Research on Spatial Optimization and Functional Layout Model of Urban Buildings Based on Big Data Analysis

A spatial optimization and functional layout model of urban buildings is based on big data analysis to address the limitations of traditional urban planning methods in dealing with urban complexity and dynamics. By integrating multi-source data, including mobile positioning, building use, environmental monitoring, population statistics and traffic flow, using data cleaning, integration and storage technology, combining machine learning algorithm and GIS spatial analysis technology, a scientific and effective model framework for urban building spatial optimization and functional layout is constructed. Taking a big city as an example, the key features are extracted by principal component analysis (PCA) and cluster analysis, and the random forest algorithm is used to optimize the model, and the architectural space optimization scheme for this city is put forward. The results show that after optimization, the urban traffic congestion index decreased by 38.79%, the average commuting time shortened by 22.97%, the air quality index increased by 25.90%, the green space coverage increased by 37.91%, and the residents' satisfaction increased by 21.51%. The research shows that big data analysis technology can provide scientific basis for urban planning, improve the accuracy and effectiveness of urban planning, and provide new ideas and methods to solve the problems of uneven spatial distribution and unreasonable functional layout of urban buildings.