On the discovery of urban typologies: data mining the many dimensions of urban form

Abstract

The use of typomorphology as a means of understanding urban areas has a long tradition amongst academics but the reach of these methods into urban design practice has been limited. In this paper we present a method to support the description and prescription of urban form that is contextsensitive, multi-dimensional, systematic, exploratory, and quantitative, thus facilitating the application of urban typomorphology to planning practice. At the core of the proposed method is the k-means statistical clustering technique to produce objective classifications from the large complex data sets typical of urban environments. Block and street types were studied as a test case and a context-sensitive sample of types that correspond to two different neighbourhoods were identified. This method is suitable to support the identification, understanding and description of emerging urban forms that do not fall into standard classifications. The method can support larger urban form studies through consistent application of the procedures to different sites. The quantitative nature of its output lends itself to integration with other systematic procedures related to the research, analysis, planning and design of urban areas.

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

“…The data-driven approach was applied in identifying urban form typologies in urban contexts in Europe (Berghauser Pont and Olsson, 2017; Bobkova, 2019; Gil et al, 2012; Schirmer and Axhausen, 2016), North America (Song et al, 2013; Vialard, 2013), and Asia (Asami and Niwa, 2008). These studies have diverse definitions of the basic spatial unit (BSU), form components, and clustering methods.…”

Measuring the superblock based on a hierarchy matrix of geometry, configuration, network, and area: The case of Nanjing

“…(Song et al, 2021) classified the built form with an access structure composed of three types of voids: street spaces, areas, and rooms. In recent years, the integration of geometry and configuration to classify morphological types has become an obvious trend, being used by, for example, Berghauser Pont et al, 2019;Fusco and Araldi, 2018;Gil et al, 2012;Vialard, 2014. This study constructs a comprehensive framework of the morphological characteristics of a superblock by matrixing the two coordinates of perspective (geometry and configuration) and object (area and network). This helps better integrate hierarchical features through multidimensional pattern recognition and provides a platform for quantifying the descriptions.…”

“…(Song et al, 2021) classified the built form with an access structure composed of three types of voids: street spaces, areas, and rooms. In recent years, the integration of geometry and configuration to classify morphological types has become an obvious trend, being used by, for example, Berghauser Pont et al, 2019; Fusco and Araldi, 2018; Gil et al, 2012; Vialard, 2014.…”

The shape and size of urban blocks

“…However, blocks associated with altstadt , conjoint , and distributory street patterns (Marshall, 2005) come in a wide variety of shapes and cover large proportions of urban areas (Shpuza, 2009). Several studies, ranging in scope from the typo-morphological classification of urban form (Dibble et al, 2019; Fleischmann et al, 2020) to urban modeling (Schirmer and Axhausen, 2015), have considered, apart from the area , various aspects of block shape including perimeter and shape factor (Barthelemy, 2017; Barthelemy and Flammini, 2008; Lämmer et al, 2006; Serra et al, 2017; Strano et al, 2012), length-width , area-perimeter ratios , and circularity (Gil et al, 2012; Rashid, 2017; Serra et al, 2018); shape complexity based on the visibility among contour points (Laskari et al, 2008; Psarra and Grajewski, 2001), compactness (Marshall et al, 2019), shape index , and fractal dimension (Hermosilla et al, 2014), and square compactness , dispersion , and elongation (Vialard, 2013). The review of the literature to date shows that the relationship between the shape and size of blocks and the properties of street networks has not been fully examined due to the reliance on indices that do not adequately capture the shape complexity, and importantly, having used single indices at a time rather than coupled.…”