Spatial-Morphological Modeling for Multi-Attribute Imputation of Urban Blocks

TL;DR

This paper introduces the SM imputer tool that combines morphological clustering with neighborhood methods to accurately reconstruct missing urban morphological indicators like FSI and GSI, enhancing urban data analysis.

Contribution

The study develops a novel spatial-morphological imputation approach that integrates city-scale patterns with local spatial data for improved urban block attribute reconstruction.

Findings

SM combined with IDW or sKNN outperforms existing models.

Composite methods leverage morphological and spatial data for better accuracy.

SM alone captures meaningful urban morphological structures.

Abstract

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.