Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science

Fleischmann, Martin; Feliciotti, Alessandra; Kerr, William R.

doi:10.1111/gean.12302

Cited by 32 publications

(10 citation statements)

References 66 publications

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“…In urban morphology, theory and qualitative evidence suggest that different urban patterns emerge in areas of different historical origins or else belonging to different ‘morphological periods’ (Whitehand et al, 2014). This notion has also been observed quantitatively in the urban fabric (Boeing, 2021; Dibble et al, 2019; Fleischmann et al, 2021; Porta et al, 2014) as well as in land-use patterns (Castro et al, 2019) of cities and is inherently embedded in our OTU.…”

Section: Method: Building a Taxonomy Of Urban Formsupporting

confidence: 67%

Methodological foundation of a numerical taxonomy of urban form

Fleischmann

Feliciotti

Rómice

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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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.

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Section: Method: Building a Taxonomy Of Urban Formsupporting

confidence: 67%

Methodological foundation of a numerical taxonomy of urban form

Fleischmann

Feliciotti

Rómice

et al. 2021

Environment and Planning B: Urban Analytics and City Science

Self Cite

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“…As mapping features such as building footprints continue to be complex and time-consuming, they remain unmapped in much of the world. We postulate that GANmapper could be used as a solution to create datasets that approximately reveal the urban form, which despite the synthetic nature of the data, may be found valuable by various use cases such as population estimation, urban morphology, energy, and climate simulations (Xu et al, 2017;Wang et al, 2017;Yuan et al, 2019;Wang et al, 2020;Fleischmann et al, 2021;Barbour et al, 2019;Schug et al, 2021;Shang et al, 2021). For many of these applications, the exact geometry of each building is not essential, and such applications benefit from aggregated building data (e.g.…”

Section: Discussionmentioning

confidence: 99%

GANmapper: geographical data translation

Wu,

Biljecki

2021

Preprint

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We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints, and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables generating approximate maps of the urban form, and it is generalisable to augment other types of geoinformation, enhancing the completeness and quality of spatial data infrastructure. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data.

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“…The morphological analysis had the goal of visually comparing and interpreting the middle-class mass housing phenomenon from an architectural and urban perspective. This study is part of a long tradition of morphological analysis of cities (Oliveira, 2016;Fleischmann, 2022) and visualises the qualities of the existing structures, the potential of common spaces, and the impact of future interventions (Dragutinovic et al, 2023).…”

Section: Methods 01: Mass Studymentioning

confidence: 99%

Evaluation & Criticism

Benbernou,

Como,

Harea

et al. 2023

docomomo

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The COST-Action (CA 18137) on Middle Class Mass Housing in Europe (MCMH-EU) has established a transnational scientific network to document the productions of middle-class mass housing built in Europe since the 1950s in order to investigate this specific topic and share knowledge. Considering that middle-class mass housing dominates most of our cities, the research translates into the study of the extensive development of cities in Europe after World War II. The breadth of the theme and the differences between the countries make it difficult to construct a systematic and unified criticism of middle-class mass housing, albeit concentrating on the post-war period. The COST-Action has the goal to build a network to gather research representing the pooled knowledge and experiences from the network of multidisciplinary researchers. So, transversally throughout the Working Groups, an inventory of case studies, a collection of articles, and studies on the policies were produced. This paper elaborates on the collected and produced material and data in order to trigger comparisons and reflections on the approaches and methodologies to face the complexity of middle-class mass housing topic. The comparison was built by using different methods intersecting multiple points of view and following specific thematic tracks that seek to deconstruct the complexity of the middle-class mass housing topic into singular aspects. This paper presents the results of data analyses, visualisation techniques and comparative studies to identify massification processes, morphological structures, demographic and policy developments. It shows a combination of several methods to build a cross-sectional and systematic approach to the diverse knowledge envisioned to develop a methodology for future research. This can be especially useful for future developments and insights towards joint or individual European guidelines, laws and policies to improve the dilapidated housing stock, current housing situation and to compete the housing crisis in general.

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Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science

Cited by 32 publications

References 66 publications

Methodological foundation of a numerical taxonomy of urban form

Methodological foundation of a numerical taxonomy of urban form

GANmapper: geographical data translation

Evaluation & Criticism

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