用空间形态+邻近信息，补全城市街区缺失的用地指标。
Spatial-Morphological Modeling for Multi-Attribute Imputation of Urban Blocks
- 基于形态聚类与邻域方法融合，结合全局模式与局部信息。
- 组合模型在FSI和GSI补全上优于当前最优方法。
- 适合城市规划、地理信息系统研究者使用。
准确重建城市中缺失的形态指标对城市规划与数据驱动分析至关重要。本文提出空间-形态（SM）插补工具，结合数据驱动的形态聚类与基于邻域的方法，重构城市街区层面的容积率（FSI）与地面率（GSI）缺失值，受SpaceMatrix框架启发。该方法将城市尺度的形态模式作为全局先验，结合局部空间信息进行上下文依赖的插值。评估表明，尽管单独使用SM可捕捉有意义的形态结构，但其与反距离加权（IDW）或空间k近邻（sKNN）方法结合后，在性能上优于现有最先进模型。复合方法展现出形态与空间方法的互补优势。
原文摘要 · Abstract (English)
Accurate reconstruction of missing morphological indicators of a city is crucial for urban planning and data-driven analysis. This study presents the spatial-morphological (SM) imputer tool, which combines data-driven morphological clustering with neighborhood-based methods to reconstruct missing values of the floor space index (FSI) and ground space index (GSI) at the city block level, inspired by the SpaceMatrix framework. This approach combines city-scale morphological patterns as global priors with local spatial information for context-dependent interpolation. The evaluation shows that while SM alone captures meaningful morphological structure, its combination with inverse distance weighting (IDW) or spatial k-nearest neighbor (sKNN) methods provides superior performance compared to existing SOTA models. Composite methods demonstrate the complementary advantages of combining morphological and spatial approaches.
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