| Abstract |
This article uses spatial data and 3S (Spatial, Surveying, and Remote Sensing) technology to enhance digital city planning. The methodology integrates WebGIS Big data, statistical feature extraction techniques, and strategic planning to create a comprehensive framework for digital urban planning. Spatial information point calibration ensures accurate spatial positioning during the planning process. At the same time, data fusion and fuzzy C-means clustering analysis are utilized to detect and analyze WebGIS data within the digital city planning context. The proposed model incorporates a piecewise fitting method within the Big data integration scheduling framework for digital city spatial planning. A C/S (Client/Server) architecture and ARM-embedded technology have been developed to establish a robust digital city planning system to support this approach. This system encompasses modules for WebGIS information collection, bus control, database management, human-machine interaction, and data processing terminals. Simulation results demonstrate that the method significantly reduces delays in digital city planning and design. When analyzing a data scale of 100, the method exhibits an 83.4% lower delay than the fuzzy method. Although delays increase with larger data scales, even at a scale of 400, the method still offers a 43.1% reduction compared to the fuzzy method. Across varying data scales, the proposed method consistently maintains approximately 60% lower latency than the rough set method. This method showcases superior intelligence and exhibits strong capabilities in accessing and scheduling WebGIS data effectively. © 2023 The Authors |
