The urban network is an important method of spatial optimization, and measuring the development level of the urban network is a prerequisite for spatial optimization. Combining geographic information system (GIS) spatial analysis, social network analysis, and multidimensional scaling models, we explored the evolution of the urban network spatial structure in the Yangtze River Delta from 1990 to 2017 and proposed corresponding optimization measures. The results showed that the urban network spatial structure of the Yangtze River Delta has evolved from a single-center cluster with Shanghai as its core to a multi-center network with Shanghai as its core and Nanjing, Hangzhou, and Hefei as secondary cores. The density of the urban network has gradually expanded, but the strength of the connection between edge cities such as Chizhou, Suqian, and Quzhou and the core cities needs to be further improved. We found that the evolution of the urban network spatial structure has been driven by preferential attachment, path dependence, and differences in economic and industrial development. Finally, we propose optimizing the urban network spatial structure by strengthening the driving ability of the core cities, clarifying urban functions and development directions, and establishing a unified coordination mechanism. This paper enriches and deepens our understanding of the characteristics of the city network in the Yangtze River Delta, and provides a reference for the optimization of the urban network spatial structure.

The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way of solving the existing copyright management problem or other problems in spatial data infrastructure for data sharing. An Ethereum smart contract is used in this model to realize spatial data digital rights management function. The InterPlanetary File System is utilized as external data storage for storing spatial data in the decentralized file system to avoid data destruction that is caused by a single point of failure. There is no central server in the model architecture, which has a completely decentralized nature and it makes spatial data rights management not dependent on third-party trust institutions. We designed three spatial data copyright management algorithms, developed a prototype system to implement and test the model, used the smart contract security verification tool to check code vulnerabilities, and, finally, discussed the usability, scalability, efficiency, performance, and security of the proposed model. The result indicates that the proposed model not only has diversified functions of copyright management compared with previous studies on the blockchain-based digital rights management, but it can also solve the existing problems in traditional spatial data infrastructure for data sharing due to its characteristics of complete decentralization, mass orientation, immediacy, and high security.

There is undoubtedly a groundswell of support for the concept of geographic data sharing with the rapid development and wide-ranging application of geographic information science. However, copyright protection and infringement detection in the process of geographic data sharing has always been an important issue that needs to be addressed urgently. In this paper, we present a novel infringement detection method for GIS vector data to compensate for the shortcomings of vector data digital watermarking technology in infringement detection. The method determines whether infringement exists by the duplication degree between the original data and the vector data to be detected in three features including feature features, included angle features and vertex features which gets by using the spatial information of vector data to perform the feature matching based on GeoJSON format data. The experimental results indicate that the proposed algorithm can effectively resist common geometric attacks, such as interpolation attack, deletion attack, similarity transformation attack, feature order scrambling attack, and feature simplification attack, on vector data, which proves that the proposed algorithm has excellent robustness and meets the requirements of practical application.