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Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

Geoff Boeing

arXiv:1910.00118v1physics.soc-ph

TL;DR

Urban morphology needs ways to examine how street networks encode spatial logics and to make complex urban patterns legible. This paper uses OSM data, OSMnx, and visual analysis to compare street form and orientation across places. The visualizations reveal differences in urban texture and spatial ordering, but a single square-mile view does not represent a whole municipality.

  • Problem

    The paper asks how computational methods can extend traditional manual workflows to examine street-network spatial logics and make complex patterns legible to planners and citizens.

    Source: §2 · p. 4

  • Method

    The paper models street networks from OSM data and uses same-scale figure-ground diagrams and rose diagrams to examine urban form and street orientations.

    Source: §3 · p. 5

  • Results

    The visualizations reveal differences in cities’ texture, grain, connectivity, and spatial-ordering logic.

    Source: §5 · p. 12

  • Takeaways & Limitations

    These visual methods can help planners interpret street-network patterns and communicate them to citizens in collaborative planning.

    Source: §6 · p. 13

    A single square-mile diagram cannot represent broader spatial scales or other locations within the same municipality.

    Source: §4 · p. 8

Abstract

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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.

1. Introduction

The introduction frames urban morphology as an information-management problem and asks how user-contributed data and visualization can support historically and culturally informed interpretation of urban form.

1. Introduction

  • Urban research information management spans acquiring and transforming data, structuring and interpreting it, and disseminating knowledge for planning and advocacy.The paper considers urban morphology through these processes in the Smart Cities paradigm.Source: §1 · p. 1
  • The Smart Cities paradigm can support community advocacy and public decision-making, while critical analysis can identify sampling biases in user-generated data and foreground marginalized voices.Interpretive and narrative approaches can contextualize data-driven urban morphology.Source: §1 · p. 2
  • OpenStreetMap provides freely available street-network and urban-infrastructure data used in research on travel, city organization, and planning and design histories.Street networks are a key spatial infrastructure shaping flows of people, goods, and information through urban space.Source: §1 · p. 2
  • The paper asks how user-contributed big data can be collected, organized, and modeled to explore spatial logics resulting from urban planning, design, and evolution.It calls for workflows that integrate data-driven and qualitative, interpretive or narrative, methods.Source: §1 · p. 2
  • The paper situates computational workflows within a visual culture of planning and argues that historically and culturally informed quantitative methods are essential to understanding urban patterns and forms.It describes collecting, modeling, analyzing, and visualizing OSM data with open-source tools such as OSMnx.Source: §1 · pp. 2–3

2. Spatial Information and Urban Form

This section connects urban morphology to Smart Cities and visual analysis, emphasizing that spatial patterns require contextual interpretation and posing questions for computational study of street networks.

2. Spatial Information and Urban Form

  • Smart Cities research uses information technology to quantify urban patterns and processes, while related morphology research studies circulation networks and varied spatial ordering principles.The section notes that cities without one formal geometric logic may still have well-defined physical and social structure.Source: §2 · p. 3
  • Data-driven urban morphology models spatial data to trace urban histories, configurations, and orientations, but requires narrative and interpretive approaches to reveal local context and history.The section argues that spatial data must be contextually interpreted to become meaningful information.Source: §2 · p. 4
  • Visual representations render abstract spatial information legible and mediate empirical research traditions and the creative process of urban design.The section situates this visual thinking in cartography, geography, architecture, and planning, including Nolli maps and Jacobs’s comparative street-network diagrams.Source: §2 · p. 4
  • The paper frames three questions: how street networks embody spatial logics, how orientations and configurations vary across places, and how computational visualizations can make patterns legible.These questions extend traditional manual workflows through automated methods and spatial big data.Source: §2 · p. 4

3. Working with OpenStreetMap Data

This section presents OSMnx-based workflows for turning OpenStreetMap data into street-network models and visualizations that support comparative analysis of urban form and orientation.

3. Working with OpenStreetMap Data

  • OSMnx streamlines downloading OSM data for a study site and constructing it into street-network graphs or spatial data frames for visualization and statistical analysis.The paper notes that raw OSM data and custom query languages can be challenging to use directly for nuanced analysis.Source: §3 · pp. 4–5
  • The street-network models are nonplanar directed multigraphs, with intersections and dead-ends as nodes and street segments as edges.OSMnx constructs models from OSM data for study-site boundaries and supports analysis and visualization.Source: §3 · p. 5
  • Figure-ground diagrams depict street networks and building footprints at the same scale, enabling visual comparison of urban forms across one-square-mile areas.The method adapts Jacobs’s hand-drawn figure-ground approach to a computational workflow.Source: §3 · p. 5
  • Rose diagrams encode street orientations in 10° compass bins, with bar lengths showing the relative frequency of segments in each bin.They visualize whether a network follows a low-entropy grid or has streets oriented more evenly across compass directions.Source: §3 · p. 5

4. Urban Circulation Systems and Spatial Logics

At a consistent one-square-mile scale, street-network and figure-ground diagrams make urban forms comparable and reveal different planning and urbanization logics. Orientation diagrams show how street patterns vary across boroughs and cities, while place-specific examples connect those patterns to local histories.

4. Urban Circulation Systems and Spatial Logics

  • At one square mile, Portland’s compact grid, Irvine’s coarse-grained form, and Rome’s fine-grained organic network show contrasting urban patterns.The common scale makes these qualitative differences easy to compare.Source: Figure 1 · p. 6, §4 · p. 6
  • Place-specific networks retain historical traces: Broadway follows an older Wickquasgeck Trail, while Haussmann’s Paris project is visible in radiating streets and block patterns.Broadway’s path dependence is described in Holloway (2013) and Shorto (2004); Paris’s spatial signatures are linked to Hall (1996).Source: §4 · p. 7
  • A single square-mile diagram cannot represent broader scales or other locations within a municipality; it compares urbanization patterns at a common scale instead.Street patterns can vary substantially within the same city.Source: §4 · p. 8
  • Figure 2 contrasts orderly, centralized US grids with finer-grained informal settlements; Port-au-Prince’s square mile contains 14,037 building footprints with median area 34 square meters.The diagrams pair street networks with building footprints.Source: Figure 2 · p. 8, §4 · p. 9
  • Manhattan’s orthogonal grid concentrates street bearings in four bins offset from true North, while the other boroughs show higher-entropy orientations.The rose diagrams use 10-degree compass bins, with bar lengths showing relative frequency.Source: §4 · p. 9, Figure 4 · p. 10
  • In preindustrial centers, buildings dominate open space, whereas Brasília’s modernist landscape places scattered buildings across a void-dominant ground, suggesting an ordering of social relations.The figure-ground contrast is presented as a way to interpret urban form and circulation networks.Source: §4 · p. 9, Figure 3 · p. 10
  • Across 25 municipalities, Toronto and Beijing show the clearest grid signatures, while Rome and Rio de Janeiro have more organic, less-orthogonal patterns.The citywide scale aggregates heterogeneous neighborhoods but captures the scale of municipal planning jurisdictions.Source: Figure 5 · p. 11, §4 · pp. 11–12

5. Discussion

The paper uses OSM data and computational visualizations to make urban form legible across places. Figure-ground and rose diagrams expose different aspects of urban texture and spatial ordering, with potential uses in planning communication and public engagement.

5. Discussion

  • OSM enables figure-ground and rose-diagram workflows to be performed automatically and computationally, extending the analytical cartographic approach of earlier urban morphology work.Jacobs adapted Nolli maps to manually illustrate figure-ground diagrams.Source: §5 · p. 12
  • Figure-ground and rose diagrams reveal urban texture, grain, and spatial ordering, while rose diagrams make street-network orientation entropy legible as a plot.Figure-ground diagrams also support same-scale comparison of texture, grain, and connectivity across places.Source: §5 · p. 12
  • These visual methods can help planners explain density, connectivity, and urban texture to lay audiences and support public engagement in citizen science.The paper identifies quantifying connectivity and griddedness and tracing street-network change across the United States as future research directions.Source: §5 · p. 12

6. Conclusion

The conclusion presents OSMnx workflows as a way to combine data-driven and interpretive urban morphology, making street-network patterns available for analysis and communication. It also identifies richer interactive visualization and measures of orientation-order and griddedness as future research directions.

6. Conclusion

  • The paper advances an OSMnx workflow for acquiring, modeling, interpreting, and disseminating user-contributed big data in urban morphology.It takes up Crooks et al.’s (2016) call to meld data-driven and interpretive approaches.Source: §6 · p. 12
  • The workflow turns Jacobs’s (1995) figure-ground diagrams and rose diagrams into reproducible computational methods for exploring spatial ordering in OSM street-network data.The paper situates this approach within planning’s visual culture and uses a visually mediated narrative.Source: §6 · p. 13
  • The paper contributes a workflow for studying street-network spatial logics, an approach to modeling orientation and configuration across places, and visual communication for collaborative scenario comparison.The visual communication contribution extends quantitative Smart Cities and information-management work in a qualitative direction.Source: §6 · p. 13
  • Richer interactive visual functionality and further development of rose-diagram measures for orientation-order and griddedness remain future research needs.The paper notes that some interaction was discussed, while calling interactive functionality an emerging frontier and proposing study of change over time across cities and countries.Source: §6 · p. 13
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