Research on a Prototype System of Spatial Big Data Approximation Query for Smart City Based on Cloud Computing

Smart city spatial query is a data structure that arranges spatial objects in a certain order based on their location and shape, or a certain spatial relationship between spatial objects. It aims to quickly filter spatial objects that are unrelated to specific spatial operations and is an important indicator for ensuring efficient search and display of spatial data. Its performance directly affects the overall performance of geographic information systems and spatial databases. At present, various spatial approximation query algorithms have been optimized, but there are still significant limitations. This article empirically tests the relationship between smart cities and high-quality development of the circulation industry based on the spatial DID model. The trajectory data is divided into sub trajectories using a trajectory data partitioning method based on optimized minimum boundary rectangle MBR to improve the approximation effect of trajectory data. The results show that the optimal convergence speed of our method is 50 times, and the convergence accuracy is the highest, with a maximum value of 1.5160, a minimum value of 1.6623, and an average value of 1.54471. When the data volume is 3.0, the running time of the method in this article is 560ms. Based on cloud computing systems, the efficiency and accuracy of window queries and spatial connection queries are the highest, and the algorithm has high adaptability.