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The spatial data model is the precondition and key to the design and realization of spatial database. To choose which kind of data model for storage and management of GIS data, how to realize the data management by using database system, how to make sure the integrality and consistency of geographic data and how to gain the high efficiency of the access to the spatial database are all the important questions the need to be solved during the whole system design. The geospatial database of national fundamental geographic information system is one of the important parts of the National spatial data infrastructure (NSDI). As the foundation of dimensional orientation for various of information systems of national level, it has been playing a very important role in the national economical and social development. To improve the organization and management of fundamental geographic spatial data, and to ensure the data can be utilized well, a relatively perfect data model must be applied. In this paper, the course of development of GIS spatial data model was summarized at first. Through an analysis of the advantages and disadvantages of current mainstream GIS data models, it was inferred that the object-relation data model could organize and manage geographic data better. Then the paper probed into applying the object-relation data model to the management of geographic data, and the data model of national 1:1,000,000 geospatial database was established. Finally, an illustration of administrative areas was used to demonstrate that the model could manage spatial data effectively.

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Neighborhood relationship plays an important role in spatial analysis, map generalization, co-location data mining and other applications. From the perspective of computation, the formal model of neighborhood representation is a challenging question. This study presents a formal spatial data model for representing the planar spatial field with the support of Delaunay triangulation. Based on the three geometric elements in a triangle of the vertex, edge, and triangle area, the constructed data model describes the spatial objects of a point, line, and region respectively, as well as the neighborhood relationships among them. Three types of operators based on the model are formally defined, expanding, compressing and skeletonizing. For practical applications, three complex operators are extended by continuous and conditional operation. Through the application example of urban building generalization, this study illustrates the analysis of a neighborhood relationship and the detection of spatial conflicts, which is a crucial pre-process during map generalization. With the support of the proposed formal model of neighborhood representation, the generalization method uses the three basic operations of grouping, displacement and aggregation to perform decision making and detailed operation. The generalized result can retain the balance of built-up area better than that of other similar building generalization methods.

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformer, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures and "hallucinated" structures. Results suggest that low complexity regions in the sequences designed by deep learning techniques remain to be improved, when compared to the native sequences.