Methods and Measures for Analyzing Complex Street Networks and Urban Form

Abstract

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. 2 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 ...

Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

“…Given this background and motivation, this project originally developed a new open-source Python-based software toolkit called OSMnx [45]. OSMnx can download data from OpenStreetMap using configurable user queries, then construct a nonplanar, directed multigraph, and finally clean the topology [79]. Python was chosen to develop this tool for three reasons.…”

Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

“…Python-based software toolkit called OSMnx [45]. OSMnx can download data from OpenStreetMap using configurable user queries, then construct a nonplanar, directed multigraph, and finally clean the topology [79]. Python was chosen to develop this tool for three reasons.…”

An Integrated Pipeline Architecture for Modeling Urban Land Use, Travel Demand, and Traffic Assignment

“…OSMnx is built on top of NetworkX, a Python package for network analysis developed by researchers at Los Alamos National Laboratory. OSMnx extends NetworkX's network analysis capabilities by working explicitly with spatial infrastructure networks and interfacing with OpenStreetMap's various APIs (Boeing, 2017a). It can automatically construct topologically-corrected nonplanar directed multigraphs constrained to any polygonal boundaries for anywhere in the world from OpenStreetMap data.…”