An elastic urban morpho-blocks (EUM) modeling method for urban building morphological analysis and feature clustering

Ma, Rui; Li, Xin; Chen, Jiayu

doi:10.1016/j.buildenv.2021.107646

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…While there have been discussions about different BSU definitions in urban form studies (Fleischmann, 2019; Ma et al, 2021; Quan and Bansal, 2021), this study adopted the grid as BSU as it provides consistent scale comparisons for urban forms within the same city and across cities. The BSU of a grid also allows integration with other spatial data, such as raster-based remote sensing data (Jochem et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework

Quan

2023

Environment and Planning B: Urban Analytics and City Science

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Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and workflows, lacking a systematic framework. This study addressed these issues by developing a new form clustering framework to systematically identify form typologies at a large scale and demonstrated its application in Seoul. With a 500 m × 500 m grid as the basic spatial unit and twelve urban form attributes as learning features, 14 clusters were identified using the Gaussian mixture model. These clusters were further translated into form typologies following a semantic typology naming system, with representative form samples identified. The resulting typologies were then verified and validated through comparisons with previous studies. Their relationships with zoning classes were also examined, emphasizing their role in urban planning and design. Results suggest this new framework is an effective and promising way to identify urban form typologies in complex urban environments to better support urban planning and management.

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Section: Methodsmentioning

confidence: 99%

Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework

Quan

2023

Environment and Planning B: Urban Analytics and City Science

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“…Here, street-blocks with area >100,000 m 2 and built density <0.05 (Ma et al, 2021) were filtered out (e.g., rivers or parks). The effective blocks with area ratios of each building type, unbuilt area, and block area were prepared as input data for clustering (Table S4).…”

Section: Block Clustering Modelmentioning

confidence: 99%

“…This method has been used in various urban form studies such as neighborhoods (Francisco et al, 2017; Song and Knaap, 2007), building groups (Schirmer and Axhausen, 2013), and street blocks (Gil et al, 2012). Besides K-means, recent studies have started to explore GMM in clustering block and neighborhood types (Fleischmann et al, 2021; Ma et al, 2021). As an advanced version of k-means, GMM considers the data density and distribution, which has been applied in classification of more complicated urban forms (Huang et al, 2007; Li and Quan, 2023; Samuelsson et al, 2019; Su et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…As an advanced version of k-means, GMM considers the data density and distribution, which has been applied in classification of more complicated urban forms (Huang et al, 2007; Li and Quan, 2023; Samuelsson et al, 2019; Su et al, 2021). While many of these studies are raster-based that focus on large urban scales, other studies developed block-level spatial metrics (Araldi and Fusco, 2019; Fleischmann et al, 2021; Gil et al, 2012; Hermosilla et al, 2014; Ma et al, 2021; Vanderhaegen and Canters, 2017) to classify street blocks. Among them, Fleischmann et al (2021), Gil et al (2012), and Ma et al (2021) used GMM to cluster blocks based on spatial metrics that described block geometries and positions of buildings inside.…”

Section: Introductionmentioning

confidence: 99%

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Developing a two-level machine-learning approach for classifying urban form for an East Asian mega-city

Chen,

Koch,

Reicher

2023

Environment and Planning B: Urban Analytics and City Science

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Having had the most rapid urbanization in the world since the 1990s, mega-cities in East Asia featured highly compact and atomized modernist architecture. With densely built modernist architecture and relatively free building regulations, it is challenging to trace the actual development of the whole city. Compared to European cities, their overall urban landscapes are much denser, higher, and functionally mixed. In order to achieve a quicker and more accurate identification of urban forms in mega-cities, this study proposed a two-level machine-learning approach. At the building level, we extracted features from topographic maps and building licenses to automatically classify building types. Four state-of-the-art multi-class classification models were compared. At the block level, we used building types as input data and compared two methods for block clustering. In total 61,426 buildings from Taipei were classified and grouped into 10 block types. Different from Western cities, many of the block types in Taipei were mixtures of different types of buildings. This approach is efficient in exploring new urban form types, especially for emerging mega-cities where block types are previously unknown. The result not only sheds light on the features of East Asian urban landscapes but also serves as important basis of type-based strategic plans for contemporary urban issues.

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“…The perfection and development of digital landscape theory and technical methods also provide new research and practice tools for exploring the feature of morphological [ 23 ]. In research of urban morphology [ 24 ], settlement pattern [ 25 ], and landscape morphology [ 26 ] have accumulated a large number of indexes. Meanwhile, statistical and machine learning models are applied to morphology index analysis, expanding the visual analysis capabilities of multidimensional morphological data with large sample sizes.…”

Section: Relevant Researchmentioning

confidence: 99%

Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River

Han

et al. 2022

Computational Intelligence and Neuroscience

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Landscape morphology is a significant area of landscape architecture research. One of the scientific and technological issues in recent landscape morphology research is the use of quantitative analysis technology driven by morphology indexes and computational models to describe, compare, and analyze form features. This article focuses on the form features of the polder landscape, based on existing theoretical and practical achievements in landscape morphology. First, we choose five landscape morphology indexes based on the morphological constituent units of the landscape (elongation, rectangular compactness, concavity, ellipse compactness, and fractal dimension). Then, using the self-organizing map (SOM), we create an identification model for clustering the types of constituent units. The experimental results show that the identification model can classify polder morphology and analyze the distribution of units using typical polders in the Yangtze River’s south bank as study cases. This article presents a technical approach to polder landscape morphology classification as well as a reference and developable quantitative analysis method for landscape morphology research.

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An elastic urban morpho-blocks (EUM) modeling method for urban building morphological analysis and feature clustering

Cited by 18 publications

References 37 publications

Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework

Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework

Developing a two-level machine-learning approach for classifying urban form for an East Asian mega-city

Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River

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