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Methods and Measures for Analyzing C...

Boeing, Geoffrey D.

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Methods and Measures for Analyzing Complex Street Networks and Urban Form.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods and Measures for Analyzing Complex Street Networks and Urban Form./
作者:	Boeing, Geoffrey D.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	236 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
Contained By:	Dissertation Abstracts International78-11A(E).
標題:	Urban planning. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10277034
ISBN:	9780355032093

Methods and Measures for Analyzing Complex Street Networks and Urban Form.
Boeing, Geoffrey D.

Methods and Measures for Analyzing Complex Street Networks and Urban Form. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 236 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Recent years have witnessed an explosion in the science of networks. Much of this research has been stimulated by advances in statistical physics and the study of complex systems---that is, systems that comprise many interrelated components whose interactions produce unpredictable large-scale emergent behavior. Cities are complex systems formed both through decentralized, bottom-up, self-organizing processes as well as through top-down planning interventions. Humans shape their urban ecosystems (the built environment, institutions, cultures, etc.) and are in turn shaped by them. Cities comprise numerous interdependent components that interact through networks---social, virtual, and physical---such as street networks.

ISBN: 9780355032093Subjects--Topical Terms:

2122922
Urban planning.

Methods and Measures for Analyzing Complex Street Networks and Urban Form.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
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502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
520 $a Recent years have witnessed an explosion in the science of networks. Much of this research has been stimulated by advances in statistical physics and the study of complex systems---that is, systems that comprise many interrelated components whose interactions produce unpredictable large-scale emergent behavior. Cities are complex systems formed both through decentralized, bottom-up, self-organizing processes as well as through top-down planning interventions. Humans shape their urban ecosystems (the built environment, institutions, cultures, etc.) and are in turn shaped by them. Cities comprise numerous interdependent components that interact through networks---social, virtual, and physical---such as street networks.
520 $a This dissertation examines urban street networks, their structural complexity (emphasizing density, connectedness, and resilience), and how planning eras and design paradigms shape them. Interventions into a complex system often have unpredictable outcomes, even if the intervention is minor, as effects compound or dampen nonlinearly over time. Such systems' capacity for novelty, through emergent features that arise from their components' interactions, also makes them unpredictable. These interactions and the structure of connections within a system are the subject of network science. In cities, the structural characteristics of circulation networks influence how a city's physical links organize its human dynamics. Urban morphologists have long studied the built form's complexity and, following from scholars such as Jane Jacobs and Christopher Alexander, various urban design paradigms today speak both directly and indirectly to the value of complexity in the built environment. However, these claims are often made loosely, without formally connecting with theory, implications, or evaluation frameworks.
520 $a This dissertation develops an interdisciplinary typology of measures for assessing the complexity of urban form and design, particularly emphasizing street network analytic measures. Street network analysis has held a prominent place in network science ever since Leonhard Euler presented his famous Seven Bridges of Konigsberg problem in 1736. The past 15 years have been no exception as the growth of interdisciplinary network science has included numerous applications to cities and their street networks. These studies have yielded new understandings of urban form and design, transportation flows and access, and the topology and resilience of urban street networks. However, current limitations of data availability, consistency, and technology have resulted in four substantial shortcomings: small sample sizes, excessive network simplification, difficult reproducibility, and the lack of consistent, easy-to-use research tools. While these shortcomings are by no means fatal, their presence can limit the scalability, generalizability, and interpretability of empirical street network research.
520 $a To address these challenges, this dissertation presents OSMnx, a new tool to download and analyze street networks and other geospatial data from OpenStreetMap for any study site in the world. OSMnx contributes five capabilities for researchers and practitioners: first, the downloading of political boundaries, building footprints, and elevation data; second, the scalable retrieval and construction of street networks from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including projecting and visualizing networks, routing, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as measures of the structure and topology of the network. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using non-planar, directed graphs.
520 $a This study collects and analyzes 27,000 U.S. street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales---namely, every U.S. city and town, census urbanized area, and Zillow-defined neighborhood. It presents wide-ranging empirical findings on U.S. urban form and street network characteristics, emphasizing measures relevant to graph theory, urban design, and morphology such as structural complexity, connectedness, density, centrality, and resilience. We find that the typical American urban area has approximately 26 intersections/km2, 2.8 streets connected to the average node, 160m average street segment lengths, and a network that is 7.4% more circuitous than straight-line streets would be. The typical city has approximately 25 intersections/km2, 2.9 streets connected to the average node, 145m average street segment lengths, and a network that is 5.5% more circuitous than straight-line streets would be. The typical Zillow neighborhood has approximately 46 intersections/km2, 2.9 streets connected to the average node, 135m average street segment lengths, and a network that is 4.4% more circuitous than straight-line streets would be. At all three scales, 3-way intersections are by far the most prevalent intersection type across the U.S.
520 $a We find a strong linear relationship, invariant across scales, between total street length and the number of nodes in a network. (Abstract shortened by ProQuest.).
590 $a School code: 0028.
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650 4 $a Land use planning. $3 2122760
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