Walking is underrepresented in large area models of urban behavior, largely due to difficulty in obtaining data and computational issues in representing land use at such a small scale. Recent advances in data availability, like the ubiquitous point-of-interest data collected by many private companies, as well as a worldwide dataset of local streets in OpenStreetMap, a standard format for obtaining transit schedules in GTFS, etc, provide the potential to build a scalable methodology to understand travel behavior at a pedestrian scale which can be applied wherever these datasets are available.

In addition, the recent invention of fast network algorithms like Contraction Hierarchies greatly reduce related computational issues, as most network computations in this work are computable in less than a second. This thesis is a presentation of such a scalable methodology, which uses widely available datasets wherever possible, with computations that run quickly to encourage exploration of nuance in urban behavior and transparency of outcomes.

Additionally, indexes like WalkScore have been widely studied in the literature recently, both to predict walking behavior and real estate home values. This dissertation takes the position that WalkScore does not sufficiently support the set of destinations it includes, the weights that are applied, the distance decay function, and most importantly does not account for variation in behavior based on the demographics of the traveler. It is also likely that the use of destinations like coffee shops and bookstores in the index measures a specific kind of walkability that embeds a certain kind of neighborhood into its definition.

This dissertation improves on similar indexes like WalkScore by estimating a model that represents the substitution of destinations around a location and between the modes of walking, automobile, and transit. This model is estimated using the San Francisco Bay Area portion of the 2012 California Household Travel Survey to capture observed transportation behavior, and accounts for the demographics included in the survey. These representations of travel behavior can then be used as right-hand side variables in other urban models: for instance, to create a residential location choice model where measures of accessibility and available demographics are used to understand why people choose to live where they do.

In all cases, location choice models - both destination choice and residential location choice - use a level of detail not common in the literature in order to accurately represent walkability. This dissertation proposes the concept of "street node geography" which uses the local street network to define the geography with which to perform aggregations in the city. In this conceptualization, land uses and other urban data are mapped to their nearest street intersections, and overlapping aggregations are performed along the street network up to a given horizon distance. This representation of urban space is equivalent to a voronoi diagram around the intersections of the local street network, and can be thought of as having automatically generated set of 226,000 micro-zones in the San Francisco Bay Area. Street node geography thus provides a novel compromise between detail and performance for the kinds of computations performed here.

This dissertation is organized into four topics, one for each of chapters 2-5. The first topic establishes a framework for measuring the network of destination opportunities in the city for each of the walking, transit, and auto transportation modes. Destinations in the form of parcels and buildings, businesses, population, and points of interest are tied to each network so that the distance from each location to every destination can be computed by mode. The use of a points-of-interest dataset as the set of public-facing destinations is novel in the context of a traditional travel demand destination model.

This chapter also creates a case study model of trip generation for home-based walking trips is the 2012 California Household Travel Survey. This model finds that WalkScore is predictive of walking trips, that residential density and 4-way intersections have an additional but small impact, and that regional access by the transit network has a synergistic effect on walking, but regional access by auto has no impact when controlling for regional access by transit.

The second topic engages with the question of the impact of accessibility to local amenities on home values. Although early research has found that the composite index WalkScore is positively correlated with home values, this dissertation unpacks the impact of each category of destination used in WalkScore (as well as several others) on home values. The model shows that some amenities are far more predictive of home values in the datasets used here; in particular, cafes and coffee shops tend to be the indicator of neighborhood-scale urban fabric that has the largest positive relationship with home values, where a one standard deviation increase in access to cafes is associated with a 15\% increase in home values.

Although the previous topic provides some evidence that walkable amenities are related to increased home values with the datasets analyzed here, it does not prove that households are valuing walking to these amenities; it is equally plausible that households are capitalizing short driving trips into increased home values. The third topic thus creates a nested mode-destination model for each trip purpose (with destinations nested into modes) so that the logsums of the lower nest give an absolute measure of the accessibility by mode for each purpose for each location in the region.

These logsums are then weighted by the number of trips made for each purpose, and segmented by income and weighted by the incomes of the people that live at each location in the city. The result is an index based only on empirically observed behavior (in this case, the primary dataset is the 2012 CHTS) which is an absolute measure of walking behavior, not just of walkability. The methodology from this chapter yields an index for all three modes, and all indexes are included in the hedonic model described above. The model shows that a one standard deviation change in the auto index has the largest impact on home values, but that the walking index is positive, statistically significant, and almost as large. Although part of the reason for this finding might be that these neighborhoods are undersupplied, where they exist they are clearly in high demand.

The fourth topic then engages with the question of how many people actually value walking when making the residential location choice decision. In this section, latent class choice models are used so that coefficients on the three mode-specific indexes (and other neighborhood descriptors) are allowed to change based on selection into unobserved classes. This can be thought of as a form of consumer preference segmentation for mode-specific accessibility.

The model shows that there are three large segments present in the Bay Area. One that is young and moderately high-income that selects into the walkable neighborhoods of San Francisco, Oakland, and Berkeley (13\% of households), one that is transit-oriented and selects into the relatively less-expensive neighborhoods near BART but outside the urban core (37\% of households), and one that is composed of middle class families that prefers the idyllic suburbs outside San Francisco (50\% of households). Apparently about 50\% of Bay Area households value transit access, likely because BART allows commute access to the thriving labor market in the urban core (e.g. the SOMA neighborhood which is the target of so much venture capital in the region).

The main research question explored by this methodology is the question of the size of the segment of the population that is positively affected by walking accessibility for the residential location choice and the results show that this segment exists but is of modest size. However, a major finding of this research is that for planning interventions that seek to increase travel by active modes, members of the transit-oriented segment might have the most latent potential to change their behavior. Perhaps creating denser and more walkable environments around the less expensive neighborhoods near BART stations in the region could relieve pressure on the San Francisco housing market as well as create walkable environments for the lower middle class that appear to be a major component of residential demand in the region.

A ripe area for future research is to perform a gap analysis that compares neighborhoods that are high probability areas for each of the three classes presented here to test for the impact of increases in transit service and pedestrian infrastructure on both the residential location choice and travel behavior. Taking into account the heterogeneity of preferences explored here, the result of such a study would target the locations that could have the highest impact on sustainable behavior for the smallest amount of public investment.

This dissertation explores changes in cities at the onset of a new millennium from different planning prisms, perspectives and geographies. Planning is about accommodating to–even shaping–long term political-economic trajectories. Cities grow, bringing challenges to managing infrastructures, politics and even neighborhoods as they are affected by new waves of infill development, encouraged by state-level policies critical of the sprawl paradigm as it evolved in the post-war years. Other cities lose population due to complex changes in the economy and beyond. Chapter 1 engages with urban modeling knowledge, from the perspective of a regional planning agency charged with making sense of the changing region. Chapter 2 examines the potential for spillovers of new market rate housing on local prices for ownership housing, while Chapter 3 studies a change in how housing subsidies are assessed for voucher holders for rental housing. Lastly, Chapter 4 engages the conceptual debate of shrinking cities, arguing for appropriate analytical clarity.

Chapter 1 engages growth projections as a piece of planning practice. Carrying out growth projections decades into the future is often a highly technical exercise involving models based on complicated econometric relationships, estimated based on observed data for the region in question. At the same time, the future of a region is not exclusively a quantitative exercise but a decidedly normative one related to the core of the planning discipline: looking forward, setting design parameters, anticipating challenges and addressing them, or making the future more than just describing it. Thus the core dilemma of the projections work: between describing the world such as it is and how it might evolve, and the world such as it should be, in a normative-planning sense. Questions of urban epistemology abound: How to make “objective“ projections when the future is so much of a normative, design driven exercise? Based on review of historical documents as well as observation from within the agencies charged with preparing projections, we analyze the long term shift in projections as a planning practice for the San Francisco Bay Area. We characterize the projections work as straddling different knowledge domains, and highlight key analytical dilemmas in their preparation, particularly as the role of projections has transitioned from being a standalone informational report to being part of a state-mandated regional planning process with higher stakes and visibility. We identify different styles between the agencies working to prepare them, themselves caught between quantitative modalities and more design-driven sensibilities. The work of projecting, while having substantial technical features, cannot be reduced to a technical exercise alone, and must embrace the complications of being neither purely technical or purely design-focused, but a complex hybrid knowledge product requiring buy-in from a large number of stakeholders.

Chapter 2 focuses on housing spillovers. California is in the midst of a state-driven redirection of regional planning with a joint focus on realigning the state’s urban regions towards more infill development as a way to handle transportation demand and reduce greenhouse gases. As infill is encouraged and envisioned as a way to solve planning challenges, questions arise about what the impact of infill on those areas will be: As displacement and gentrification concerns are voiced by many, will the new additions to the housing stock help lower housing prices in those neighborhoods, or might it conversely be a contributor to gentrification through theoretically plausible spillover effects? Empirically, we focus on spillovers, seeking to measure the effect of new supply on sales of the existing multi-family housing stock. Using data from 20 years of arms-length home sales, I examine the San Francisco Bay Area housing market using a series of hedonic regressions relating sales prices to characteristics of not just transacting properties and neighborhoods in general, but with an emphasis on access to new housing in particular. Employing both continuous and discrete difference-in-differences estimators I find support for the conventional view that more housing production leads to a modest decrease in nearby prices, with some exceptions when assessing heterogenous effects of different price tiers of development. Findings suggest new housing is critical, but much caution will be needed to ensure neighborhood price stability of the existing stock.

Chapter 3 engages rental markets following a change in U.S. Department of Housing and Urban Development’s (HUD) technical process for determining rent subsidies. The transition from metro-scale to the detailed Small Area Fair Market Rent (SAFMRs) holds great promise in offering subsidies to voucher holders that can better match prices in actually existing housing markets, with much local variation. This potentially opens up more high- opportunity areas to the program’s users. A large-scale assessment of this key rental housing policy has been difficult due to paucity of current national yet sufficiently local, datasets describing rental housing markets. Using recent and spatially comprehensive rental data from Craigslist, a listing website that includes housing, we analyze HUD data for 2,600 FMR areas nationwide and show rental gaps between the actual cost of rentals and the subsidy ceiling. We report on both the areas selected for the SAFMRs as well as those not selected. Based on our findings, we argue that more areas should be included in the program if appropriate safeguards can be instituted.

Chapter 4 engages critically with the concept of shrinking cities. Shrinking cities on several continents beset with sustained population losses have been the focus of a number of studies in the past decade, marking an increasing awareness that growth should not be the only preoccupation of planners. Since the Great Recession of 2006-2008, research has widened the map and shown shrinkage even in the Sun Belt cities of California in connection with the recession, leading some researchers to conclude new geographic fault lines for shrinkage. While these works, which have provided such information, are welcome additions to the literature, in this study I will proceed from the observation that the term ”shrinkage” has been used for cities as diverse as Flint, Michigan and San Francisco and San Jose in California. Consequently, I will examine the concept of shrinkage and argue that, while the term’s heterogeneity and flexibility are crucial to the productive employment of the concept, we must, nevertheless, tighten its definition and its application. Otherwise, we risk watering it down to the point where it is no longer useful to describe the vastly different trajectories of differing cities. The study will conclude with reflections on the appropriateness of local scale to address shrinkage.

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.

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 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 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.

Over the last decade, a shift in mobility has occurred to accommodate different scales and purposes of travel, with accessibility becoming a more prominent objective. Micromobility, such as scooters and e-bikes, are beginning to redefine local travel. Transportation network companies, such as Uber and Lyft, are questioning the need for private vehicle ownership. Bus rapid transit and magnetic levitation are transforming the efficiency of public transit. Autonomous vehicles are attempting to reimagine the commute to accommodate medium-distance trips in the comfort of an automobile without the stress and lost time of driving. Urban air mobility as a concept is also becoming more popular, as it seeks to decrease the travel times for long-distance trips in sprawled and congested metropolitan areas. In short, the new key to mobility is multimodality. This dissertation focuses on the impact of urban air mobility on metropolitan transportation networks.

Introducing a new mobility mode into a region has enormous effects across a variety of metrics, including congestion, equity, sustainability, economic productivity, and long-term travel behavior and land use decisions. While these objectives are often intertwined and complex, it is valuable to initially investigate each independently to understand upper and lower bounds. Using simulation, this dissertation attempts to unpack the impact of urban air mobility on travel behavior and congestion specifically, developing the foundational software architecture for future scenario planning and transportation analysis. The preparedness of public agencies for urban air mobility is also investigated, underscoring the need for the simulation tools developed in this dissertation as decision support systems.

Microsimulation Analysis for Network Traffic Assignment (MANTA), a parallelized, GPU-based microsimulation platform, is presented as an agile, modular, and extensible contribution to the transportation simulation and modeling literature. Initial modeling of urban air mobility is conducted with MANTA, and a holistic modeling framework of ground traffic, ground-air coordination, and aerial flight, known as SimUAM, is then presented. The dissertation concludes with boots on the ground, revealing which public agencies in the United States are prepared in planning and policy for integrating urban air mobility into their transportation networks.

The research contained herein is comprised of three studies, each of which utilizes a unique set of population-wide microdata to address a few of the many challenges that arise when statistical models are used to study the complex dynamics that govern the function and evolution of urban systems. These challenges range from the technical to the political, but they are all highly relevant to the management of the modern metropolis in which “data- driven” decision-making is increasingly the norm. Taken together, these studies draw upon relevant literature from the fields of urban geography, behavioral economics, and computa- tional statistics, among others, to synthesize a body of work which questions what can and cannot be achieved through urban systems modeling while simultaneously making several contributions to its study and its practice.

The first study demonstrates how thoughtful model design can be exploited to make powerful inferences about an understudied urban economic policy whose costs are primarily borne by the most vulnerable inhabitants of our cities. In particular, it identifies the first causal estimate of the effect of rent control status on eviction filing rates in the scientific literature. A 10-year dataset of eviction notices (n=21,806) is combined with a complete history of property tax records (n=1,978,687) in a regression discontinuity design to estimate a local average treatment effect of 0.013 evictions per residential unit per year conditioned on positive rent control status. Compared to the baseline rate of eviction notices over this same period, this translates to a 240% increase in the likelihood of eviction for tenants living in controlled units. I argue that this finding is best understood not as an inherent inefficiency of rent control policy in general, but rather as the result of specific state-wide laws, passed in the years following the adoption of rent control in San Francisco, which granted rent controlled property owners an economic incentive to evict and the legal means to do so. The chapter concludes by making specific policy recommendations which city officials can enact today in order to protect its most vulnerable residents. In the chapter that follows I address one of the myriad challenges that arise once a model, having been thoughtfully designed and estimated, is used to make inferences about the future rather than the past. The issue of simulation error due to sampling of alternatives in discrete choice models is an important one, not only because it represents a longstanding criticism of the statistical validity of many state-of-the-art microsimulation models, but also because it points to a significant disconnect in the development of methods for estimation and for simulation. This disconnect reflects a larger rift that exists between theory and practice in urban systems modeling. The purpose of this chapter is to help bridge that gap by defining a novel measure of forecast error and using it to quantify the extent of the problem as it manifests in a disaggregate model of discretionary location choice, typical of those that are commonly found in use today. The definition of this metric is itself a valuable contribution to a discipline which is often maligned for its inability to assess the accuracy of its methods. I am also able to demonstrate this value by using the metric to identify two key findings. First, I show that the proportion of aggregate demand which is misallocated due to sampling of alternatives is actually reduced as the size of the universe of alternatives increases (i.e. becomes more disaggregate). Secondly, I find that in most scenarios, simple random sampling actually outperforms the importance sampling approach in terms of simulation error due to sampling of alternatives. Both results contradict the traditional wisdom about best practices in microscopic models of travel and land use demand.

The final study included here presents a method designed to make it easier for researchers and practitioners alike to acquire the kinds of data required to perform microscopic urban mod- eling. Unlike households and persons, no public repository of establishment-level microdata currently exists for businesses in the United States. Work in this domain has therefore been primarily limited to those with the resources to purchase expensive commercial datasets. As a result, the development of disaggregate models of business and firm dynamics has lagged behind their person- and household-based counterparts, hindering the development of fully integrated transportation and land use microsimulation systems as a whole. Drawing on recent advances in the application of Bayesian networks to population synthesis and data privacy preservation, I estimate a series of probabilistic models on a dataset of proprietary business establishment listings and use them to generate synthetic populations which match the joint distributions of key characteristics from the original data but contain none of the original records themselves. In a second analysis I show how aggregate Census data can be used as control totals for sampling from the fitted models to create synthetic populations that match the aggregate Census counts but have a much richer set of features than what is made publicly available by the Census. In theory, these data, along with the fitted models themselves, can be shared freely among collaborators without fear of copyright violation or disclosure of private data. These results demonstrate the great potential of Bayesian net- works, and probabilistic models in general, to democratize access to microdata and thereby facilitate greater scientific collaboration in the field of disaggregate urban systems modeling.

I conclude with a brief summary of these findings and discuss their relation to current issues in urban systems modeling. I identify several opportunities to improve and build on the work presented here, while also addressing the inherent limitations of model-based research to meet the most pressing needs of our urban communities at this moment in history.