Leveraging Machine Learning to Explore the Spatial Function Service Value Through Human Perceptual Experience

Spatial function is critical to sustainable development in modern metropolises. Traditional spatial function patterns are mainly shaped by market forces, policies and zoning regulations. The role of human perceptual experience remains understudied. Taking Beijing as the study area, this study examines the relationship between public perception and spatial function. It focuses on how consistency or inconsistency between the two environments influences spatial function service value. A two-step method is adopted in this research. First, the Weighted Average Cluster Index (WACI) works to analyze the spatial clustering of points of interest (POIs). This provides a quantitative basis for identifying spatial functions from integrated multi-source data. Second, a CatBoost binary classification model is applied to evaluate consistency and interpret the driving mechanisms. Key findings are obtained: (1) Perceptual underestimation of agricultural and cultural POI is significant in urban–rural transition zones. (2) Global analysis identifies education, commercial and sports POIs as the strongest contributors to function recognition. Local analysis reveals heterogeneous effects of POI categories across spatial scales. (3) Positive synergies occur between education–commercial and leisure–scenic areas. Industrial zones show functional competition with leisure and scenic areas. Shapley Additive Explanations (SHAP) clarifies the causes of perceptual discrepancies. It emphasizes the impacts of diverse urban morphological features and their interactive effects on public perception. Accordingly, strategies are provided for urban planners and policymakers such as promoting functionally mixed layouts with high consistency. This study offers an alternative approach to improving spatial function efficiency towards a sustainable development of modern metropolises.