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

237 Pages·2017·5.96 MB·English

UC Berkeley
UC Berkeley Electronic Theses and Dissertations
Title
Methods and Measures for Analyzing Complex Street Networks and Urban Form
Permalink
https://escholarship.org/uc/item/44s43425
Author
Boeing, Geoffrey D.
Publication Date
2017

Peer reviewed|Thesis/dissertation
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Methods and Measures for Analyzing Complex Street Networks and Urban Form

By

Geoffrey D. Boeing

A dissertation submitted in partial satisfaction of the

requirements for the degree of

Doctor of Philosophy

in

City and Regional Planning

in the

Graduate Division

of the

University of California, Berkeley

Committee in Charge:

Professor Paul Waddell, Chair
Professor Robert Cervero
Professor Elizabeth Macdonald
Professor David O’Sullivan

Spring 2017
Methods and Measures for Analyzing Complex Street Networks and Urban Form

Copyright © 2017
By
Geoffrey D. Boeing
Abstract

Methods and Measures for Analyzing Complex Street Networks and Urban Form

by

Geoffrey D. Boeing

Doctor of Philosophy in City and Regional Planning

University of California, Berkeley

Professor Paul Waddell, Chair

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.
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.
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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 Königsberg 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.
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.
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
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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.
We find a strong linear relationship, invariant across scales, between total street length
and the number of nodes in a network. This contradicts some previous findings in the
literature that relied on smaller sample sizes and different geographic contexts. We also
find that most networks demonstrate a lognormal distribution of street segment lengths.
However, an obvious exception to lognormal distribution lies in those networks that
exhibit substantial uniformity network-wide. At the neighborhood scale, examples
include downtown neighborhoods with consistent orthogonal grids, such as that of
Portland, Oregon. At the municipal scale, examples include towns in the Great Plains that
have orthogonal grids with consistent block sizes, platted at one time, and never subjected
to sprawl. These spatial signatures of the Homestead Act, successive land use regulations,
urban design paradigms, and planning instruments remain etched into these cities’ urban
forms and street networks today. Nebraska’s cities have the lowest circuity, the highest
average number of streets per node, the second shortest average street segment length,
and the second highest intersection density. These findings illustrate how street networks
across the Great Plains developed all at once and grew little afterwards – unlike, for
instance, cities in California that were settled in the same era but were later subjected to
substantial sprawl.
The characteristics of a city street network fundamentally depend on what “city” means:
municipal boundaries, urbanized areas, or certain core neighborhoods? The first is a
political/legal definition, but it captures the scope of city planning jurisdiction and
decision-making for top-down interventions into a street network. The second captures
the wider self-organized human system and its emergent built form, but tends to
aggregate multiple heterogeneous built forms together into a single unit of analysis. The
third captures the nature of the local built environment and lived experience, but at the
expense of a broader view of the urban system and metropolitan-scale trip-taking. In
short, multiple scales in concert provide planners a clearer view of the urban form and the
topological and metric complexity of the street network than any single scale can.
The emerging methods of computational data science, visualization, network science, and
big data analysis have broadened the scope of urban design’s traditional toolbox. Such
methods may yield new insights and rigor in urban form/design research, but they may
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also promulgate the weaknesses of reductionism and scientism by ignoring the theory,
complexity, and qualitative nuance of human experience crucial to urbanism. The tools
we use shape the kinds of questions we can even ask about cities. Today, the
dissemination of quantitative network science into the social sciences offers an exciting
opportunity to study the dynamics and structure of cities and urban form, but paths
forward must consider cities as uniquely human complex systems, inextricably bound up
with politics, privilege, power relations, and planning decisions.
This dissertation comprises six substantive chapters bookended by introductory and
concluding chapters. As a whole, the dissertation is divided into two primary parts. The
first comprises chapters 2 and 3 and develops the theoretical framework. Chapter 2
introduces the background of the nonlinear paradigm by discussing systems, dynamics,
self-similarity, and the nature of prediction in the presence of nonlinearity. These
foundations set up the complexity theories of cities and the study of networks presented
in chapter 3. This first part of the dissertation emphasizes the dynamics of complex urban
systems before we turn our attention to their structure in the second part. Urban
circulation networks serve as a physical substrate that underlies and organizes the city’s
complex human interactions. Chapter 4 collates various indicators of complexity from
multiple research literatures into a typology of measures of the complexity of urban form,
emphasizing the scale of urban design practice. In particular, it presents several measures
of network complexity and structure that we then operationalize in chapters 5, 6, and 7.
Methodologically, chapter 5 introduces OSMnx, a new tool to acquire, construct, correct,
visualize, and analyze complex urban street networks. Chapter 6 applies OSMnx
empirically in a small case study of street networks in Portland, Oregon. Chapter 7 then
expands the empirical application of OSMnx to a large study of 27,000 urban street
networks at various scales across the U.S. These street networks and measures data sets
have been shared in a public repository for other researchers to re-purpose.
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For Tracy and Alden:
forsan et haec olim meminisse iuvabit
i
Uneducated people who have no experience of true reality will
never adequately govern a city, and neither will people who
have been allowed to spend their whole lives in education. The
former fail because they do not have a single goal in life at
which all their actions, public and private, inevitably aim; the
latter because they would refuse to act, thinking they had
emigrated, while still alive, to the Isles of the Blessed.
—Plato, Republic

ii
Table of Contents

Table of Contents ......................................................................................................................... iii 
List of Figures .............................................................................................................................. vii 
List of Tables ................................................................................................................................. xi 
Acknowledgements .................................................................................................................... xii 
Chapter 1: Introduction ................................................................................................... 1 
1.1. Abstract ............................................................................................................................... 2 
1.2. Introduction ........................................................................................................................ 2 
1.3. Context of the Study .......................................................................................................... 4 
1.4. Motivation ........................................................................................................................... 6 
1.5. Organization and Contribution by Chapter ................................................................... 7 
1.5.1. Chapter 1 – Introduction ........................................................................................... 8 
1.5.2. Chapter 2 – Foundations of the Nonlinear Paradigm ............................................ 8 
1.5.3. Chapter 3 – Complexity and Cities ........................................................................... 9 
1.5.4. Chapter 4 – Measuring the Complexity of Urban Form and Design ................... 9 
1.5.5. Chapter 5 – Acquiring, Analyzing, and Visualizing Street Networks ............... 10 
1.5.6. Chapter 6 – Case Study: Portland, Oregon ........................................................... 11 
1.5.7. Chapter 7 – A Multi-Scale Analysis of Urban Street Networks ......................... 12 
1.5.8. Chapter 8 – Conclusion .......................................................................................... 14 
Chapter 2: Foundations of the Nonlinear Paradigm .................................................... 15 
2.1. Abstract ............................................................................................................................ 16 
2.2. Introduction ..................................................................................................................... 16 
2.3. Background and Model .................................................................................................. 18 
iii