118 research outputs found
Urban Street Network Analysis in a Computational Notebook
Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future
A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood
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
Spatial information and the legibility of urban form: Big data in urban morphology
Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, propose 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
Urban Street Network Analysis in a Computational Notebook
Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively run code and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks can empower guides for introducing methods to new users and can help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future
Planarity and Street Network Representation in Urban Form Analysis
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, 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
Honolulu Rail Transit: International Lessons from Barcelona in Linking Urban Form, Design, and Transportation
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 Cerda'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 Cerda'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
The Effects of Inequality, Density, and Heterogeneous Residential Preferences on Urban Displacement and Metropolitan Structure: An Agent-Based Model
Urban displacement - when a household is forced to relocate due to conditions
affecting its home or surroundings - often results from 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 of
urban settlement, agglomeration, displacement, and sprawl. New settlements form
around a spatial 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 the original amenity
or to the emerging human agglomeration. As this agglomeration grows and
densifies, land values increase, and the initial poor settlers are displaced
from the spatial amenity on which they relied. Through path dependence,
high-income residents remain clustered around this original amenity for which
they have no direct use or interest. This toy model explores these dynamics,
demonstrating a simplified mechanism of how urban displacement and
gentrification can be sensitive to income inequality, density, and varied
preferences for different types of amenities
The Right Tools for the Job: The Case for Spatial Science Tool-Building
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. Next it reviews this tool's use in the recent multidisciplinary spatial
network science literature 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 Spatial Order: Street Network Orientation, Configuration, and Entropy
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
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