Street network structure and household activity spaces

Parthasarathi, Pavithra; Hochmair, Hartwig H.; Levinson, David

doi:10.1177/0042098014537956

Cited by 37 publications

(17 citation statements)

References 41 publications

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“…Our measures of sprawl, or related ones such as intersection density, are commonly used in urban planning and transportation research (12)(13)(14)(15). However, the literature offers many alternative measures, such as density, contiguity of the built-up area, segregation of land uses, and urban design.…”

Section: Measuring Street-network Sprawlmentioning

confidence: 99%

A century of sprawl in the United States

Barrington‐Leigh

Millard‐Ball

2015

Proc. Natl. Acad. Sci. U.S.A.

160

108

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The urban street network is one of the most permanent features of cities. Once laid down, the pattern of streets determines urban form and the level of sprawl for decades to come. We present a highresolution time series of urban sprawl, as measured through street network connectivity, in the United States from 1920 to 2012. Sprawl started well before private car ownership was dominant and grew steadily until the mid-1990s. Over the last two decades, however, new streets have become significantly more connected and grid-like; the peak in street-network sprawl in the United States occurred in ∼1994. By one measure of connectivity, the mean nodal degree of intersections, sprawl fell by ∼9% between 1994 and 2012. We analyze spatial variation in these changes and demonstrate the persistence of sprawl. Places that were built with a low-connectivity street network tend to stay that way, even as the network expands. We also find suggestive evidence that local government policies impact sprawl, as the largest increases in connectivity have occurred in places with policies to promote gridded streets and similar New Urbanist design principles. We provide for public use a county-level version of our street-network sprawl dataset comprising a time series of nearly 100 y.road network | urban sprawl | transportation | policy | climate

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Section: Measuring Street-network Sprawlmentioning

confidence: 99%

A century of sprawl in the United States

Barrington‐Leigh

Millard‐Ball

2015

Proc. Natl. Acad. Sci. U.S.A.

160

108

View full text Add to dashboard Cite

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“…Mathematical models of street networks, called graphs, have grown ubiquitous in the urban studies literature in recent years as they have been used to model household travel behavior, access and equity, pedestrian volume, urban design patterns, spatial morphology, and location centrality and polycentricity (Marshall and Garrick, 2010;Dessemontet et al, 2010;Porta et al, 2012;Lee and Sohn, 2014; * Cite as: Boeing, G. 2018. Planarity and Street Network Representation Porta et al, 2014;Marshall et al, 2014;Hajrasouliha and Yin, 2015;Parthasarathi et al, 2015;Knight and Marshall, 2015;Xiao et al, 2016;Zhong et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Planarity and street network representation in urban form analysis

Boeing

2018

Environment and Planning B: Urban Analytics and City Science

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Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. It develops two new indicators-the Spatial Planarity Ratio and the Edge Length Ratio-to measure planarity and describe infrastructure and urbanization. While some street networks are approximately planar, we empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

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“…Spatial networks such as streets, paths, and transit lines organize the human dynamics of complex urban systems. They shape travel behavior, location decisions, and the texture of the urban fabric (Jacobs, 1995;Levinson & El-Geneidy, 2009;Parthasarathi, Hochmair, & Levinson, 2015). Accordingly, researchers have recently devoted much attention to street network patterns, performance, complexity, and configuration (Barthelemy, Bordin, Berestycki, & Gribaudi, 2013;Batty, 2005;Masucci, Stanilov, & Batty, 2013).…”

Section: Introductionmentioning

confidence: 99%

Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

Boeing

2018

Preprint

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Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city's spatial logic and order. Measures of entropy can reveal a city's streets' order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city's typical street segment length, average circuity, average node degree, and the network's proportions of four-way intersections and dead-ends. It also develops a new indicator of orientation-order that quantifies how a city's street network follows the geometric ordering logic of a single grid. It finds significant statistical relationships between a city's orientation entropy and other indicators of spatial order, including street circuity and measures of connectedness. These indicators, taken in concert, help reveal the extent and nuance of the grid. On average, the US/Canada study sites are far more grid-like than those elsewhere, exhibiting less entropy and circuity. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating complex urban transportation system patterns and configurations around the world.

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Street network structure and household activity spaces

Cited by 37 publications

References 41 publications

A century of sprawl in the United States

A century of sprawl in the United States

Planarity and street network representation in urban form analysis

Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

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