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.

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Urban Morphometrics (UMM) is an expanding area of urban studies that aims at representing and measuring objectively the physical form of cities to support evidence-based research. An essential step in its development is the identification of a suitable spatial unit of analysis, where suitability is determined by its degree of reliability, universality, accessibility and significance in capturing essential urban form patterns. In Urban Morphology such unit is found in the plot, a fundamental component in the morphogenetic of urban settlements. However, the plot is a conceptually and analytically ambiguous concept and a kind of spatial information often unavailable or inconsistently represented across geographies, issues that limit its reliability and universality and hence its suitability for Urban Morphometric applications. This calls for alternative methods of deriving a spatial unit able to convey reliable plot-scale information, possibly comparable with that provided by plots. This paper presents Morphological Tessellation (MT), an objectively and universally applicable method that derives a spatial unit named Morphological Cell (MC) from widely available data on building footprint only and tests its informational value as proxy data in capturing plot-scale spatial properties of urban form. Using the city of Zurich (CH) as case study we compare MT to the cadastral layer on a selection of morphometric characters capturing different geometrical and configurational properties of urban form, to test the degree of informational similarity between MT and cadastral plots.Findings suggest that MT can be considered an efficient informational proxy for cadastral plots for many of the tested morphometric characters, that there are kinds of plot-scale information only plots can provide, as well as kinds only morphological tessellation can provide. Overall, there appears to be clear scope for application of MT as fundamental spatial unit of analysis in Urban Morphometrics, opening the way to largescale urban morphometric analysis.

While interest in blockchain technology and applications increases, research studying the role of trust as an element that leads potential users and consumers to adopt and accept the technology remains scarce. This study conducts acceptance research that expands beyond traditional acceptance models and explores the role of trust from the user/consumer perspective. It provides comprehensive insights from the user/consumer angle and a deeper understanding of the role of trust in blockchain adoption. Using an inductive research approach that builds theory from qualitative empirical data, this paper identifies trust as a critical benefit of blockchain technology and applications, encompassing both functional (economic and system-/ process-related) as well as emotional benefits (social and personal). As trust spans across functional and emotional benefit dimensions, this study suggests that trust is a key driver for user/consumer adoption of blockchain technology and applications.

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.