A multiscale classiﬁcation of urban morphology

Schirmer, Patrick; Axhausen, Kay W.

doi:10.5198/jtlu.2015.667

Cited by 34 publications

(37 citation statements)

References 20 publications

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“…When observing the best partitions of the 27 best number of clusters, both numerically and graphically ( Figure 2), it is observed that there is a certain disparity between them. A significant repetition of the partition of two profiles can be highlighted, confirming that the partition in two profiles tends to be the most frequent in morphological analysis [40].…”

Section: Each Of the Urban Fabrics Represented Insupporting

confidence: 62%

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

2019

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In recent decades, the concept of urban density has been considered key to the creation of sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has been observed in defining valid measurement indicators universally. With the intention of identifying the variables that allow the best characterization of the shape of urban fabrics and of obtaining the metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence is proposed. The main objective of this paper was to evaluate the capacity and interest of such a methodology from standard indicators of the built density, measured at various urban scales, (i) to cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to obtain the metrics that characterize them with an identity. As a case study, this methodology was applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13 regular parameters to qualify urban shape and density. It was verified that the profiles obtained were more robust than those based on a limited number of indicators, evidencing that the proposed methodology offers operational opportunities in urban management by allowing the comparison of a fabric with the identified profiles.

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Section: Each Of the Urban Fabrics Represented Insupporting

confidence: 62%

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

2019

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“…anthropological contexts (Krebs and Pilz 2013). As Tagliaventi (2016) suggests to distinguish between different building types within the urban Transect Zones, it might be recommendable from a geographers point of view to analyse the urban morphology along a transect, classified by building, composition and neighbourhood typologies as documented in Schirmer and Axhausen (2015). Bertaud (2018) suggests a number of indicators to manage developments with relevance to urban planning, such as "rentto-income ratio, floor consumption per capita, and median commuting time", the latter corresponding to the accessible labour market.…”

Section: Discussionmentioning

confidence: 99%

Urban Transects and Trunk Roads: Observations in Hamburg

Klein¹,

Clausen²

2020

KN J. Cartogr. Geogr. Inf.

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The International Hamburg Bauforum, a periodic workshop for several 100 s of architects and town planners, in 2019 focused on "Magistralen", trunk roads with large traffic volumes that shape the adjacent urban fabric in manifold ways. This study aimed to support the Bauforum with qualitative background about the land use and socioeconomic patterns along the road axis. The longitudinal examination resembles the urban-to-rural-transect school of thought in urban planning and supplies quantitative empirical data to support the corresponding discussions. Our study shows that the transect approach, applied to a radial trunk road, is a helpful systematic approach to analyse a succession urban space along the whole of the road and determine sections that deserve more detailed analysis. Further, we see considerable potential of the approach to illustrate and explain sociodemographic and economic developments of the city as a whole, especially using data and comparing the transect diagrams from different points in time. ZusammenfassungDas Internationale Hamburger Bauforum, ein regelmäßiger Workshop an dem mehrere 100 Architekten und Stadtplaner teilnehmen, konzentrierte sich 2019 auf die "Magistralen", verkehrsreiche Fernstraßen, die das angrenzende Stadtgefüge auf vielfältige Weise prägen. Ziel dieser Studie war es, dass Bauforum mit qualitativem Hintergrund über die Flächennutzung und sozioökonomischen Muster entlang der Straßenachsen zu unterstützen. Die Längsschnittuntersuchung ähnelt der urbanländlich-Transekt Denkschule in der Stadtplanung und liefert quantitative empirische Daten zur Unterstützung der entsprechenden Diskussionen. Unsere Studie zeigt, dass der Transekt-Ansatz, angewandt auf eine radiale Fernstraße, ein hilfreicher systematischer Ansatz ist, um einen städtischen Sukzessionsraum entlang der gesamten Straße zu analysieren und Abschnitte zu bestimmen, die eine detailliertere Analyse verdienen. Darüber hinaus sehen wir ein erhebliches Potenzial des Ansatzes, um die soziodemographische und wirtschaftliche Entwicklung der Stadt als Ganzes zu veranschaulichen und zu erklären, insbesondere durch die Verwendung von Daten und den Vergleich der Transektdiagramme aus verschiedenen Zeitpunkten.

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“…However, since urban form has a diverse range of influences (Schwarz, 2010;Batty and Longley, 1994), many studies also include socio-economic indicators (Kasanko et al, 2006;Tsai, 2005;Frenkel and Ashkenazi, 2008) when defining urban form. These socio-economic metrics include social processes in the analysis of urban form (Lima, 2001), representing the built environment by the distribution of socio-economic variables (Schirmer and Axhausen, 2015). This attempts to assess how human behaviour varies in space (Schirmer and Axhausen, 2015;Gil, 2016) and the impact of human behaviour on urban form.…”

Section: Urban Formmentioning

confidence: 99%

“…These socio-economic metrics include social processes in the analysis of urban form (Lima, 2001), representing the built environment by the distribution of socio-economic variables (Schirmer and Axhausen, 2015). This attempts to assess how human behaviour varies in space (Schirmer and Axhausen, 2015;Gil, 2016) and the impact of human behaviour on urban form. For example, how travel behaviour is influenced by population density.…”

Section: Urban Formmentioning

confidence: 99%

“…As mentioned in "Urban Form" subsection, although definitions of urban form vary in the literature (Schwarz, 2010;Tsai, 2005), both landscape metrics and socio-economic indicators were considered in the selection of urban characteristics to search for correlations with energy consumption. Landscape metrics enable the understanding of land use change and quantification of urban sprawl (Kasanko et al, 2006;Dieleman and Wegener, 2004), whereas socio-economic indicators include social processes and spatial behaviour in the analysis (Lima, 2001;Schirmer and Axhausen, 2015;Gil, 2016) Several urban form definitions were considered, based on previous studies (Schwarz, 2010;Huang et al, 2007;Bhatta, 2010;Kasanko et al, 2006;Schneider and Woodcock, 2008;Tsai, 2005). The following measures were selected (Table 1), taking into consideration previous research, data availability at the scale of analysis and significance to the study of energy consumption:…”

Section: Urban Form Metricsmentioning

confidence: 99%

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Understanding the Relationship between Energy Consumption and Urban Form

Osorio¹,

McCullen²,

Walker³

et al. 2017

AJS

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A large proportion of energy demand comes from urban areas, mostly from buildings and transport, the use of which has impacts on climate and air quality through the emissions of greenhouse gases and other pollutants. The research in this paper investigates the relationship between the selected urban form characteristics and energy consumption in England, in order to understand how one influences the other. The influence of urban form is recognized in many aspects of cities, such as human behavior and transport dynamics. Consequently, it is also expected to have a significant impact on energy consumption and to be a key component in future urban sustainability. Urban energy consumption is calculated at a large geographic scale of analysis combining the consumption of both buildings and commute transport. Urban form indicators are obtained for the same land-parcels and correlations between the two calculated. The results demonstrate that a variety of urban form characteristics influence energy consumption. Some measures show little correlation with energy consumption, whereas other density measures show a significant scaling relationship. Therefore, density indicators such as population density are suggested as a means to explain urban energy consumption. Additionally, the results reveal that the relationship between energy consumption and urban characteristics follows a sublinear scaling relationship and hence show an economy of scale. This analysis suggests that better energy efficiency is achieved by areas with higher population density, which provides new insights to urban policy-makers and planners seeking to design strategies to cut carbon emissions and energy consumption.

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A multiscale classiﬁcation of urban morphology

Cited by 34 publications

References 20 publications

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

Urban Transects and Trunk Roads: Observations in Hamburg

Understanding the Relationship between Energy Consumption and Urban Form

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