with the confusion matrix and ROC curve. Youden index is used to determine the optimal threshold for the uncertain villages, subsequently. And we classified villages into three categories: villages with strong, medium and weak relevance with Great Wall resource sites. Experimental results show that the strong and medium relevance villages accounted for 68.36%. These villages are primarily located in the central part of the study area, characterized by flat terrain and closely aligned with the road network. Village density decreases with distance from the watchtowers. Additionally, out of the 40 uncertain villages, 5 were confirmed as villages while the remaining were classified as non-villages. The conclusions of this study can serve as a reference for evaluating and predicting suitable conservation and planning initiatives for the Great Wall and its surrounding villages in the future.