Urban morphology is the area of urban studies that explores the physical form of cities in space and the way it changes in time in relation to the agents of such change (Kropf, 2017). The discipline is based on the analysis of space, traditionally mostly visual and qualitative (Dibble et al., 2015); its objects are the fundamental elements of urban form (building, plot, street) (Moudon, 1997) as well as a range of analytical constructs such as axial maps (Ariza-Villaverde, Jiménez-Hornero, & Ravé, 2013) or proximity bands (Araldi & Fusco, 2019). The increased availability of morphological data and computational power have led in time to more emphasis on quantitative forms of analysis, and the emergence of Urban Morphometrics (UMM) (Dibble et al., 2017): this approach describes urban form via the systematic and comprehensive measurement of its morphological characters. Since UMM analysis is addressed both in-depth and at large scale, it is grounded on the intensive use of GIS software (proprietary ArcGIS, MapInfo, open-source QGIS) either through built-in processing tools or specific plugins like Urban Network Analysis (Sevtsuk & Mekonnen, 2012) or Place Syntax Tool (Ståhle, Marcus, & Karlström, 2005). However, essential functions to conduct such measurements on specific urban morphometric characters or processes such as morphological tessellation, are not always available: current plugins offer only a limited number of functionalities as they are mainly application or case-specific. This paper is hereby proposing momepy, a Python toolkit which aims to overcome such limitations by enabling a systematic in-depth analysis of urban form, to comprehensively include a wide range of measurable characters, with a prospect of expanding future development due to its open-source nature and independence on proprietary software or operating systems. The development of momepy is timely, as the role of measurable characters is vital to recognize form-based patterns and establish descriptive and analytical frameworks of human settlements, in the "age of urbanization".

Unprecedented urbanisation processes characterise the Great Acceleration, urging urban researchers to make sense of data analysis in support of evidence-based and large-scale decision-making. Urban morphologists are no exception since the impact of urban form on fundamental natural and social patterns (equity, prosperity and resource consumption’s efficiency) is now fully acknowledged. However, urban morphology is still far from offering a comprehensive and reliable framework for quantitative analysis. Despite remarkable progress since its emergence in the late 1950s, the discipline still exhibits significant terminological inconsistencies with regards to the definition of the fundamental components of urban form, which prevents the establishment of objective models for measuring it. In this article, we present a study of existing methods for measuring urban form, with a focus on terminological inconsistencies, and propose a systematic and comprehensive framework to classify urban form characters, where ‘urban form character’ stands for a characteristic (or feature) of one kind of urban form that distinguishes it from another kind. In particular, we introduce the Index of Elements that allows for a univocal and non-interpretive description of urban form characters. Based on such Index of Elements, we develop a systematic classification of urban form according to six categories (dimension, shape, spatial distribution, intensity, connectivity and diversity) and three conceptual scales (small, medium, large) based on two definitions of scale (extent and grain). This framework is then applied to identify and organise the urban form characters adopted in available literature to date. The resulting classification of urban form characters reveals clear gaps in existing research, in particular, in relation to the spatial distribution and diversity characters. The proposed framework reduces the current inconsistencies of urban morphology research, paving the way to enhanced methods of urban form systematic and quantitative analysis at a global scale.

Cities are complex products of human culture, characterised by a startling diversity of visible traits. Their form is constantly evolving, reflecting changing human needs and local contingencies, manifested in space by many urban patterns. Urban morphology laid the foundation for understanding many such patterns, largely relying on qualitative research methods to extract distinct spatial identities of urban areas. However, the manual, labour-intensive and subjective nature of such approaches represents an impediment to the development of a scalable, replicable and data-driven urban form characterisation. Recently, advances in geographic data science and the availability of digital mapping products open the opportunity to overcome such limitations. And yet, our current capacity to systematically capture the heterogeneity of spatial patterns remains limited in terms of spatial parameters included in the analysis and hardly scalable due to the highly labour-intensive nature of the task. In this paper, we present a method for numerical taxonomy of urban form derived from biological systematics, which allows the rigorous detection and classification of urban types. Initially, we produce a rich numerical characterisation of urban space from minimal data input, minimising limitations due to inconsistent data quality and availability. These are street network, building footprint and morphological tessellation, a spatial unit derivative of Voronoi tessellation, obtained from building footprints. Hence, we derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities – Prague and Amsterdam – and discuss potential applications and further developments. The proposed classification method represents a step towards the development of an extensive, scalable numerical taxonomy of urban form and opens the way to more rigorous comparative morphological studies and explorations into the relationship between urban space and phenomena as diverse as environmental performance, health and place attractiveness.

The recent growth of geographic data science (GDS) fuelled by increasingly available open data and open source tools has influenced urban sciences across a multitude of fields. Yet there is limited application in urban morphology—a science of urban form. Although quantitative approaches to morphological research are finding momentum, existing tools for such analyses have limited scope and are predominantly implemented as plug‐ins for standalone geographic information system software. This inherently restricts transparency and reproducibility of research. Simultaneously, the Python ecosystem for GDS is maturing to the point of fully supporting highly specialized morphological analysis. In this paper, we use the open source Python ecosystem in a workflow to illustrate its capabilities in a case study assessing the evolution of urban patterns over six historical periods on a sample of 42 locations. Results show a trajectory of change in the scale and structure of urban form from pre‐industrial development to contemporary neighborhoods, with a peak of highest deviation during the post‐World War II era of modernism, confirming previous findings. The wholly reproducible method is encapsulated in computational notebooks, illustrating how modern GDS can be applied to urban morphology research to promote open, collaborative, and transparent science, independent of proprietary or otherwise limited software.