The concept of human-earth system represents a dynamic structure formed by the interaction between human and earth within a specific region. Considering the complexity of the system, it is necessary to construct a more powerful indicator system to describe the interaction and reflection processes between human and earth systems. The term of spatial resilience can be understood as the overall ability of regional space to absorb, recover or transform natural and social disturbances. Spatial resilience targeted at securing the structural stability of human-earth system, and it also provides a new approach for the shaping of targeted theoretical models and quantifiable indicator system in the study of human-earth system. This study reviews the research progress of spatial resilience from three perspectives, including: ecological policy and ecosystems spatial resilience from a natural perspective; environmental changes and spatial resilience of community production and living from a social perspective and the spatial resilience integrated indicator system from a comprehensive description perspective. Then the article puts forward three important directions of spatial resilience research in China, including understanding the spatial state that guaranteed system sustainability, identifying the spatial thresholds of human-earth system dynamic process, and satisfying the national demands of ecological protection and social development. This study also discusses the spatial resilience indicator system in various human-earth system functional areas from aspects of system structure, function and threshold. By the deepening of the theories and methods of spatial resilience, the human-earth system dynamics can be further understood, so as to improve the regional level accuracy on simulating the socioeconomic, environmental and political paths, and provide key theoretical and methodological support for the decision-making of sustainable development at global, national and regional levels.

With the improvement of geographic data acquisition capabilities, the volume of geographic data has been growing exponentially, and the data types as well as characteristics have become more diverse. The effective identification and classification of data has become the key to understand spatio-temporal patterns, evolutionary processes, and driving mechanisms of geographic phenomena. However, traditional clustering methods are facing some challenges, such as large amount, high-dimensionality and poor-quality of the data to be dealt with. Therefore, it is necessary to improve clustering methods. This paper first describes the transformation from one-way clustering to tri-clustering. One-way clustering methods perform the clustering analysis along with the samples or the attributes. They played an important role in previous studies, but ignored local features that are very similar. Co-clustering methods perform the submatrix partitioning scheme based on location similarity of elements within the data matrix. They avoid shortages of one-way clustering by realizing the clustering from both rows and columns, making similar elements into the same submatrix and dissimilar ones into different ones. However, they cannot satisfy multiple directions interpretations of geographical research since they do not support 3D panel data body. Then, we develop a new tri-clustering method, presents the workflow of using tri-clustering to spatio-temporal patterns' studies, and summarizes how to construct the 3D data matrix for clustering according to different aspects of 'space-time-scale-attribute' involved in the analysis. Finally, we show some practices of tri-cluster. The results show that: (1) Tri-clustering is an effective method to identify the spatio-temporal differentiation of geographic data in the era of big data by solving problems, i.e. data of high dimensionality and low quality. (2) Tri-clustering is universal in the algorithmic level when facing different geographic topics, but the differences rely on the 3D data matrices constructed according to different aspects of "space-time-scale-attribute" involved in the analysis. And, different data matrices are clustered to different results, which answer different topics. (3) Tri-clustering is able to interpret the spatio-temporal differentiation of geographic data in multiple directions, multiple scales, and multiple hierarchies, and thereby reveal the superposition effects of spatio-temporal scales of geographic features. Finally, we emphasize the significance of constructing 3D data matrices based on different geographic topics and expect that tri-clustering methods can enhance the ability to analyze geographic data with multiple spatial scales and attributes in the future.

The complexity in low altitude has hindered the development and utilization of low-altitude airspace resources with UAVs as the main application users. With the rapid increase of UAV numbers and the rapid expansion of commercial application of UAVs, the conflict between the increasing flight demand and the limited flight space is increasingly prominent at low altitude. Therefore, how to conduct the activities of UAVs at low-altitude safely and efficiently is a problem to be urgently solved. Many countries or regions are looking for solutions. Among the proposed solutions or methods, the low-altitude public air route network of UAVs, which is proposed firstly by the Chinese Academy of Sciences (CAS) to orderly manage UAV low-altitude traffic and efficiently utilize low-altitude resources, has been widely recognized. However, the concept is still in the early exporting stage and is not clear on how to construct. In addition, how to quickly acquire high-precision geographic information to support safe and efficient flights of UAV in low altitude, especially in urban areas with complex and high-dynamic changeable surface environment, is also difficult. In view of the promising application of remote sensing (RS) technology in extracting and processing air route sensitive elements, this paper proposes a method to efficiently and iteratively construct the low-altitude public air route network by RS and geographic information technology in urban areas, and then demonstrates the feasibility of this method from theoretical support and existing research foundation. The technical roadmap includes four steps: (1) generating the first-level air route network based on ground roads to make full use of ground traffic facilities; (2) constructing the second-level air route network by using positive constraints of air routes, such as green lands and waters; (3) constructing the third-level air route network by avoiding negative constraints of air routes, including buildings, weak-communication areas and power lines (poles); (4) generating the fourth- and fifth-level air route network by simulated flight and practical flight tests. Comparative analysis between actual measurement and simulated environment map is conducted to ensure safe UAV flights. The method proposed above comprehensively utilizes interdisciplinary technologies such as RS, geographic information system (GIS), aviation and transportation, and provides a new way to make safe and efficient UAV operations in low altitude. Furthermore, this paper uses geography method to construct UAV air route network in low altitude, which is another breakthrough in the application of UAVs in geography after UAV remote sensing application. It also expands the research scope of geography and will certainly promote the development of geographic science.