Identifying Urban Morphology from Street Networks with Graphlet Analysis

Gabriel Agostini, J.E. Goncalves, T. Verma

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Urban street networks contain repetitive structures that reflect human needs as cities expand and evolve. To identify and understand these building blocks of cities, we propose the use of graphlet-based methods-that is, focusing on small, connected subgraphs of these networks. Looking at graphlets of up to 4 nodes in the street networks of New York City, we identify local structures such as gridded patches through spatial auto-correlation statistics. This methodology can be quickly applied to any city in the world, helping researchers classify local street structures and identify common urban development trends across many cities.
Original languageEnglish
Title of host publicationAnnual Geographical Information Science Research UK (GISRUK)
PublisherZenodo
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 2021
Event30th Annual Geographical Information Science Research UK (GISRUK) 2022 - Liverpool, United Kingdom
Duration: 5 Apr 20228 Apr 2022

Conference

Conference30th Annual Geographical Information Science Research UK (GISRUK) 2022
Country/TerritoryUnited Kingdom
CityLiverpool
Period5/04/228/04/22

Keywords

  • Spatial Planning
  • Street Networks
  • Urban Morphology
  • Graphlet Analysis

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