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Abstract This article interprets emerging scholarship on rental housing platforms—particularly the most well-known and used short- and long-term rental housing platforms—and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators’ own financial and political interests. The question of which data—and people—are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations. Policy and practice recommendations As rental housing technologies upend traditional market processes in favor of platform oligopolies, policymakers must reorient these processes toward the public good. Long-term and short-term rental platforms offer different market benefits and drawbacks, but the latter in particular requires proactive regulation to mitigate harm. At a minimum, policymakers must require that short-term rental platforms provide the information necessary for cities to enforce current, let alone new, housing regulations. Practitioners should be cautious inferring market conditions from rental housing platform data, due to difficult-to-measure sampling biases.

In 1974, Robert Caro published The Power Broker, a critical biography of Robert Moses’s dictatorial tenure as the “master builder” of mid-century New York. Moses transformed the urban fabric and transportation system of New York in a profound way, producing the Brooklyn Battery Tunnel, the Verrazano Narrows Bridge, the Westside Highway, the Cross-Bronx Expressway, the Lincoln Center, the UN headquarters, Shea Stadium, Jones Beach State Park and many other projects. However, The Power Broker did lasting damage to his public image and today he remains one of the most polarizing figures in city planning history. On August 26, 1974, Moses issued a 23-page typed statement denouncing Caro’s work as “full of mistakes, unsupported charges, nasty baseless personalities, and random haymakers.” The statement went on to gainsay several of Caro’s assertions one at a time. Robert Moses’s typewritten response survives today as a grainy photocopy from the New York City Parks Department archive. To better preserve and disseminate it, I have extracted and transcribed its text using optical character recognition, and edited the result to correct transcription errors.

City planning policies influence urban lifestyles, health, and sustainability. We assessed policy frameworks for city planning for 25 cities across 19 lower-middle-income countries, upper-middle-income countries, and high-income countries to identify whether these policies supported the creation of healthy and sustainable cities. We systematically collected policy data for evidence-informed indicators related to integrated city planning, air pollution, destination accessibility, distribution of employment, demand management, design, density, distance to public transport, and transport infrastructure investment. Content analysis identified strengths, limitations, and gaps in policies, allowing us to draw comparisons between cities. We found that despite common policy rhetoric endorsing healthy and sustainable cities, there was a paucity of measurable policy targets in place to achieve these aspirations. Some policies were inconsistent with public health evidence, which sets up barriers to achieving healthy and sustainable urban environments. There is an urgent need to build capacity for health-enhancing city planning policy and governance, particularly in low-income and middle-income countries.

Planning scholars have identified economic, safety, and social benefits of converting one-way streets to two-way. Less is known about how conversions could impact vehicular distances traveled—of growing relevance in an era of fleet automation, electrification, and ride-hailing. We simulate such a conversion in San Francisco, California. We find that its current street network’s average intra-city trip is ~1.7 percent longer than it would be with all two-way streets, corresponding to 27 million kilometers of annual surplus travel. As transportation technologies evolve, planners must consider different facets of network efficiency to align local policy and street design with sustainability and other societal goals.

An essential characteristic of a healthy and sustainable city is a physically active population. Effective policies for healthy and sustainable cities require evidence-informed quantitative targets. We aimed to identify the minimum thresholds for urban design and transport features associated with two physical activity criteria: at least 80% probability of engaging in any walking for transport and WHO's target of at least 15% relative reduction in insufficient physical activity through walking. The International Physical Activity and the Environment Network Adult (known as IPEN) study (N=11 615; 14 cities across ten countries) provided data on local urban design and transport features linked to walking. Associations of these features with the probability of engaging in any walking for transport and sufficient physical activity (≥150 min/week) by walking were estimated, and thresholds associated with the physical activity criteria were determined. Curvilinear associations of population, street intersection, and public transport densities with walking were found. Neighbourhoods exceeding around 5700 people per km 2, 100 intersections per km 2, and 25 public transport stops per km 2 were associated with meeting one or both physical activity criteria. Shorter distances to the nearest park were associated with more physical activity. We use the results to suggest specific target values for each feature as benchmarks for progression towards creating healthy and sustainable cities.

Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations' vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California case study we examine how this relates to race, ethnicity, and socioeconomic status -- and how these relationships vary across the region. We find that, all else equal, tracts whose residents drive less are exposed to more air pollution, as are tracts with a less-White population. Commuters from majority-White tracts disproportionately drive through non-White tracts, compared to the inverse. Decades of racially-motivated freeway infrastructure planning and residential segregation shape today's disparities in who produces vehicular air pollution and who is exposed to it, but opportunities exist for urban planning and transport policy to mitigate this injustice., Pre-print

This Series on urban design, transport, and health aimed to facilitate development of a global system of health-related policy and spatial indicators to assess achievements and deficiencies in urban and transport policies and features. This final paper in the Series summarises key findings, considers what to do next, and outlines urgent key actions. Our study of 25 cities in 19 countries found that, despite many well intentioned policies, few cities had measurable standards and policy targets to achieve healthy and sustainable cities. Available standards and targets were often insufficient to promote health and wellbeing, and health-supportive urban design and transport features were often inadequate or inequitably distributed. City planning decisions affect human and planetary health and amplify city vulnerabilities, as the COVID-19 pandemic has highlighted. Hence, we offer an expanded framework of pathways through which city planning affects health, incorporating 11 integrated urban system policies and 11 integrated urban and transport interventions addressing current and emerging issues. Our call to action recommends widespread uptake and further development of our methods and open-source tools to create upstream policy and spatial indicators to benchmark and track progress; unmask spatial inequities; inform interventions and investments; and accelerate transitions to net zero, healthy, and sustainable cities. BG-C was supported by an RMIT Vice-Chancellor's Fellowship. CH was supported through an NHMRC Centre for Research Excellence in Healthy Liveable Communities grant (number 1061404) and The Australian Prevention Partnership Centre (number 9100003). DA was supported by an Impact Acceleration Award from the Economic and Social Research Council and funding from the Global Challenges Research Fund administered by the Department for the Economy, Northern Ireland, UK. EC's research was supported by the Australian Catholic University. SL was supported by the experiential fellowships from College of Social Science and Humanities, Northeastern University. JFS was supported by Australian Catholic University. DS was supported by Washington University in St Louis (MO, USA), Center for Diabetes Translation Research (number P30DK092950 from the US National Institute of Diabetes and Digestive and Kidney Diseases of the US National Institute of Health) and by the Cooperative Agreement Number U48DP006395 from the US Centers for Disease Control and Prevention. SB was supported by an Australian Research Council Linkage Grant (LP160100324) and European Union's Horizon 2020 research and innovation programme under grant agreement #730426 (Urban GreenUP). We would like to thank Belinda Nemec and Judy Boyce. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urb...

Author(s): Boeing, Geoff | Abstract: Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open‐source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study’s reproducible computational workflow, introduces two new open data repositories of ready‐to‐use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open‐source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator data set has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within- and between-city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

Researchers and practitioners across many disciplines have recently adopted computational notebooks to develop, document, and share their scientific workflows - and the GIS community is no exception. This chapter introduces computational notebooks in the geographical context. It begins by explaining the computational paradigm and philosophy that underlie notebooks. Next it unpacks their architecture to illustrate a notebook user's typical workflow. Then it discusses the main benefits notebooks offer GIS researchers and practitioners, including better integration with modern software, more natural access to new forms of data, and better alignment with the principles and benefits of open science. In this context, it identifies notebooks as the glue that binds together a broader ecosystem of open source packages and transferable platforms for computational geography. The chapter concludes with a brief illustration of using notebooks for a set of basic GIS operations. Compared to traditional desktop GIS, notebooks can make spatial analysis more nimble, extensible, and reproducible and have thus evolved into an important component of the geospatial science toolkit.

This morphological study identifies and measures recent nationwide trends in American street network design. Historically, orthogonal street grids provided the interconnectivity and density that researchers identify as important factors for reducing vehicular travel and emissions and increasing road safety and physical activity. During the 20th century, griddedness declined in planning practice alongside declines in urban form compactness, density, and connectivity as urbanization sprawled around automobile dependence. But less is known about comprehensive empirical trends across US neighborhoods, especially in recent years. This study uses public and open data to examine tract-level street networks across the entire US. It develops theoretical and measurement frameworks for a quality of street networks defined here as griddedness. It measures how griddedness, orientation order, straightness, 4-way intersections, and intersection density declined from 1940 through the 1990s while dead-ends and block lengths increased. However, since 2000, these trends have rebounded, shifting back toward historical design patterns. Yet, despite this rebound, when controlling for topography and built environment factors all decades post-1939 are associated with lower griddedness than pre-1940. Higher griddedness is associated with less car ownership—which itself has a well-established relationship with vehicle kilometers traveled and greenhouse gas emissions—while controlling for density, home and household size, income, jobs proximity, street network grain, and local topography. Interconnected grid-like street networks offer practitioners an important tool for curbing car dependence and emissions. Once established, street patterns determine urban spatial structure for centuries, so proactive planning is essential.

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

Author(s): Boeing, Geoff | Abstract: This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this, then reviews the literature of multidisciplinary empirical spatial network science recently conducted using it to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability.

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.

As the rental housing market moves online, the internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings’ representativeness could impact different communities’ access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners’ and researchers’ ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the USA and finds that they spatially concentrate and overrepresent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities’ online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real time, it does not evenly represent all market segments. The internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies, human interactions, and planners’ ability to monitor and respond to urban challenges.

Circuity, the ratio of network distances to straight-line distances, is an important measure of urban street network structure and transportation efficiency. Circuity results from a circulation network’s configuration, planning, and underlying terrain. In turn, it impacts how humans use urban space for settlement and travel. Although past research has examined overall street network circuity, researchers have not studied the relative circuity of walkable versus drivable circulation networks. This study uses OpenStreetMap data to explore relative network circuity. We download walkable and drivable networks for 40 US cities using the OSMnx software, which we then use to simulate four million routes and analyze circuity to characterize network structure. We find that walking networks tend to allow for more direct routes than driving networks do in most cities: average driving circuity exceeds average walking circuity in all but four of the cities that exhibit statistically significant differences between network types. We discuss various reasons for this phenomenon, illustrated with case studies. Network circuity also varies substantially between different types of places. These findings underscore the value of using network-based distances and times rather than straight-line when studying urban travel and access. They also suggest the importance of differentiating between walkable and drivable circulation networks when modeling and characterizing urban street networks: although different modes’ networks overlap in any given city, their relative structure and performance vary in most cities.

Current sources of data on rental housing - such as the census or commercial databases that focus on large apartment complexes - do not reflect recent market activity or the full scope of the U.S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U.S. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data., Comment: 20 pages, 9 figures, Journal of Planning Education and Research. 2016. Online first

Traditional U.S. rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market—what existing renters pay each month. They do not explicitly tell us about the spot market—that is, the asking rents that current homeseekers must pay to acquire housing—though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets.

Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide and then examines the variation of nonplanarity across a single city. It develops two new indicators—the Spatial Planarity Ratio and the Edge Length Ratio—to measure planarity and describe infrastructure and urbanization. While some street networks are approximately planar, we empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city’s spatial logic and order. Measures of entropy reveal a city’s streets’ order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city’s typical street segment length, average circuity, average node degree, and the network’s proportions of four-way intersections and dead-ends. It also develops a new indicator of orientation-order that quantifies how a city’s street network follows the geometric ordering logic of a single grid. A cluster analysis is performed to explore similarities and differences among these study sites in multiple dimensions. Significant statistical relationships exist between city orientation-order and other indicators of spatial order, including street circuity and measures of connectedness. On average, US/Canadian study sites are far more grid-like than those elsewhere, exhibiting less entropy and circuity. These indicators, taken in concert, help reveal the extent and nuance of the grid. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating complex urban transportation system patterns and configurations around the world.

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.

The city of Honolulu, Hawaii is currently planning and developing a new rail transit system. While Honolulu has supportive density and topography for rail transit, questions remain about its ability to effectively integrate urban design and accessibility across the system. Every transit trip begins and ends with a walking trip from origins and to destinations: transportation planning must account for pedestrian safety, comfort, and access. Ildefons Cerd ’s 19th century utopian plan for Barcelona’s Eixample district produced a renowned, livable urban form. The Eixample, with its well-integrated rail transit, serves as a model of urban design, land use, transportation planning, and pedestrian-scaled streets working in synergy to produce accessibility. This study discusses the urban form of Honolulu and the history and planning of its new rail transit system. Then it reviews the history of Cerd ’s plan for the Eixample and discusses its urban form and performance today. Finally it draws several lessons from Barcelona’s urban design, accessibility, and rail transit planning and critically discusses their applicability to policy and design in Honolulu. This discussion is situated within wider debates around livable cities and social justice as it contributes several form and design lessons to the livability and accessibility literature while identifying potential concerns with privatization and displacement.

Urban displacement – when a household is forced to relocate due to conditions affecting its home or surroundings – is frequently caused by rising housing costs, particularly in wealthy, prosperous cities. However, its dynamics are complex and often difficult to understand. This paper presents an agent-based model (ABM) of urban settlement, agglomeration, displacement, and sprawl. New human settlements form around a natural amenity that draws initial, poor settlers to subsist on the resource. As the settlement grows, subsequent settlers of varying income, skills, and interests are heterogeneously drawn to either 1) the natural amenity or 2) the emerging human agglomeration. As the agglomeration grows and densifies, land values increase, and the initial poor settlers may be displaced away from the natural amenity on which they relied. Through path dependence, high-income residents remain clustered around this natural amenity for which they have no direct use or interest. The agent-based model presented here explores the dynamics of this process. In particular, it reveals how urban displacement and gentrification can be sensitive to income inequality, density, and varied preferences for different types of spatial amenities.

Complex systems have been widely studied by social and natural scientists in terms of their dynamics and their structure. Scholars of cities and urban planning have incorporated complexity theories from qualitative and quantitative perspectives. From a structural standpoint, the urban form may be characterized by the morphological complexity of its circulation networks - particularly their density, resilience, centrality, and connectedness. This dissertation unpacks theories of nonlinearity and complex systems, then develops a framework for assessing the complexity of urban form and street networks. It introduces a new tool, OSMnx, to collect street network and other urban form data for anywhere in the world, then analyze and visualize them. Finally, it presents a large empirical study of 27,000 street networks, examining their metric and topological complexity relevant to urban design, transportation research, and the human experience of the built environment., PhD thesis (2017), City and Regional Planning, UC Berkeley

Complex systems have become a popular lens for analyzing cities and complexity theory has many implications for urban performance and resilience. This paper develops a typology of measures and indicators for assessing the physical complexity of the built environment at the scale of urban design. It extends quantitative measures from city planning, network science, ecosystems studies, fractal geometry, statistical physics, and information theory to the analysis of urban form and qualitative human experience. Metrics at multiple scales are scattered throughout diverse bodies of literature and have useful applications in analyzing the adaptive complexity that both evolves and results from local design processes. In turn, they enable urban designers to assess resilience, adaptability, connectedness, and livability with an advanced toolkit. The typology developed here applies to empirical research of various neighborhood types and design standards. It includes temporal, visual, spatial, scaling, and connectivity measures of the urban form. Today, prominent urban design movements openly embrace complexity but must move beyond inspiration and metaphor to formalize what "complexity" is and how we can use it to assess both the world as-is as well as proposals for how it could be instead.

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. 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 nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

Author(s): Boeing, Geoff | Abstract: A review of The New Science of Cities by Michael Batty

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This paper has three aims. First it discusses several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second it introduces to an interdisciplinary audience the foundations of nonlinear dynamics, chaos, fractals, self-similarity, and the limits of prediction – arguing that information visualization is a key way to engage with these concepts. Finally it presents Pynamical, a tool to visualize and explore nonlinear dynamical systems' behavior.

An urban physics paradigm has emerged in recent years to argue that applying the scientific tools of physics to urban data and processes can yield important new insights into cities. New sources of...

This study expands on one previously published in the Berkeley Planning Journal examining LEED-ND’s criteria for Neighborhood Pattern and Design. LEED-ND was developed as a system for rating new neighborhoods on the sustainability of their planning. It has increasingly been adopted by cities as a de facto measure of livable neighborhood design and used to accelerate development processes. We hypothesize that these Neighborhood Pattern and Design standards do not accurately capture livability as defined by residents, nor can a single set of prescriptive design guidelines reflect the diverse values and desired amenities of different communities. Our study areas are Temescal, a historic neighborhood in Oakland, CA and the Promenade, a New Urbanist neighborhood in the suburban town of Hercules, CA. Neither Temescal nor the Promenade could achieve LEED-ND certification due to technical disqualifications but residents of both neighborhoods rated their livability very highly. Furthermore, residents consistently rated and ranked livability characteristics differently than did LEED-ND, calling into question its validity as a universally codifiable rating system.