Methods and Measures for Analyzing Complex Street Networks and Urban Form

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./nThis 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./nThis 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./nTo 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./nThis 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./nWe 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./nThe 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./nThe 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 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./nThis 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.

Advisor: Waddell, Paul