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Foreword: plan less to plan better -- Preface: towards masterplanning for change -- About the authors -- PART I TOWARDS AN ECOLOGY OF URBAN FORM -- 1 Design and change: reconciling the paradox -- 1.1 Urbanisation, anthropocene and the great acceleration -- 1.2 About connectedness and complexity: the way change occurs -- 1.3 How complex systems change: adaptive cycles, panarchy & -- resilience -- 1.3.1 Adaptive cycle and panarchy -- 1.3.2 Bouncing back and bouncing forward: the concept of resilience across disciplines -- 1.3.3 Neither back nor forward: evolutionary resilience -- 1.4 Implementing resilience in urban design -- 2 From system ecology to urban morphology -- 2.1 System ecology & -- urban morphology: not so different after all -- 2.2 Introducing urban morphology -- 2.2.1 How urban form changes: the urban form adaptive cycle -- 2.2.2 Between inertia and change: panarchy in urban form -- 2.3 Design and change: the paradox reconciled -- PART 2 MASTERPLANNING FOR CHANGE: THE DESIGN APPROACH -- 3 Towards a design agenda -- 3.1. Designing the city as a complex system -- 3.1.1 Sustainability, resilience and 'shepherded self-organization' -- 3.1.2 Advancing the place-making tradition -- 3.2 The five attributes of resilient cities -- 3.2.1 Defining the attributes -- 3.2.2 The attributes at-scale: a component-specific description -- 4 Masterplanning for change: the design approach -- 4.1 Analysis -- WP. 1: Drawing the City -- WP. 2: History -- WP. 3: Stories -- WP. 4: Planning and Policy Framework -- WP. 5: Community Potential -- WP. 6: Density and Urban Intensity -- WP. 7: Comparing Places -- WP. 8: Mental Map -- WP. 9: Fear Map -- WP. 10: Street Centrality -- WP. 11: Street Hierarchy -- WP. 12: Transportation Network -- WP. 13: Street Front Quality.

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.

Proliferation of Short Term Rental (STR) in cities has generated considerable debate as it was found associated with negative externalities, such as gentrification. Nonetheless, it signals urban qualities working as attractors at different geographical scales. STRs' relation with urban form remains largely understudied. In this paper, we explore how urban form relates to STRs registered by the Airbnb platform in Amsterdam (NL). First, we identify urban types (homogenous patterns of form) through an 'urban morphometric' approach. Second, we assess the relation between urban types and density of Airbnbs via a composite machine learning (ML) technique. Third, we provide profiles of the urban types most strongly associated with it. Fifteen urban types explain up to 44% of Airbnb density's variance. Compact and diverse urban types relate more strongly with Airbnbs. Conversely, repetitive, sparse and uniform urban types are inversely related. The proposed morphometric-based method is robust, replicable and scalable, offering a novel way to study the intricate relation between urban form, STRs and, in fact, any other measurable urban dynamics at an unprecedented scale. By identifying spatial features related to urban attractiveness, it can inform evidence-based design codes incorporating place-making qualities in existing and new neighbourhoods.

Studies on urban deprivation date back to the 19th Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses such a gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making.