OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks (1611.01890v5)

Abstract: Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

• The paper introduces OSMnx as a novel open-source tool that automates acquiring and analyzing street networks from OpenStreetMap data.
• It details customizable methods for constructing networks with accurate topological corrections and flexible multi-format data exports.
• The study demonstrates the tool's practical impact on urban research by reliably quantifying connectivity and resilience in diverse cityscapes.

OSMnx: A Tool for Comprehensive Street Network Analysis

The paper "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," authored by Geoff Boeing, introduces OSMnx, an open-source Python-based software package. This tool was developed to mitigate the constraints of current urban planning and street network analysis in terms of data availability, consistency, and reproducibility. The design and implementation of OSMnx address specific empirical shortfalls in urban network research, providing functionalities that enhance the accuracy and comprehensiveness of street network datasets.

Key Contributions and Functionalities

OSMnx is notable for its multifaceted capabilities, which include:

1. Automated Data Acquisition: The tool can automatically download political boundaries, building footprints, and street networks from OpenStreetMap (OSM). This implies a substantial reduction in the manual effort required to collect and prepare data for analysis.
2. Customizable Network Construction: OSMnx allows users to tailor the retrieval of street network data by various parameters such as bounding boxes, points with a specified distance radius, and polygons. This versatility ensures that the acquired network datasets are aligned with the specific research requirements.
3. Topological Correction: The software includes mechanisms to correct network topology, consolidating street segments to maintain accurate graph representation. This is particularly crucial when dealing with non-planar networks like those found in modern cities with overpasses and tunnels.
4. Flexible Data Export: OSMnx enables the saving of constructed networks in various formats, including shapefiles, GraphML, and SVG. These formats are compatible with numerous GIS and network analysis platforms, facilitating broader applicability in research and practice.
5. Advanced Analytical Tools: The tool supports complex analyses such as computing shortest paths, projecting and visualizing networks, and calculating a comprehensive set of metrics and topological properties. These include traditional urban design measures as well as advanced graph-theoretic metrics like betweenness centrality, clustering coefficients, and average node connectivity.

Case Study and Numerical Results

The paper provides an illustrative case paper focused on the street networks of three neighborhoods in Portland, Oregon: Downtown, Laurelhurst, and Northwest Heights. The analysis quantifies differences in network density, connectedness, and resilience. For example, Downtown Portland exhibits a high intersection density (164 intersections/km²) and node degree centrality, showcasing its fine-grained and interconnected street network. In contrast, Northwest Heights, with 28 intersections/km² and a high average street segment length of 117 m, represents a sparser and less connected network topology.

Practical and Theoretical Implications

From a practical perspective, OSMnx offers significant improvements in the usability, scalability, and reproducibility of street network analysis. The tool’s ability to automate the acquisition and construction of street networks facilitates large-scale analyses that were previously cumbersome due to data handling complexities. The incorporation of accurate non-planar network representations ensures that the resulting analyses reflect real-world conditions more faithfully than previous methods, which often relied on planar simplifications.

In theoretical terms, OSMnx underpins a more detailed exploration of urban form and functioning. By enabling the computation of robust graph-theoretic measures, researchers can gain deeper insights into the structural properties and resilience of urban networks. This, in turn, can inform more effective urban planning and policy-making, particularly in the context of enhancing connectivity and mitigating vulnerabilities in urban infrastructure.

Future Directions

The future of OSMnx and its associated research can be visualized in several trajectories:

1. Enhanced Data Integration: As OpenStreetMap continues to evolve with richer attribute data, OSMnx can integrate additional layers of information, such as street widths, pedestrian pathways, and real-time traffic data, further enriching the analysis.
2. Scalability and Performance: Developing optimizations to handle even larger datasets efficiently will allow for more extensive comparative studies across multiple urban regions or countries.
3. Customization and Extensions: Future updates to OSMnx could introduce more customizable analysis options, allowing for tailored metrics specific to different paper goals, whether focused on vehicular traffic, pedestrian movement, or urban resilience to natural disruptions.

In conclusion, OSMnx represents a substantial advancement in the field of urban network analysis, providing a robust, scalable, and reproducible framework for the acquisition, construction, and analysis of complex street networks. Its integration of graph theory and spatial analysis offers researchers and practitioners a powerful tool to explore and understand the intricacies of urban form and its implications on modern urban living.