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Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

The quality of urban living environments has become a focal point for local governments and citizens. By conducting a thorough analysis of the human settlement environment, the study can not only gain an intuitive insight into the quality of life of residents but also propose forward-thinking and sustainable suggestions for areas of improvement. This study optimizes and analyzes open platform data closely related to residents and assesses the suitability of different areas for living from diverse perspectives and methodologies. This study has chosen Beijing and Xi’an as the primary case studies. The local living environment is categorized into residential, living, recreational environment, transportation convenience, and safety. Our evaluation combines subjective and objective analysis methods and considers hotspot and cold spot analyses. This study employs the Analytic Hierarchy Process (AHP) as a subjective analysis method and the entropy method for objective analysis. By integrating both methods, it assesses the living environment conditions of Beijing and Xi’an. Furthermore, using GIS software, hotspot analysis is conducted for both cities, identifying areas of high and low quality. Detailed analysis is subsequently carried out for the low-quality clusters. Ultimately, this study, grounded in the theory of Transit-Oriented Development (TOD), presents recommendations for sustainable development aimed at representative rural towns and streets. City centers in Beijing and Xi’an have high-quality environments, while the outskirts show declining quality. Xi’an has uneven resource distribution, while Beijing is more balanced, with hotspot analyses indicating specific high- and low-quality cluster locations in both cities. These disparities and characteristics of the low-quality clusters offer insights for future urban development.

Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures.