Planarity and street network representation in urban form analysis
Bibliographic Data
| ID | 21247600 |
|---|---|
| Authors | Geoff Boeing (0000-0003-1851-6411, Northeastern University, corresponding author) |
| Year | 2020 |
| Volume | 47 |
| Issue | 5 |
| Pages | 855-869 |
| Publication date | 2020-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/2399808318802941 |
| OpenAlex | W2805698423 |
| Language | EN |
| Citations received | 19 |
| References cited | 49 |
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
Cartography · Civil engineering · Combinatorics · Computer network · Economic geography · Geography · Planar · Planarity testing · Street network · Transport engineering · Urban Morphology · Urban planning · Urbanization · Computer Science · Engineering · Land Use and Ecosystem Services · Mathematics · Urban Design and Spatial Analysis · Urban Transport and Accessibility
Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood
AwaP-IC—An Open-Source GIS Tool for Measuring Walkable Access
Decoding urban form through street networks
Resilient urban forms
Urban spatial order
Urban spatial attributes and sustainability
Machine learning-based characterisation of urban morphology with the street pattern
Road network structure and ride-sharing accessibility
Quantifying the Spatial Ratio of Streets in Beijing Based on Street-View Images
Achieving ‘Active’ 30 Minute Cities
Mapping Street Patterns with Network Science and Supervised Machine Learning
Modeling and Analyzing Urban Networks and Amenities With OSMnx
A multi-scale analysis of 27,000 urban street networks
Segregated by design? Street network topological structure and the measurement of urban segregation
Urban planning and design with points of interest and visual perception
Making plans findable, accessible, interoperable, and reusable with data infrastructure
Ethically Informed Urban Planning
Planar Graphs
Urban volumetrics
Networks
Spatial networks
How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets
Centrality measures in spatial networks of urban streets
OSMnx
Detecting the dynamics of urban structure through spatial network analysis
Elementary processes governing the evolution of road networks
From paths to blocks
Defining Street-based Local Area and measuring its effect on house price using a hedonic price approach
Street Centrality and the Location of Economic Activities in Barcelona
Alterations in scale
Identifying house price effects of changes in urban street configuration
Revealing centrality in the spatial structure of cities from human activity patterns
The impact of street network connectivity on pedestrian volume
Identifying the Impact on Land Prices of Replacing At-grade or Elevated Railways with Underground Subways in the Seoul Metropolitan Area
Switzerland as a Single Metropolitan Area? A Study of its Commuting Network
Street network structure and household activity spaces
| Unique citing works | 19 |
|---|---|
| Citations per year | 2.71 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 16 |