Identifying Urban Morphology from Street Networks with Graphlet Analysis

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.