Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its sub-basins were selected as the research areas, and the generalized fractal dimension and multifractal spectrum were computed and analyzed with a multifractal technique based on the slope distribution probability. The results showed that the Yellow River Basin and its sub-basins exhibit clear multifractal characteristics, which indicates that the multifractal theory can be applied well to the analysis of large-scale basin geomorphological features. We also concluded that the region with the most uneven terrain is the Yellow River Downstream Basin with the “overhanging river”, followed by the Weihe River Basin, the Yellow River Mainstream Basin, and the Fenhe River Basin. Multifractal analysis can reflect the geomorphological feature information of the basins comprehensively with the generalized fractal dimension and the multifractal spectrum. There is a strong correlation between some common topographic parameters and multifractal parameters, and the correlation coefficients between them are greater than 0.8. The results provide a scientific basis for analyzing the geomorphic characteristics of large-scale basins and for the further research of the morphogenesis of the forms.

The identification and delineation of urban functional zones (UFZs), which are the basic units of urban organisms, are crucial for understanding complex urban systems and the rational allocation and management of resources. Points of interest (POI) data are weak in identifying UFZs in areas with low building density and sparse data, whereas remote sensing data lack the necessary semantic information for functional zoning, and single-source data cannot perform a highly comprehensive characterization of complex UFZs. To address these issues, this study proposes a method for identifying UFZs by fusing multi-attribute features from multi-source data and introduces nighttime light and land surface temperature (LST) indicators as functional zoning references, taking the main urban area of Zhengzhou as an example. The experimental results show that the POI data with integrated three-level semantic information can characterize the semantic information of functional areas well, and the incorporation of multi-spectral, nighttime light, and LST data can further improve the recognition accuracy by approximately 10.1% compared with the POI single-source data. The final recognition accuracy and kappa coefficient reached 84.00% and 0.8162, respectively, indicating that the method is largely consistent with the actual situation and is feasible. The analysis showed that the main urban area of Zhengzhou as a whole is characterized by the coordinated development of single and mixed functional areas, in which a distinct residential-commercial-public complex is formed, and the urban functional areas on the block scale have diverse attributes. This study can provide a decision-making reference for the future development planning and management of Zhengzhou, China.

Urban expansion is influenced by complex and variable social, economic, natural, and policy-related factors. Given their nonlinear interactions, accurately modeling these urban expansion processes poses a challenge. While most studies treat the city as an independent entity, prioritizing internal urban factors, urban land expansion is influenced by intercity interactions and the ecological environment. This study proposes a new approach that couples the gravitational field model, ecological constraints, and the Future Land Use Simulation (FLUS) model, comprehensively considering the impact of intercity interaction and the ecological environment. The experiment in Henan Province in China assessed the effects of factors such as basic spatial variables (Slope and distance to the city center), urban gravitational field, and ecological constraints on urban expansion through the optimal parameters-based geographical detector (OPGD) model. The feasibility of the method was confirmed by this case study, which shows that it improves the simulation accuracy of the urban agglomeration scale, particularly for central cities. We identified the urban gravitational field and ecological constraints as two important factors affecting the expansion of urban agglomerations. Areas with stronger urban spatial fields are more likely to attract neighboring resources and promote urban expansion, whereas ecological factors constrain the expansion behavior of cities under the condition of ecological and environmental resource protection needs, and both of them work together to influence the expansion behavior of urban clusters. Therefore, we posit that intercity interactions and ecological constraints are important considerations for the future spatial planning of urban agglomerations and for coordinating the harmonious development of urbanization and ecological conservation.

Understanding the mechanisms driving the urban heat island (UHI) phenomenon is essential for urban sustainability. This study investigated the spatiotemporal dynamics and underlying factors of surface urban heat island (SUHI) in Xiamen. Utilizing the radiation conduction equation, we calculated surface urban heat island intensity (SUHII) for the summers and winters of 2003, 2005, 2010, 2015, and 2020, followed by spatial distribution analysis. The local climate zone (LCZ) method was employed to assess surface morphology and spatial structure in 2010 and 2020. Urban driving factors, including built-up areas, building height, gross domestic product (GDP) per capita, industrial structure, and population density, were analyzed using the Geodetector model to explore their influence on SUHI across seasons. Based on different LCZ types, a more detailed analysis was conducted on SUHI and the performance of influencing factors using Pearson’s correlation. Key findings indicate that (1) the proportion of SUHI areas in built-up LCZ types always exceeds that of natural LCZ types and is more pronounced in the summer than in the winter. (2) In built-up LCZ types, open mid-rise built (LCZ 5) showed the highest average proportion of SUHI areas in the summer (95.95%), and large low-rise built (LCZ 8) had the highest average proportion in the winter (95.28%). In natural LCZ types, bare rock or paved (LCZ E) had the highest average proportion of SUHI areas in both the summer (61.86%) and winter (51.26%), and water (LCZ G) had the lowest average proportion in the summer (6.16%) and winter (4.92%). (3) Significantly, building height and proportion of the secondary industry intensified the SUHI in the summer, with dynamic changes observed during the winter. This study provides more targeted insights into mitigating SUHI in Xiamen and other similar coastal cities.

Multifractal theory provides an effective method for the scientific quantification of the river network features. This method has been applied to estimate river network structure in previous research, but there are few temporal and spatial analysis studied for large river basins based on multifractals. In this study, the Yellow River Basin and its nine provinces were selected as the study area, and the generalized fractal dimension and multifractal spectrum of the study area during the period 2000–2020 were calculated and analyzed. We analyzed the response relationship between the river network changes and the urbanization process. Results indicated that the river network of the study area exhibited obvious multifractal properties, which were mainly affected by dense river networks. The complexity of the studied river network has shown a tendency to decrease over time. The rate of change in the river network structure has a high positive correlation with urbanization, that is, the higher the rate of urbanization, the greater the impact on the river network structure. Additionally, the changes of the river network structure are more affected by urbanization during the rapid urbanization stage. We applied multifractal analysis to study the river network structure changes, which is of great significance for scientifically quantifying fluvial characteristics and studying the development and evolution of river networks.

In the process of global information construction, different fields have built their own discrete global grid systems (DGGS). With the development of big data technology, data exchange, integration, and update have gradually become a trend, as well as the associative integration of different DGGS. Due to the heterogeneity of DGGS and the different encoding rules, how to build the encoding conversion rules and data mapping relationship between the same object in various DGGS is an effective support and key technology to achieve the interoperability of DGGS. As a kind of multipurpose DGGS, the quaternary triangular mesh (QTM) has become an effective spatial framework for constructing the digital earth because of its simple structure. At present, there are many schemes for QTM encoding research, which plays a key role in the development of QTM, but at the same time, it also leads to difficulties in the communication and integration of QTM under different encoding. In order to solve this problem, we explore the characteristics of QTM encoding, and put forward three conversion algorithms: resampling conversion algorithm, hierarchical conversion algorithm, and row–column conversion algorithm.