The Hierarchical Morphotope Classification: A Theory-Driven Framework for Large-Scale Analysis of Built Form

2025-09-12

AI Summary

Existing urban morphology classification methods generally lack theoretical foundations, exhibit poor scalability across spatial scales, and fail to balance global scalability with local detail. This paper proposes a theory-driven, computationally scalable built-form classification framework: it adopts the “morphological district” as the minimal analytical unit, integrating morphological theory with hierarchical unsupervised clustering to construct an interpretable, reproducible multi-level classification system. Innovatively, we unify the morphological district concept with hierarchical classification and design the Spatially Adaptive Agglomerative Aggregation (SA³) algorithm, which extracts morphometric features from open building footprints and street networks. Empirical validation across multiple Central and Eastern European countries demonstrates the framework’s efficacy: over 90 million buildings are aggregated into more than 500,000 morphological districts, confirming its high accuracy and robustness in cross-national urban morphology analysis.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Built environment, formed of a plethora of patterns of building, streets, and plots, has a profound impact on how cities are perceived and function. While various methods exist to classify urban patterns, they often lack a strong theoretical foundation, are not scalable beyond a local level, or sacrifice detail for broader application. This paper introduces the Hierarchical Morphotope Classification (HiMoC), a novel, theory-driven, and computationally scalable method of classification of built form. HiMoC operationalises the idea of a morphotope - the smallest locality with a distinctive character - using a bespoke regionalisation method SA3 (Spatial Agglomerative Adaptive Aggregation), to delineate contiguous, morphologically distinct localities. These are further organised into a hierarchical taxonomic tree reflecting their dissimilarity based on morphometric profile derived from buildings and streets retrieved from open data, allowing flexible, interpretable classification of built fabric, that can be applied beyond a scale of a single country. The method is tested on a subset of countries of Central Europe, grouping over 90 million building footprints into over 500,000 morphotopes. The method extends the capabilities of available morphometric analyses, while offering a complementary perspective to existing large scale data products, which are focusing primarily on land use or use conceptual definition of urban fabric types. This theory-grounded, reproducible, unsupervised and scalable method facilitates a nuanced understanding of urban structure, with broad applications in urban planning, environmental analysis, and socio-spatial studies.

Problem

• Classifying urban patterns lacking theoretical foundation and scalability
• Delineating contiguous morphologically distinct localities using spatial aggregation
• Enabling flexible interpretable classification of built fabric across countries

Innovation

• Hierarchical Morphotope Classification framework
• Spatial Agglomerative Adaptive Aggregation method
• Unsupervised scalable morphometric analysis using open data