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.

翻译：准确重建城市缺失的形态指标对于城市规划和数据驱动分析至关重要。本研究受SpaceMatrix框架启发，提出空间形态（SM）填补工具，该方法结合数据驱动的形态聚类与基于邻域的方法，在城市街区层面重建容积率（FSI）与建筑密度（GSI）的缺失值。该途径将城市尺度的形态模式作为全局先验，与局部空间信息相结合，实现上下文相关的插值。评估结果表明，虽然单独使用SM方法能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）方法结合时，相较于现有SOTA模型展现出更优性能。复合方法证明了形态学方法与空间方法结合所具有的互补优势。