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

TL;DR
This paper explores how big data and computational tools like OSMnx enhance traditional urban morphology analysis by revealing patterns in street networks and urban form worldwide.
Contribution
It introduces a framework integrating visual culture theories with computational data science to analyze urban fabric patterns using OpenStreetMap data.
Findings
Street network patterns vary significantly across different cities.
Ubiquitous urban data enables comprehensive global urban form analysis.
Computational workflows reveal new insights into spatial order and urban evolution.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
