The hierarchical morphotope classification

A theory-driven framework for large-scale analysis of built form

Bibliographic Data

ID11455068
AuthorsMartin Fleischmann (0000-0003-3319-3366, corresponding author), Krasen Samardzhiev, Anna Brázdová, Daniela Dančejová, Lisa Winkler (0000-0002-6861-0046)
Year2026
Volume174
Pages107047
Publication date2026-07-01
Open AccessYes
TypeARTICLE
VenueCities (JOURNAL)
Journal identifiersISSN: 0264-2751 • E-ISSN: 1873-6084
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cities.2026.107047
OpenAlexW4414594463
LanguageEN
Citations received3
References cited31

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

Bespoke · Cluster analysis · Hierarchical clustering · Locality · Perspective (graphical · Regionalisation · Scalability · Scale (ratio · 3D Surveying and Cultural Heritage · Architecture and Computational Design · BIM and Construction Integration

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